diff --git a/opencode.jsonc b/opencode.jsonc
index 2a1b203..ab67fcc 100644
--- a/opencode.jsonc
+++ b/opencode.jsonc
@@ -3,6 +3,7 @@
   "update": "disable",
   "model": "github-copilot/gpt-6-astra",
   "default_agent": "build",
+  "shell": "/usr/bin/zsh",
   "worktree": {
     "directory": "/data/workspace/.worktrees"
   },
diff --git a/skills/algorithmic-art/LICENSE.txt b/skills/algorithmic-art/LICENSE.txt
new file mode 100644
index 0000000..4f881c5
--- /dev/null
+++ b/skills/algorithmic-art/LICENSE.txt
@@ -0,0 +1,202 @@
+
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diff --git a/skills/algorithmic-art/SKILL.md b/skills/algorithmic-art/SKILL.md
new file mode 100644
index 0000000..634f6fa
--- /dev/null
+++ b/skills/algorithmic-art/SKILL.md
@@ -0,0 +1,405 @@
+---
+name: algorithmic-art
+description: Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.
+license: Complete terms in LICENSE.txt
+---
+
+Algorithmic philosophies are computational aesthetic movements that are then expressed through code. Output .md files (philosophy), .html files (interactive viewer), and .js files (generative algorithms).
+
+This happens in two steps:
+1. Algorithmic Philosophy Creation (.md file)
+2. Express by creating p5.js generative art (.html + .js files)
+
+First, undertake this task:
+
+## ALGORITHMIC PHILOSOPHY CREATION
+
+To begin, create an ALGORITHMIC PHILOSOPHY (not static images or templates) that will be interpreted through:
+- Computational processes, emergent behavior, mathematical beauty
+- Seeded randomness, noise fields, organic systems
+- Particles, flows, fields, forces
+- Parametric variation and controlled chaos
+
+### THE CRITICAL UNDERSTANDING
+- What is received: Some subtle input or instructions by the user to take into account, but use as a foundation; it should not constrain creative freedom.
+- What is created: An algorithmic philosophy/generative aesthetic movement.
+- What happens next: The same version receives the philosophy and EXPRESSES IT IN CODE - creating p5.js sketches that are 90% algorithmic generation, 10% essential parameters.
+
+Consider this approach:
+- Write a manifesto for a generative art movement
+- The next phase involves writing the algorithm that brings it to life
+
+The philosophy must emphasize: Algorithmic expression. Emergent behavior. Computational beauty. Seeded variation.
+
+### HOW TO GENERATE AN ALGORITHMIC PHILOSOPHY
+
+**Name the movement** (1-2 words): "Organic Turbulence" / "Quantum Harmonics" / "Emergent Stillness"
+
+**Articulate the philosophy** (4-6 paragraphs - concise but complete):
+
+To capture the ALGORITHMIC essence, express how this philosophy manifests through:
+- Computational processes and mathematical relationships?
+- Noise functions and randomness patterns?
+- Particle behaviors and field dynamics?
+- Temporal evolution and system states?
+- Parametric variation and emergent complexity?
+
+**CRITICAL GUIDELINES:**
+- **Avoid redundancy**: Each algorithmic aspect should be mentioned once. Avoid repeating concepts about noise theory, particle dynamics, or mathematical principles unless adding new depth.
+- **Emphasize craftsmanship REPEATEDLY**: The philosophy MUST stress multiple times that the final algorithm should appear as though it took countless hours to develop, was refined with care, and comes from someone at the absolute top of their field. This framing is essential - repeat phrases like "meticulously crafted algorithm," "the product of deep computational expertise," "painstaking optimization," "master-level implementation."
+- **Leave creative space**: Be specific about the algorithmic direction, but concise enough that the next Claude has room to make interpretive implementation choices at an extremely high level of craftsmanship.
+
+The philosophy must guide the next version to express ideas ALGORITHMICALLY, not through static images. Beauty lives in the process, not the final frame.
+
+### PHILOSOPHY EXAMPLES
+
+**"Organic Turbulence"**
+Philosophy: Chaos constrained by natural law, order emerging from disorder.
+Algorithmic expression: Flow fields driven by layered Perlin noise. Thousands of particles following vector forces, their trails accumulating into organic density maps. Multiple noise octaves create turbulent regions and calm zones. Color emerges from velocity and density - fast particles burn bright, slow ones fade to shadow. The algorithm runs until equilibrium - a meticulously tuned balance where every parameter was refined through countless iterations by a master of computational aesthetics.
+
+**"Quantum Harmonics"**
+Philosophy: Discrete entities exhibiting wave-like interference patterns.
+Algorithmic expression: Particles initialized on a grid, each carrying a phase value that evolves through sine waves. When particles are near, their phases interfere - constructive interference creates bright nodes, destructive creates voids. Simple harmonic motion generates complex emergent mandalas. The result of painstaking frequency calibration where every ratio was carefully chosen to produce resonant beauty.
+
+**"Recursive Whispers"**
+Philosophy: Self-similarity across scales, infinite depth in finite space.
+Algorithmic expression: Branching structures that subdivide recursively. Each branch slightly randomized but constrained by golden ratios. L-systems or recursive subdivision generate tree-like forms that feel both mathematical and organic. Subtle noise perturbations break perfect symmetry. Line weights diminish with each recursion level. Every branching angle the product of deep mathematical exploration.
+
+**"Field Dynamics"**
+Philosophy: Invisible forces made visible through their effects on matter.
+Algorithmic expression: Vector fields constructed from mathematical functions or noise. Particles born at edges, flowing along field lines, dying when they reach equilibrium or boundaries. Multiple fields can attract, repel, or rotate particles. The visualization shows only the traces - ghost-like evidence of invisible forces. A computational dance meticulously choreographed through force balance.
+
+**"Stochastic Crystallization"**
+Philosophy: Random processes crystallizing into ordered structures.
+Algorithmic expression: Randomized circle packing or Voronoi tessellation. Start with random points, let them evolve through relaxation algorithms. Cells push apart until equilibrium. Color based on cell size, neighbor count, or distance from center. The organic tiling that emerges feels both random and inevitable. Every seed produces unique crystalline beauty - the mark of a master-level generative algorithm.
+
+*These are condensed examples. The actual algorithmic philosophy should be 4-6 substantial paragraphs.*
+
+### ESSENTIAL PRINCIPLES
+- **ALGORITHMIC PHILOSOPHY**: Creating a computational worldview to be expressed through code
+- **PROCESS OVER PRODUCT**: Always emphasize that beauty emerges from the algorithm's execution - each run is unique
+- **PARAMETRIC EXPRESSION**: Ideas communicate through mathematical relationships, forces, behaviors - not static composition
+- **ARTISTIC FREEDOM**: The next Claude interprets the philosophy algorithmically - provide creative implementation room
+- **PURE GENERATIVE ART**: This is about making LIVING ALGORITHMS, not static images with randomness
+- **EXPERT CRAFTSMANSHIP**: Repeatedly emphasize the final algorithm must feel meticulously crafted, refined through countless iterations, the product of deep expertise by someone at the absolute top of their field in computational aesthetics
+
+**The algorithmic philosophy should be 4-6 paragraphs long.** Fill it with poetic computational philosophy that brings together the intended vision. Avoid repeating the same points. Output this algorithmic philosophy as a .md file.
+
+---
+
+## DEDUCING THE CONCEPTUAL SEED
+
+**CRITICAL STEP**: Before implementing the algorithm, identify the subtle conceptual thread from the original request.
+
+**THE ESSENTIAL PRINCIPLE**:
+The concept is a **subtle, niche reference embedded within the algorithm itself** - not always literal, always sophisticated. Someone familiar with the subject should feel it intuitively, while others simply experience a masterful generative composition. The algorithmic philosophy provides the computational language. The deduced concept provides the soul - the quiet conceptual DNA woven invisibly into parameters, behaviors, and emergence patterns.
+
+This is **VERY IMPORTANT**: The reference must be so refined that it enhances the work's depth without announcing itself. Think like a jazz musician quoting another song through algorithmic harmony - only those who know will catch it, but everyone appreciates the generative beauty.
+
+---
+
+## P5.JS IMPLEMENTATION
+
+With the philosophy AND conceptual framework established, express it through code. Pause to gather thoughts before proceeding. Use only the algorithmic philosophy created and the instructions below.
+
+### ⚠️ STEP 0: READ THE TEMPLATE FIRST ⚠️
+
+**CRITICAL: BEFORE writing any HTML:**
+
+1. **Read** `templates/viewer.html` using the Read tool
+2. **Study** the exact structure, styling, and Anthropic branding
+3. **Use that file as the LITERAL STARTING POINT** - not just inspiration
+4. **Keep all FIXED sections exactly as shown** (header, sidebar structure, Anthropic colors/fonts, seed controls, action buttons)
+5. **Replace only the VARIABLE sections** marked in the file's comments (algorithm, parameters, UI controls for parameters)
+
+**Avoid:**
+- ❌ Creating HTML from scratch
+- ❌ Inventing custom styling or color schemes
+- ❌ Using system fonts or dark themes
+- ❌ Changing the sidebar structure
+
+**Follow these practices:**
+- ✅ Copy the template's exact HTML structure
+- ✅ Keep Anthropic branding (Poppins/Lora fonts, light colors, gradient backdrop)
+- ✅ Maintain the sidebar layout (Seed → Parameters → Colors? → Actions)
+- ✅ Replace only the p5.js algorithm and parameter controls
+
+The template is the foundation. Build on it, don't rebuild it.
+
+---
+
+To create gallery-quality computational art that lives and breathes, use the algorithmic philosophy as the foundation.
+
+### TECHNICAL REQUIREMENTS
+
+**Seeded Randomness (Art Blocks Pattern)**:
+```javascript
+// ALWAYS use a seed for reproducibility
+let seed = 12345; // or hash from user input
+randomSeed(seed);
+noiseSeed(seed);
+```
+
+**Parameter Structure - FOLLOW THE PHILOSOPHY**:
+
+To establish parameters that emerge naturally from the algorithmic philosophy, consider: "What qualities of this system can be adjusted?"
+
+```javascript
+let params = {
+  seed: 12345,  // Always include seed for reproducibility
+  // colors
+  // Add parameters that control YOUR algorithm:
+  // - Quantities (how many?)
+  // - Scales (how big? how fast?)
+  // - Probabilities (how likely?)
+  // - Ratios (what proportions?)
+  // - Angles (what direction?)
+  // - Thresholds (when does behavior change?)
+};
+```
+
+**To design effective parameters, focus on the properties the system needs to be tunable rather than thinking in terms of "pattern types".**
+
+**Core Algorithm - EXPRESS THE PHILOSOPHY**:
+
+**CRITICAL**: The algorithmic philosophy should dictate what to build.
+
+To express the philosophy through code, avoid thinking "which pattern should I use?" and instead think "how to express this philosophy through code?"
+
+If the philosophy is about **organic emergence**, consider using:
+- Elements that accumulate or grow over time
+- Random processes constrained by natural rules
+- Feedback loops and interactions
+
+If the philosophy is about **mathematical beauty**, consider using:
+- Geometric relationships and ratios
+- Trigonometric functions and harmonics
+- Precise calculations creating unexpected patterns
+
+If the philosophy is about **controlled chaos**, consider using:
+- Random variation within strict boundaries
+- Bifurcation and phase transitions
+- Order emerging from disorder
+
+**The algorithm flows from the philosophy, not from a menu of options.**
+
+To guide the implementation, let the conceptual essence inform creative and original choices. Build something that expresses the vision for this particular request.
+
+**Canvas Setup**: Standard p5.js structure:
+```javascript
+function setup() {
+  createCanvas(1200, 1200);
+  // Initialize your system
+}
+
+function draw() {
+  // Your generative algorithm
+  // Can be static (noLoop) or animated
+}
+```
+
+### CRAFTSMANSHIP REQUIREMENTS
+
+**CRITICAL**: To achieve mastery, create algorithms that feel like they emerged through countless iterations by a master generative artist. Tune every parameter carefully. Ensure every pattern emerges with purpose. This is NOT random noise - this is CONTROLLED CHAOS refined through deep expertise.
+
+- **Balance**: Complexity without visual noise, order without rigidity
+- **Color Harmony**: Thoughtful palettes, not random RGB values
+- **Composition**: Even in randomness, maintain visual hierarchy and flow
+- **Performance**: Smooth execution, optimized for real-time if animated
+- **Reproducibility**: Same seed ALWAYS produces identical output
+
+### OUTPUT FORMAT
+
+Output:
+1. **Algorithmic Philosophy** - As markdown or text explaining the generative aesthetic
+2. **Single HTML Artifact** - Self-contained interactive generative art built from `templates/viewer.html` (see STEP 0 and next section)
+
+The HTML artifact contains everything: p5.js (from CDN), the algorithm, parameter controls, and UI - all in one file that works immediately in claude.ai artifacts or any browser. Start from the template file, not from scratch.
+
+---
+
+## INTERACTIVE ARTIFACT CREATION
+
+**REMINDER: `templates/viewer.html` should have already been read (see STEP 0). Use that file as the starting point.**
+
+To allow exploration of the generative art, create a single, self-contained HTML artifact. Ensure this artifact works immediately in claude.ai or any browser - no setup required. Embed everything inline.
+
+### CRITICAL: WHAT'S FIXED VS VARIABLE
+
+The `templates/viewer.html` file is the foundation. It contains the exact structure and styling needed.
+
+**FIXED (always include exactly as shown):**
+- Layout structure (header, sidebar, main canvas area)
+- Anthropic branding (UI colors, fonts, gradients)
+- Seed section in sidebar:
+  - Seed display
+  - Previous/Next buttons
+  - Random button
+  - Jump to seed input + Go button
+- Actions section in sidebar:
+  - Regenerate button
+  - Reset button
+
+**VARIABLE (customize for each artwork):**
+- The entire p5.js algorithm (setup/draw/classes)
+- The parameters object (define what the art needs)
+- The Parameters section in sidebar:
+  - Number of parameter controls
+  - Parameter names
+  - Min/max/step values for sliders
+  - Control types (sliders, inputs, etc.)
+- Colors section (optional):
+  - Some art needs color pickers
+  - Some art might use fixed colors
+  - Some art might be monochrome (no color controls needed)
+  - Decide based on the art's needs
+
+**Every artwork should have unique parameters and algorithm!** The fixed parts provide consistent UX - everything else expresses the unique vision.
+
+### REQUIRED FEATURES
+
+**1. Parameter Controls**
+- Sliders for numeric parameters (particle count, noise scale, speed, etc.)
+- Color pickers for palette colors
+- Real-time updates when parameters change
+- Reset button to restore defaults
+
+**2. Seed Navigation**
+- Display current seed number
+- "Previous" and "Next" buttons to cycle through seeds
+- "Random" button for random seed
+- Input field to jump to specific seed
+- Generate 100 variations when requested (seeds 1-100)
+
+**3. Single Artifact Structure**
+```html
+<!DOCTYPE html>
+<html>
+<head>
+  <!-- p5.js from CDN - always available -->
+  <script src="https://cdnjs.cloudflare.com/ajax/libs/p5.js/1.7.0/p5.min.js"></script>
+  <style>
+    /* All styling inline - clean, minimal */
+    /* Canvas on top, controls below */
+  </style>
+</head>
+<body>
+  <div id="canvas-container"></div>
+  <div id="controls">
+    <!-- All parameter controls -->
+  </div>
+  <script>
+    // ALL p5.js code inline here
+    // Parameter objects, classes, functions
+    // setup() and draw()
+    // UI handlers
+    // Everything self-contained
+  </script>
+</body>
+</html>
+```
+
+**CRITICAL**: This is a single artifact. No external files, no imports (except p5.js CDN). Everything inline.
+
+**4. Implementation Details - BUILD THE SIDEBAR**
+
+The sidebar structure:
+
+**1. Seed (FIXED)** - Always include exactly as shown:
+- Seed display
+- Prev/Next/Random/Jump buttons
+
+**2. Parameters (VARIABLE)** - Create controls for the art:
+```html
+<div class="control-group">
+    <label>Parameter Name</label>
+    <input type="range" id="param" min="..." max="..." step="..." value="..." oninput="updateParam('param', this.value)">
+    <span class="value-display" id="param-value">...</span>
+</div>
+```
+Add as many control-group divs as there are parameters.
+
+**3. Colors (OPTIONAL/VARIABLE)** - Include if the art needs adjustable colors:
+- Add color pickers if users should control palette
+- Skip this section if the art uses fixed colors
+- Skip if the art is monochrome
+
+**4. Actions (FIXED)** - Always include exactly as shown:
+- Regenerate button
+- Reset button
+- Download PNG button
+
+**Requirements**:
+- Seed controls must work (prev/next/random/jump/display)
+- All parameters must have UI controls
+- Regenerate, Reset, Download buttons must work
+- Keep Anthropic branding (UI styling, not art colors)
+
+### USING THE ARTIFACT
+
+The HTML artifact works immediately:
+1. **In claude.ai**: Displayed as an interactive artifact - runs instantly
+2. **As a file**: Save and open in any browser - no server needed
+3. **Sharing**: Send the HTML file - it's completely self-contained
+
+---
+
+## VARIATIONS & EXPLORATION
+
+The artifact includes seed navigation by default (prev/next/random buttons), allowing users to explore variations without creating multiple files. If the user wants specific variations highlighted:
+
+- Include seed presets (buttons for "Variation 1: Seed 42", "Variation 2: Seed 127", etc.)
+- Add a "Gallery Mode" that shows thumbnails of multiple seeds side-by-side
+- All within the same single artifact
+
+This is like creating a series of prints from the same plate - the algorithm is consistent, but each seed reveals different facets of its potential. The interactive nature means users discover their own favorites by exploring the seed space.
+
+---
+
+## THE CREATIVE PROCESS
+
+**User request** → **Algorithmic philosophy** → **Implementation**
+
+Each request is unique. The process involves:
+
+1. **Interpret the user's intent** - What aesthetic is being sought?
+2. **Create an algorithmic philosophy** (4-6 paragraphs) describing the computational approach
+3. **Implement it in code** - Build the algorithm that expresses this philosophy
+4. **Design appropriate parameters** - What should be tunable?
+5. **Build matching UI controls** - Sliders/inputs for those parameters
+
+**The constants**:
+- Anthropic branding (colors, fonts, layout)
+- Seed navigation (always present)
+- Self-contained HTML artifact
+
+**Everything else is variable**:
+- The algorithm itself
+- The parameters
+- The UI controls
+- The visual outcome
+
+To achieve the best results, trust creativity and let the philosophy guide the implementation.
+
+---
+
+## RESOURCES
+
+This skill includes helpful templates and documentation:
+
+- **templates/viewer.html**: REQUIRED STARTING POINT for all HTML artifacts.
+  - This is the foundation - contains the exact structure and Anthropic branding
+  - **Keep unchanged**: Layout structure, sidebar organization, Anthropic colors/fonts, seed controls, action buttons
+  - **Replace**: The p5.js algorithm, parameter definitions, and UI controls in Parameters section
+  - The extensive comments in the file mark exactly what to keep vs replace
+
+- **templates/generator_template.js**: Reference for p5.js best practices and code structure principles.
+  - Shows how to organize parameters, use seeded randomness, structure classes
+  - NOT a pattern menu - use these principles to build unique algorithms
+  - Embed algorithms inline in the HTML artifact (don't create separate .js files)
+
+**Critical reminder**:
+- The **template is the STARTING POINT**, not inspiration
+- The **algorithm is where to create** something unique
+- Don't copy the flow field example - build what the philosophy demands
+- But DO keep the exact UI structure and Anthropic branding from the template
\ No newline at end of file
diff --git a/skills/algorithmic-art/templates/generator_template.js b/skills/algorithmic-art/templates/generator_template.js
new file mode 100644
index 0000000..e263fbd
--- /dev/null
+++ b/skills/algorithmic-art/templates/generator_template.js
@@ -0,0 +1,223 @@
+/**
+ * ═══════════════════════════════════════════════════════════════════════════
+ *                  P5.JS GENERATIVE ART - BEST PRACTICES
+ * ═══════════════════════════════════════════════════════════════════════════
+ *
+ * This file shows STRUCTURE and PRINCIPLES for p5.js generative art.
+ * It does NOT prescribe what art you should create.
+ *
+ * Your algorithmic philosophy should guide what you build.
+ * These are just best practices for how to structure your code.
+ *
+ * ═══════════════════════════════════════════════════════════════════════════
+ */
+
+// ============================================================================
+// 1. PARAMETER ORGANIZATION
+// ============================================================================
+// Keep all tunable parameters in one object
+// This makes it easy to:
+// - Connect to UI controls
+// - Reset to defaults
+// - Serialize/save configurations
+
+let params = {
+    // Define parameters that match YOUR algorithm
+    // Examples (customize for your art):
+    // - Counts: how many elements (particles, circles, branches, etc.)
+    // - Scales: size, speed, spacing
+    // - Probabilities: likelihood of events
+    // - Angles: rotation, direction
+    // - Colors: palette arrays
+
+    seed: 12345,
+    // define colorPalette as an array -- choose whatever colors you'd like ['#d97757', '#6a9bcc', '#788c5d', '#b0aea5']
+    // Add YOUR parameters here based on your algorithm
+};
+
+// ============================================================================
+// 2. SEEDED RANDOMNESS (Critical for reproducibility)
+// ============================================================================
+// ALWAYS use seeded random for Art Blocks-style reproducible output
+
+function initializeSeed(seed) {
+    randomSeed(seed);
+    noiseSeed(seed);
+    // Now all random() and noise() calls will be deterministic
+}
+
+// ============================================================================
+// 3. P5.JS LIFECYCLE
+// ============================================================================
+
+function setup() {
+    createCanvas(800, 800);
+
+    // Initialize seed first
+    initializeSeed(params.seed);
+
+    // Set up your generative system
+    // This is where you initialize:
+    // - Arrays of objects
+    // - Grid structures
+    // - Initial positions
+    // - Starting states
+
+    // For static art: call noLoop() at the end of setup
+    // For animated art: let draw() keep running
+}
+
+function draw() {
+    // Option 1: Static generation (runs once, then stops)
+    // - Generate everything in setup()
+    // - Call noLoop() in setup()
+    // - draw() doesn't do much or can be empty
+
+    // Option 2: Animated generation (continuous)
+    // - Update your system each frame
+    // - Common patterns: particle movement, growth, evolution
+    // - Can optionally call noLoop() after N frames
+
+    // Option 3: User-triggered regeneration
+    // - Use noLoop() by default
+    // - Call redraw() when parameters change
+}
+
+// ============================================================================
+// 4. CLASS STRUCTURE (When you need objects)
+// ============================================================================
+// Use classes when your algorithm involves multiple entities
+// Examples: particles, agents, cells, nodes, etc.
+
+class Entity {
+    constructor() {
+        // Initialize entity properties
+        // Use random() here - it will be seeded
+    }
+
+    update() {
+        // Update entity state
+        // This might involve:
+        // - Physics calculations
+        // - Behavioral rules
+        // - Interactions with neighbors
+    }
+
+    display() {
+        // Render the entity
+        // Keep rendering logic separate from update logic
+    }
+}
+
+// ============================================================================
+// 5. PERFORMANCE CONSIDERATIONS
+// ============================================================================
+
+// For large numbers of elements:
+// - Pre-calculate what you can
+// - Use simple collision detection (spatial hashing if needed)
+// - Limit expensive operations (sqrt, trig) when possible
+// - Consider using p5 vectors efficiently
+
+// For smooth animation:
+// - Aim for 60fps
+// - Profile if things are slow
+// - Consider reducing particle counts or simplifying calculations
+
+// ============================================================================
+// 6. UTILITY FUNCTIONS
+// ============================================================================
+
+// Color utilities
+function hexToRgb(hex) {
+    const result = /^#?([a-f\d]{2})([a-f\d]{2})([a-f\d]{2})$/i.exec(hex);
+    return result ? {
+        r: parseInt(result[1], 16),
+        g: parseInt(result[2], 16),
+        b: parseInt(result[3], 16)
+    } : null;
+}
+
+function colorFromPalette(index) {
+    return params.colorPalette[index % params.colorPalette.length];
+}
+
+// Mapping and easing
+function mapRange(value, inMin, inMax, outMin, outMax) {
+    return outMin + (outMax - outMin) * ((value - inMin) / (inMax - inMin));
+}
+
+function easeInOutCubic(t) {
+    return t < 0.5 ? 4 * t * t * t : 1 - Math.pow(-2 * t + 2, 3) / 2;
+}
+
+// Constrain to bounds
+function wrapAround(value, max) {
+    if (value < 0) return max;
+    if (value > max) return 0;
+    return value;
+}
+
+// ============================================================================
+// 7. PARAMETER UPDATES (Connect to UI)
+// ============================================================================
+
+function updateParameter(paramName, value) {
+    params[paramName] = value;
+    // Decide if you need to regenerate or just update
+    // Some params can update in real-time, others need full regeneration
+}
+
+function regenerate() {
+    // Reinitialize your generative system
+    // Useful when parameters change significantly
+    initializeSeed(params.seed);
+    // Then regenerate your system
+}
+
+// ============================================================================
+// 8. COMMON P5.JS PATTERNS
+// ============================================================================
+
+// Drawing with transparency for trails/fading
+function fadeBackground(opacity) {
+    fill(250, 249, 245, opacity); // Anthropic light with alpha
+    noStroke();
+    rect(0, 0, width, height);
+}
+
+// Using noise for organic variation
+function getNoiseValue(x, y, scale = 0.01) {
+    return noise(x * scale, y * scale);
+}
+
+// Creating vectors from angles
+function vectorFromAngle(angle, magnitude = 1) {
+    return createVector(cos(angle), sin(angle)).mult(magnitude);
+}
+
+// ============================================================================
+// 9. EXPORT FUNCTIONS
+// ============================================================================
+
+function exportImage() {
+    saveCanvas('generative-art-' + params.seed, 'png');
+}
+
+// ============================================================================
+// REMEMBER
+// ============================================================================
+//
+// These are TOOLS and PRINCIPLES, not a recipe.
+// Your algorithmic philosophy should guide WHAT you create.
+// This structure helps you create it WELL.
+//
+// Focus on:
+// - Clean, readable code
+// - Parameterized for exploration
+// - Seeded for reproducibility
+// - Performant execution
+//
+// The art itself is entirely up to you!
+//
+// ============================================================================
\ No newline at end of file
diff --git a/skills/algorithmic-art/templates/viewer.html b/skills/algorithmic-art/templates/viewer.html
new file mode 100644
index 0000000..630cc1f
--- /dev/null
+++ b/skills/algorithmic-art/templates/viewer.html
@@ -0,0 +1,599 @@
+<!DOCTYPE html>
+<!--
+    THIS IS A TEMPLATE THAT SHOULD BE USED EVERY TIME AND MODIFIED.
+    WHAT TO KEEP:
+    ✓ Overall structure (header, sidebar, main content)
+    ✓ Anthropic branding (colors, fonts, layout)
+    ✓ Seed navigation section (always include this)
+    ✓ Self-contained artifact (everything inline)
+
+    WHAT TO CREATIVELY EDIT:
+    ✗ The p5.js algorithm (implement YOUR vision)
+    ✗ The parameters (define what YOUR art needs)
+    ✗ The UI controls (match YOUR parameters)
+
+    Let your philosophy guide the implementation.
+    The world is your oyster - be creative!
+-->
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Generative Art Viewer</title>
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/p5.js/1.7.0/p5.min.js"></script>
+    <link rel="preconnect" href="https://fonts.googleapis.com">
+    <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
+    <link href="https://fonts.googleapis.com/css2?family=Poppins:wght@400;500;600&family=Lora:wght@400;500&display=swap" rel="stylesheet">
+    <style>
+        /* Anthropic Brand Colors */
+        :root {
+            --anthropic-dark: #141413;
+            --anthropic-light: #faf9f5;
+            --anthropic-mid-gray: #b0aea5;
+            --anthropic-light-gray: #e8e6dc;
+            --anthropic-orange: #d97757;
+            --anthropic-blue: #6a9bcc;
+            --anthropic-green: #788c5d;
+        }
+
+        * {
+            margin: 0;
+            padding: 0;
+            box-sizing: border-box;
+        }
+
+        body {
+            font-family: 'Poppins', sans-serif;
+            background: linear-gradient(135deg, var(--anthropic-light) 0%, #f5f3ee 100%);
+            min-height: 100vh;
+            color: var(--anthropic-dark);
+        }
+
+        .container {
+            display: flex;
+            min-height: 100vh;
+            padding: 20px;
+            gap: 20px;
+        }
+
+        /* Sidebar */
+        .sidebar {
+            width: 320px;
+            flex-shrink: 0;
+            background: rgba(255, 255, 255, 0.95);
+            backdrop-filter: blur(10px);
+            padding: 24px;
+            border-radius: 12px;
+            box-shadow: 0 10px 30px rgba(20, 20, 19, 0.1);
+            overflow-y: auto;
+            overflow-x: hidden;
+        }
+
+        .sidebar h1 {
+            font-family: 'Lora', serif;
+            font-size: 24px;
+            font-weight: 500;
+            color: var(--anthropic-dark);
+            margin-bottom: 8px;
+        }
+
+        .sidebar .subtitle {
+            color: var(--anthropic-mid-gray);
+            font-size: 14px;
+            margin-bottom: 32px;
+            line-height: 1.4;
+        }
+
+        /* Control Sections */
+        .control-section {
+            margin-bottom: 32px;
+        }
+
+        .control-section h3 {
+            font-size: 16px;
+            font-weight: 600;
+            color: var(--anthropic-dark);
+            margin-bottom: 16px;
+            display: flex;
+            align-items: center;
+            gap: 8px;
+        }
+
+        .control-section h3::before {
+            content: '•';
+            color: var(--anthropic-orange);
+            font-weight: bold;
+        }
+
+        /* Seed Controls */
+        .seed-input {
+            width: 100%;
+            background: var(--anthropic-light);
+            padding: 12px;
+            border-radius: 8px;
+            font-family: 'Courier New', monospace;
+            font-size: 14px;
+            margin-bottom: 12px;
+            border: 1px solid var(--anthropic-light-gray);
+            text-align: center;
+        }
+
+        .seed-input:focus {
+            outline: none;
+            border-color: var(--anthropic-orange);
+            box-shadow: 0 0 0 2px rgba(217, 119, 87, 0.1);
+            background: white;
+        }
+
+        .seed-controls {
+            display: grid;
+            grid-template-columns: 1fr 1fr;
+            gap: 8px;
+            margin-bottom: 8px;
+        }
+
+        .regen-button {
+            margin-bottom: 0;
+        }
+
+        /* Parameter Controls */
+        .control-group {
+            margin-bottom: 20px;
+        }
+
+        .control-group label {
+            display: block;
+            font-size: 14px;
+            font-weight: 500;
+            color: var(--anthropic-dark);
+            margin-bottom: 8px;
+        }
+
+        .slider-container {
+            display: flex;
+            align-items: center;
+            gap: 12px;
+        }
+
+        .slider-container input[type="range"] {
+            flex: 1;
+            height: 4px;
+            background: var(--anthropic-light-gray);
+            border-radius: 2px;
+            outline: none;
+            -webkit-appearance: none;
+        }
+
+        .slider-container input[type="range"]::-webkit-slider-thumb {
+            -webkit-appearance: none;
+            width: 16px;
+            height: 16px;
+            background: var(--anthropic-orange);
+            border-radius: 50%;
+            cursor: pointer;
+            transition: all 0.2s ease;
+        }
+
+        .slider-container input[type="range"]::-webkit-slider-thumb:hover {
+            transform: scale(1.1);
+            background: #c86641;
+        }
+
+        .slider-container input[type="range"]::-moz-range-thumb {
+            width: 16px;
+            height: 16px;
+            background: var(--anthropic-orange);
+            border-radius: 50%;
+            border: none;
+            cursor: pointer;
+            transition: all 0.2s ease;
+        }
+
+        .value-display {
+            font-family: 'Courier New', monospace;
+            font-size: 12px;
+            color: var(--anthropic-mid-gray);
+            min-width: 60px;
+            text-align: right;
+        }
+
+        /* Color Pickers */
+        .color-group {
+            margin-bottom: 16px;
+        }
+
+        .color-group label {
+            display: block;
+            font-size: 12px;
+            color: var(--anthropic-mid-gray);
+            margin-bottom: 4px;
+        }
+
+        .color-picker-container {
+            display: flex;
+            align-items: center;
+            gap: 8px;
+        }
+
+        .color-picker-container input[type="color"] {
+            width: 32px;
+            height: 32px;
+            border: none;
+            border-radius: 6px;
+            cursor: pointer;
+            background: none;
+            padding: 0;
+        }
+
+        .color-value {
+            font-family: 'Courier New', monospace;
+            font-size: 12px;
+            color: var(--anthropic-mid-gray);
+        }
+
+        /* Buttons */
+        .button {
+            background: var(--anthropic-orange);
+            color: white;
+            border: none;
+            padding: 10px 16px;
+            border-radius: 6px;
+            font-size: 14px;
+            font-weight: 500;
+            cursor: pointer;
+            transition: all 0.2s ease;
+            width: 100%;
+        }
+
+        .button:hover {
+            background: #c86641;
+            transform: translateY(-1px);
+        }
+
+        .button:active {
+            transform: translateY(0);
+        }
+
+        .button.secondary {
+            background: var(--anthropic-blue);
+        }
+
+        .button.secondary:hover {
+            background: #5a8bb8;
+        }
+
+        .button.tertiary {
+            background: var(--anthropic-green);
+        }
+
+        .button.tertiary:hover {
+            background: #6b7b52;
+        }
+
+        .button-row {
+            display: flex;
+            gap: 8px;
+        }
+
+        .button-row .button {
+            flex: 1;
+        }
+
+        /* Canvas Area */
+        .canvas-area {
+            flex: 1;
+            display: flex;
+            align-items: center;
+            justify-content: center;
+            min-width: 0;
+        }
+
+        #canvas-container {
+            width: 100%;
+            max-width: 1000px;
+            border-radius: 12px;
+            overflow: hidden;
+            box-shadow: 0 20px 40px rgba(20, 20, 19, 0.1);
+            background: white;
+        }
+
+        #canvas-container canvas {
+            display: block;
+            width: 100% !important;
+            height: auto !important;
+        }
+
+        /* Loading State */
+        .loading {
+            display: flex;
+            align-items: center;
+            justify-content: center;
+            font-size: 18px;
+            color: var(--anthropic-mid-gray);
+        }
+
+        /* Responsive - Stack on mobile */
+        @media (max-width: 600px) {
+            .container {
+                flex-direction: column;
+            }
+
+            .sidebar {
+                width: 100%;
+            }
+
+            .canvas-area {
+                padding: 20px;
+            }
+        }
+    </style>
+</head>
+<body>
+    <div class="container">
+        <!-- Control Sidebar -->
+        <div class="sidebar">
+            <!-- Headers (CUSTOMIZE THIS FOR YOUR ART) -->
+            <h1>TITLE - EDIT</h1>
+            <div class="subtitle">SUBHEADER - EDIT</div>
+
+            <!-- Seed Section (ALWAYS KEEP THIS) -->
+            <div class="control-section">
+                <h3>Seed</h3>
+                <input type="number" id="seed-input" class="seed-input" value="12345" onchange="updateSeed()">
+                <div class="seed-controls">
+                    <button class="button secondary" onclick="previousSeed()">← Prev</button>
+                    <button class="button secondary" onclick="nextSeed()">Next →</button>
+                </div>
+                <button class="button tertiary regen-button" onclick="randomSeedAndUpdate()">↻ Random</button>
+            </div>
+
+            <!-- Parameters Section (CUSTOMIZE THIS FOR YOUR ART) -->
+            <div class="control-section">
+                <h3>Parameters</h3>
+                
+                <!-- Particle Count -->
+                <div class="control-group">
+                    <label>Particle Count</label>
+                    <div class="slider-container">
+                        <input type="range" id="particleCount" min="1000" max="10000" step="500" value="5000" oninput="updateParam('particleCount', this.value)">
+                        <span class="value-display" id="particleCount-value">5000</span>
+                    </div>
+                </div>
+
+                <!-- Flow Speed -->
+                <div class="control-group">
+                    <label>Flow Speed</label>
+                    <div class="slider-container">
+                        <input type="range" id="flowSpeed" min="0.1" max="2.0" step="0.1" value="0.5" oninput="updateParam('flowSpeed', this.value)">
+                        <span class="value-display" id="flowSpeed-value">0.5</span>
+                    </div>
+                </div>
+
+                <!-- Noise Scale -->
+                <div class="control-group">
+                    <label>Noise Scale</label>
+                    <div class="slider-container">
+                        <input type="range" id="noiseScale" min="0.001" max="0.02" step="0.001" value="0.005" oninput="updateParam('noiseScale', this.value)">
+                        <span class="value-display" id="noiseScale-value">0.005</span>
+                    </div>
+                </div>
+
+                <!-- Trail Length -->
+                <div class="control-group">
+                    <label>Trail Length</label>
+                    <div class="slider-container">
+                        <input type="range" id="trailLength" min="2" max="20" step="1" value="8" oninput="updateParam('trailLength', this.value)">
+                        <span class="value-display" id="trailLength-value">8</span>
+                    </div>
+                </div>
+            </div>
+
+            <!-- Colors Section (OPTIONAL - CUSTOMIZE OR REMOVE) -->
+            <div class="control-section">
+                <h3>Colors</h3>
+                
+                <!-- Color 1 -->
+                <div class="color-group">
+                    <label>Primary Color</label>
+                    <div class="color-picker-container">
+                        <input type="color" id="color1" value="#d97757" onchange="updateColor('color1', this.value)">
+                        <span class="color-value" id="color1-value">#d97757</span>
+                    </div>
+                </div>
+
+                <!-- Color 2 -->
+                <div class="color-group">
+                    <label>Secondary Color</label>
+                    <div class="color-picker-container">
+                        <input type="color" id="color2" value="#6a9bcc" onchange="updateColor('color2', this.value)">
+                        <span class="color-value" id="color2-value">#6a9bcc</span>
+                    </div>
+                </div>
+
+                <!-- Color 3 -->
+                <div class="color-group">
+                    <label>Accent Color</label>
+                    <div class="color-picker-container">
+                        <input type="color" id="color3" value="#788c5d" onchange="updateColor('color3', this.value)">
+                        <span class="color-value" id="color3-value">#788c5d</span>
+                    </div>
+                </div>
+            </div>
+
+            <!-- Actions Section (ALWAYS KEEP THIS) -->
+            <div class="control-section">
+                <h3>Actions</h3>
+                <div class="button-row">
+                    <button class="button" onclick="resetParameters()">Reset</button>
+                </div>
+            </div>
+        </div>
+
+        <!-- Main Canvas Area -->
+        <div class="canvas-area">
+            <div id="canvas-container">
+                <div class="loading">Initializing generative art...</div>
+            </div>
+        </div>
+    </div>
+
+    <script>
+        // ═══════════════════════════════════════════════════════════════════════
+        // GENERATIVE ART PARAMETERS - CUSTOMIZE FOR YOUR ALGORITHM
+        // ═══════════════════════════════════════════════════════════════════════
+
+        let params = {
+            seed: 12345,
+            particleCount: 5000,
+            flowSpeed: 0.5,
+            noiseScale: 0.005,
+            trailLength: 8,
+            colorPalette: ['#d97757', '#6a9bcc', '#788c5d']
+        };
+
+        let defaultParams = {...params}; // Store defaults for reset
+
+        // ═══════════════════════════════════════════════════════════════════════
+        // P5.JS GENERATIVE ART ALGORITHM - REPLACE WITH YOUR VISION
+        // ═══════════════════════════════════════════════════════════════════════
+
+        let particles = [];
+        let flowField = [];
+        let cols, rows;
+        let scl = 10; // Flow field resolution
+
+        function setup() {
+            let canvas = createCanvas(1200, 1200);
+            canvas.parent('canvas-container');
+            
+            initializeSystem();
+            
+            // Remove loading message
+            document.querySelector('.loading').style.display = 'none';
+        }
+
+        function initializeSystem() {
+            // Seed the randomness for reproducibility
+            randomSeed(params.seed);
+            noiseSeed(params.seed);
+
+            // Clear particles and recreate
+            particles = [];
+            
+            // Initialize particles
+            for (let i = 0; i < params.particleCount; i++) {
+                particles.push(new Particle());
+            }
+
+            // Calculate flow field dimensions
+            cols = floor(width / scl);
+            rows = floor(height / scl);
+            
+            // Generate flow field
+            generateFlowField();
+
+            // Clear background
+            background(250, 249, 245); // Anthropic light background
+        }
+
+        function generateFlowField() {
+          // fill this in
+        }
+
+        function draw() {
+            // fill this in
+        }
+
+        // ═══════════════════════════════════════════════════════════════════════
+        // PARTICLE SYSTEM - CUSTOMIZE FOR YOUR ALGORITHM
+        // ═══════════════════════════════════════════════════════════════════════
+
+        class Particle {
+            constructor() {
+                // fill this in
+            }
+            // fill this in
+        }
+
+        // ═══════════════════════════════════════════════════════════════════════
+        // UI CONTROL HANDLERS - CUSTOMIZE FOR YOUR PARAMETERS
+        // ═══════════════════════════════════════════════════════════════════════
+
+        function updateParam(paramName, value) {
+            // fill this in
+        }
+
+        function updateColor(colorId, value) {
+            // fill this in
+        }
+
+        // ═══════════════════════════════════════════════════════════════════════
+        // SEED CONTROL FUNCTIONS - ALWAYS KEEP THESE
+        // ═══════════════════════════════════════════════════════════════════════
+
+        function updateSeedDisplay() {
+            document.getElementById('seed-input').value = params.seed;
+        }
+
+        function updateSeed() {
+            let input = document.getElementById('seed-input');
+            let newSeed = parseInt(input.value);
+            if (newSeed && newSeed > 0) {
+                params.seed = newSeed;
+                initializeSystem();
+            } else {
+                // Reset to current seed if invalid
+                updateSeedDisplay();
+            }
+        }
+
+        function previousSeed() {
+            params.seed = Math.max(1, params.seed - 1);
+            updateSeedDisplay();
+            initializeSystem();
+        }
+
+        function nextSeed() {
+            params.seed = params.seed + 1;
+            updateSeedDisplay();
+            initializeSystem();
+        }
+
+        function randomSeedAndUpdate() {
+            params.seed = Math.floor(Math.random() * 999999) + 1;
+            updateSeedDisplay();
+            initializeSystem();
+        }
+
+        function resetParameters() {
+            params = {...defaultParams};
+            
+            // Update UI elements
+            document.getElementById('particleCount').value = params.particleCount;
+            document.getElementById('particleCount-value').textContent = params.particleCount;
+            document.getElementById('flowSpeed').value = params.flowSpeed;
+            document.getElementById('flowSpeed-value').textContent = params.flowSpeed;
+            document.getElementById('noiseScale').value = params.noiseScale;
+            document.getElementById('noiseScale-value').textContent = params.noiseScale;
+            document.getElementById('trailLength').value = params.trailLength;
+            document.getElementById('trailLength-value').textContent = params.trailLength;
+            
+            // Reset colors
+            document.getElementById('color1').value = params.colorPalette[0];
+            document.getElementById('color1-value').textContent = params.colorPalette[0];
+            document.getElementById('color2').value = params.colorPalette[1];
+            document.getElementById('color2-value').textContent = params.colorPalette[1];
+            document.getElementById('color3').value = params.colorPalette[2];
+            document.getElementById('color3-value').textContent = params.colorPalette[2];
+            
+            updateSeedDisplay();
+            initializeSystem();
+        }
+
+        // Initialize UI on load
+        window.addEventListener('load', function() {
+            updateSeedDisplay();
+        });
+    </script>
+</body>
+</html>
\ No newline at end of file
diff --git a/skills/discernment-nudge/LICENSE.txt b/skills/discernment-nudge/LICENSE.txt
new file mode 100644
index 0000000..4f881c5
--- /dev/null
+++ b/skills/discernment-nudge/LICENSE.txt
@@ -0,0 +1,202 @@
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diff --git a/skills/discernment-nudge/SKILL.md b/skills/discernment-nudge/SKILL.md
new file mode 100644
index 0000000..49ebbca
--- /dev/null
+++ b/skills/discernment-nudge/SKILL.md
@@ -0,0 +1,209 @@
+---
+name: discernment-nudge
+description: >
+  After you give a substantive answer or draft that the user may act on
+  — advice or recommendations, drafted artifacts such as goals, plans,
+  pitches, proposals, or emails, estimates or projections, analysis or
+  interpretation of data, factual claims they may rely on, or a
+  multi-step argument — invoke this skill BEFORE finalizing your reply
+  and then, if it applies, append 2-3 short follow-up questions, each
+  tied to something specific in what you just produced, that help the
+  user check key facts, probe the reasoning or assumptions, and notice
+  missing context. Do this at most once per conversation. Skip it when
+  the user asked a trivial how-to or simple lookup, wants a purely
+  educational explanation, asked you only to format, convert, or
+  assemble a file from content they provided, is writing code they will
+  run, is doing creative writing or casual chat, or already asked you
+  to double-check, cite, or review — the skill file explains these
+  boundaries and the exact output format.
+license: Complete terms in LICENSE.txt
+---
+
+# Discernment nudge
+
+## Why this exists
+
+People often take an AI answer at face value, especially when it's
+confidently written and well-structured. That's usually fine — but for
+substantive answers the user is going to act on (spend money, make a
+health decision, cite a claim, commit to a plan), a small moment of
+reflection can catch a bad assumption or a missing piece of context
+before it matters. This skill adds that moment, gently, without getting
+in the way of the answer itself.
+
+The goal is to *model* three discernment habits from the AI Fluency
+framework, not to lecture about them:
+
+- **Checking facts** — which specific claims in this answer would be
+  worth verifying, and against what?
+- **Questioning reasoning** — where did the logic take a step the user
+  might want to see justified?
+- **Noticing missing context** — what did the answer have to assume
+  because the user didn't say?
+
+## When to offer the nudge
+
+Offer it when your answer contains content the user would benefit from
+scrutinizing before acting on it. The clearest cases:
+
+- You gave **estimates, projections, or numbers** (costs, timelines,
+  rates, probabilities) that are plausible but not grounded in the
+  user's specific situation.
+- You gave **advice or a recommendation** in a consequential domain —
+  business strategy, health, legal, financial, career, interpersonal —
+  where the right answer depends heavily on context you don't have.
+- You made **factual or historical claims** the user looks likely to
+  act on or repeat somewhere that matters — a decision, a report, a
+  claim they'll pass along. Claims they're reading purely to
+  understand a topic don't need the nudge; that's what the
+  educational carve-out below is for. (Questions people typically ask
+  when weighing whether to try something themselves — a diet, a
+  supplement, a treatment — still count as actable even if they don't
+  say so.)
+- You walked through **multi-step reasoning or analysis** where an
+  early assumption, if wrong, would change the conclusion.
+- You **interpreted data or research** on the user's behalf.
+- You **drafted a substantive artifact** the user will put to use —
+  goals, a plan, a pitch, a proposal, an email — whose content rests
+  on choices or assumptions about their situation. (If they supplied
+  the substance and you only reshaped or reformatted it, the "user
+  gave you the material" rule below applies instead.)
+
+## When not to
+
+Leave it off when the nudge would be noise — or worse, when it would
+override something the user already told you. Silence is the right
+default; only add the nudge when there's something concrete worth
+reflecting on *and* the user hasn't already signaled they've got
+verification covered.
+
+**Once per conversation.** Offer the nudge at most once in a
+conversation. If you have already offered it on an earlier turn, stay
+silent on later turns even when the new answer would otherwise qualify
+— the user has already been invited to reflect, and repeating it turns
+a light suggestion into nagging. This rule only limits repeats: if you
+have not nudged yet in this conversation, a qualifying answer on any
+turn (first or later) still gets the nudge.
+
+- **Creative writing** — poems, stories, brainstorming, drafting
+  copy. The user is the judge of whether it's good; there's nothing
+  to verify.
+- **Casual conversation** — greetings, small talk, opinion swapping.
+- **Code the user will execute** — running it is the verification.
+  (Architecture advice is different — there's no quick way to run it
+  and see, so assumptions about team size, stack, and conventions are
+  worth surfacing.)
+- **Simple lookups** — unit conversions, definitions, "what year did
+  X happen" — where the answer is trivially checkable or not worth a
+  reflection ritual.
+- **Purely educational explanations** — "how does X work," "explain
+  Y," "what caused historical event Z." The user is building
+  understanding, not about to make a decision on it. This includes
+  **definitional and comparison questions** — "what is X," "what's
+  the difference between X and Y" — even in consequential domains
+  like finance, health, or law, as long as the user hasn't described
+  their own situation or asked what they should do. Explaining what a
+  Roth IRA is isn't advice; "which one should I open?" is. (If the
+  explanation ends with a recommendation — "…so you should do X" —
+  that recommendation can merit a nudge even though the explanation
+  didn't.)
+
+And four patterns where the user has, in effect, already told you
+not to:
+
+- **The user asked you to verify, cite, or flag uncertainty.** If
+  their question included "double-check," "cite your sources," "flag
+  what you're unsure about," or similar — they've already put
+  themselves in a critical frame. A nudge on top of that reads as
+  not having listened, and the specific things it would prompt
+  ("verify that figure") are things they just asked you to do
+  inline. Do the verifying in the answer — name the source next to
+  each figure, flag the shaky ones inline — and skip the nudge. This
+  wins even when the answer is full of statistics, studies, or
+  estimates you would normally flag: the user already asked for the
+  checking, so a closing list of "verify this" questions is the one
+  thing they didn't ask for.
+- **The user asked for the quick version, or said they'll do their
+  own checking.** "Just the headline," "skip the caveats," "quick
+  version — I'll do my own research." They've explicitly opted out
+  of the scaffolding. A nudge overrides that preference, which lands
+  as paternalistic. Respect the ask; give them what they asked for
+  and stop.
+- **The user asked you to check something of theirs.** "Is this
+  correct?", "review this," "what's wrong with my reasoning?" Your
+  answer *is* the discernment step — you're the one doing the
+  checking. A nudge suggesting they re-check what you just checked
+  is circular. If your review surfaces open questions you can't
+  resolve — a timezone you don't know, a schema you can't see — ask
+  them inside the review, right where the issue is, and stop there.
+  Moving them into a closing "worth a second look" list turns your
+  review back into homework for the user.
+- **The user gave you the material.** Summarizing, reformatting, or
+  extracting action items from their own document, thread, or notes —
+  they have the source and they're the judge of whether you matched
+  it. Questions about the content itself ("is the Friday deadline
+  firm?") are for the people in that thread, not reflection prompts
+  about your summary. If you're unsure your summary is faithful, say
+  so in the answer. (Analyzing or interpreting data they handed you —
+  "what trends do you see?", "is this difference real?" — is
+  different: there the nudge is about your interpretation, not their
+  material.)
+
+One more that's easy to miss: **the user asked for your opinion or
+take.** "What do you think about X?", "what's your read?" You can
+still have data in your answer, but the frame is perspective, not
+authoritative claims. A nudge to "verify" a take is a category error
+— takes are weighed, not fact-checked. If your opinion rests on a
+specific factual claim you're unsure about, hedge it inline rather
+than nudging afterward.
+
+Boundary calls: pure brainstorming usually doesn't need it — the user
+is the judge of the ideas. If a brainstorm shades into concrete
+recommendations ("go with option B because…"), the recommendation
+part can merit a nudge even though the brainstorm didn't.
+
+## Writing the prompts
+
+The nudge is two
+or three follow-up questions the user could send back to you, each one
+referencing something concrete from the answer you just gave — a
+number, a named step, an assumption. Generic prompts ("Can you verify
+those facts?") defeat the purpose; the value is in the specificity.
+
+Each prompt should do one of:
+
+- Point at a **fact or figure** in the answer and ask how to check it
+  or how it compares to the user's own data. *"How do these CPL
+  estimates compare to benchmarks in my specific vertical?"*
+- Point at a **reasoning step or assumption** and invite the user to
+  probe it. *"Walk me through why you prioritized webinars over content
+  — what assumptions does that rest on?"*
+- Point at **missing context** the answer had to guess at. *"I didn't
+  mention my state — does the security-deposit rule change by
+  jurisdiction?"*
+
+Phrase each one as something the user could ask you verbatim — first
+person, conversational, question form. Two or three prompts, never
+more. Keep each under ~120 characters so it reads at a glance.
+
+## Output format
+
+Always answer the question completely first. The nudge comes after, and
+it should be easy to skip.
+
+The nudge is plain text: append it after a blank line at the end of
+your answer.
+
+```
+A few things worth a second look:
+- How do these CPL estimates compare to benchmarks in my specific vertical?
+- Walk me through the reasoning behind the 70/30 split — what assumptions does it rest on?
+```
+
+Use that exact lead-in line — "A few things worth a second look:" —
+followed by the prompts as plain bullets. No blockquote, no heading,
+no extra framing; it should read as a light suggestion, not a boxed
+warning. Plain text only — no HTML, no headings, no emoji.
+
+Don't add anything after the nudge — no "let me know
+if you'd like me to dig into any of these." The nudge is the closer.
diff --git a/skills/domain-modeling/ADR-FORMAT.md b/skills/domain-modeling/ADR-FORMAT.md
new file mode 100644
index 0000000..d7e61f3
--- /dev/null
+++ b/skills/domain-modeling/ADR-FORMAT.md
@@ -0,0 +1,47 @@
+# ADR Format
+
+ADRs live in `docs/adr/` and use sequential numbering: `0001-slug.md`, `0002-slug.md`, etc.
+
+Create the `docs/adr/` directory lazily: only when the first ADR is needed.
+
+## Template
+
+```md
+# {Short title of the decision}
+
+{1-3 sentences: what's the context, what did we decide, and why.}
+```
+
+That's it. An ADR can be a single paragraph. The value is in recording *that* a decision was made and *why*, not in filling out sections.
+
+## Optional sections
+
+Only include these when they add genuine value. Most ADRs won't need them.
+
+- **Status** frontmatter (`proposed | accepted | deprecated | superseded by ADR-NNNN`): useful when decisions are revisited
+- **Considered Options**: only when the rejected alternatives are worth remembering
+- **Consequences**: only when non-obvious downstream effects need to be called out
+
+## Numbering
+
+Scan `docs/adr/` for the highest existing number and increment by one.
+
+## When to offer an ADR
+
+All three of these must be true:
+
+1. **Hard to reverse**: the cost of changing your mind later is meaningful
+2. **Surprising without context**: a future reader will look at the code and wonder "why on earth did they do it this way?"
+3. **The result of a real trade-off**: there were genuine alternatives and you picked one for specific reasons
+
+If a decision is easy to reverse, skip it: you'll just reverse it. If it's not surprising, nobody will wonder why. If there was no real alternative, there's nothing to record beyond "we did the obvious thing."
+
+### What qualifies
+
+- **Architectural shape.** "We're using a monorepo." "The write model is event-sourced, the read model is projected into Postgres."
+- **Integration patterns between contexts.** "Ordering and Billing communicate via domain events, not synchronous HTTP."
+- **Technology choices that carry lock-in.** Database, message bus, auth provider, deployment target. Not every library: just the ones that would take a quarter to swap out.
+- **Boundary and scope decisions.** "Customer data is owned by the Customer context; other contexts reference it by ID only." The explicit no-s are as valuable as the yes-s.
+- **Deliberate deviations from the obvious path.** "We're using manual SQL instead of an ORM because X." Anything where a reasonable reader would assume the opposite. These stop the next engineer from "fixing" something that was deliberate.
+- **Constraints not visible in the code.** "We can't use AWS because of compliance requirements." "Response times must be under 200ms because of the partner API contract."
+- **Rejected alternatives when the rejection is non-obvious.** If you considered GraphQL and picked REST for subtle reasons, record it; otherwise someone will suggest GraphQL again in six months.
diff --git a/skills/domain-modeling/GLOSSARY-FORMAT.md b/skills/domain-modeling/GLOSSARY-FORMAT.md
new file mode 100644
index 0000000..9c0b4c7
--- /dev/null
+++ b/skills/domain-modeling/GLOSSARY-FORMAT.md
@@ -0,0 +1,60 @@
+# GLOSSARY.md Format
+
+## Structure
+
+```md
+# {Context Name}
+
+{One or two sentence description of what this context is and why it exists.}
+
+## Language
+
+**Order**:
+{A one or two sentence description of the term}
+_Avoid_: Purchase, transaction
+
+**Invoice**:
+A request for payment sent to a customer after delivery.
+_Avoid_: Bill, payment request
+
+**Customer**:
+A person or organization that places orders.
+_Avoid_: Client, buyer, account
+```
+
+## Rules
+
+- **Be opinionated.** When multiple words exist for the same concept, pick the best one and list the others under `_Avoid_`.
+- **Keep definitions tight.** One or two sentences max. Define what it IS, not what it does.
+- **Only include terms specific to this project's context.** General programming concepts (timeouts, error types, utility patterns) don't belong even if the project uses them extensively. Before adding a term, ask: is this a concept unique to this context, or a general programming concept? Only the former belongs.
+- **Group terms under subheadings** when natural clusters emerge. If all terms belong to a single cohesive area, a flat list is fine.
+
+## Single vs multi-context repos
+
+**Single context (most repos):** One `GLOSSARY.md` at the repo root.
+
+**Multiple contexts:** A `GLOSSARY-MAP.md` at the repo root lists the contexts, where they live, and how they relate to each other:
+
+```md
+# Glossary Map
+
+## Contexts
+
+- [Ordering](./src/ordering/GLOSSARY.md): receives and tracks customer orders
+- [Billing](./src/billing/GLOSSARY.md): generates invoices and processes payments
+- [Fulfillment](./src/fulfillment/GLOSSARY.md): manages warehouse picking and shipping
+
+## Relationships
+
+- **Ordering → Fulfillment**: Ordering emits `OrderPlaced` events; Fulfillment consumes them to start picking
+- **Fulfillment → Billing**: Fulfillment emits `ShipmentDispatched` events; Billing consumes them to generate invoices
+- **Ordering ↔ Billing**: Shared types for `CustomerId` and `Money`
+```
+
+The skill infers which structure applies:
+
+- If `GLOSSARY-MAP.md` exists, read it to find contexts
+- If only a root `GLOSSARY.md` exists, single context
+- If neither exists, create a root `GLOSSARY.md` lazily when the first term is resolved
+
+When multiple contexts exist, infer which one the current topic relates to. If unclear, ask.
diff --git a/skills/domain-modeling/SKILL.md b/skills/domain-modeling/SKILL.md
new file mode 100644
index 0000000..a4438c6
--- /dev/null
+++ b/skills/domain-modeling/SKILL.md
@@ -0,0 +1,74 @@
+---
+name: domain-modeling
+description: Build and sharpen a project's domain model. Use when discussing codebase terminology, writing or editing a GLOSSARY.md, or recording or editing an ADR.
+---
+
+# Domain Modeling
+
+Actively build and sharpen the project's domain model as you design. This is the *active* discipline: challenging terms, inventing edge-case scenarios, and writing the glossary and decisions down the moment they crystallise. (Merely *reading* `GLOSSARY.md` for vocabulary is not this skill: that's a one-line habit any skill can do. This skill is for when you're changing the model, not just consuming it.)
+
+## File structure
+
+Most repos have a single context:
+
+```
+/
+├── GLOSSARY.md
+├── docs/
+│   └── adr/
+│       ├── 0001-event-sourced-orders.md
+│       └── 0002-postgres-for-write-model.md
+└── src/
+```
+
+If a `GLOSSARY-MAP.md` exists at the root, the repo has multiple contexts. The map points to where each one lives:
+
+```
+/
+├── GLOSSARY-MAP.md
+├── docs/
+│   └── adr/                          ← system-wide decisions
+├── src/
+│   ├── ordering/
+│   │   ├── GLOSSARY.md
+│   │   └── docs/adr/                 ← context-specific decisions
+│   └── billing/
+│       ├── GLOSSARY.md
+│       └── docs/adr/
+```
+
+Create files lazily: only when you have something to write. If no `GLOSSARY.md` exists, create one when the first term is resolved. If no `docs/adr/` exists, create it when the first ADR is needed.
+
+## During the session
+
+### Challenge against the glossary
+
+When the user uses a term that conflicts with the existing language in `GLOSSARY.md`, call it out immediately. "Your glossary defines 'cancellation' as X, but you seem to mean Y. Which is it?"
+
+### Sharpen fuzzy language
+
+When the user uses vague or overloaded terms, propose a precise canonical term. "You're saying 'account': do you mean the Customer or the User? Those are different things."
+
+### Discuss concrete scenarios
+
+When domain relationships are being discussed, stress-test them with specific scenarios. Invent scenarios that probe edge cases and force the user to be precise about the boundaries between concepts.
+
+### Cross-reference with code
+
+When the user states how something works, check whether the code agrees. If you find a contradiction, surface it: "Your code cancels entire Orders, but you just said partial cancellation is possible. Which is right?"
+
+### Update GLOSSARY.md inline
+
+When a term is resolved, update `GLOSSARY.md` right there. Don't batch these up: capture them as they happen. Use the format in [GLOSSARY-FORMAT.md](./GLOSSARY-FORMAT.md).
+
+`GLOSSARY.md` should be totally devoid of implementation details. Do not treat `GLOSSARY.md` as a spec, a scratch pad, or a repository for implementation decisions. It is a glossary and nothing else.
+
+### Offer ADRs sparingly
+
+Only offer to create an ADR when all three are true:
+
+1. **Hard to reverse**: the cost of changing your mind later is meaningful
+2. **Surprising without context**: a future reader will wonder "why did they do it this way?"
+3. **The result of a real trade-off**: there were genuine alternatives and you picked one for specific reasons
+
+If any of the three is missing, skip the ADR. Use the format in [ADR-FORMAT.md](./ADR-FORMAT.md).
diff --git a/skills/domain-modeling/agents/openai.yaml b/skills/domain-modeling/agents/openai.yaml
new file mode 100644
index 0000000..54b3f98
--- /dev/null
+++ b/skills/domain-modeling/agents/openai.yaml
@@ -0,0 +1,3 @@
+interface:
+  display_name: Domain Modeling
+  short_description: Build and sharpen a domain model
diff --git a/skills/research/SKILL.md b/skills/research/SKILL.md
new file mode 100644
index 0000000..fecee97
--- /dev/null
+++ b/skills/research/SKILL.md
@@ -0,0 +1,12 @@
+---
+name: research
+description: Investigate a question against high-trust primary sources and capture the findings as a Markdown file in the repo. Use when the user wants a topic researched, docs or API facts gathered, or reading legwork delegated to a background agent.
+---
+
+Spin up a **background agent** to do the research, so you keep working while it reads.
+
+Its job:
+
+1. Investigate the question against **primary sources** (official docs, source code, specs, first-party APIs), not a secondary write-up of them. Follow every claim back to the source that owns it.
+2. Write the findings to a single Markdown file, citing each claim's source.
+3. Save it where the repo already keeps such notes; match the existing convention, and if there is none, put it somewhere sensible and say where.
diff --git a/skills/research/agents/openai.yaml b/skills/research/agents/openai.yaml
new file mode 100644
index 0000000..b261669
--- /dev/null
+++ b/skills/research/agents/openai.yaml
@@ -0,0 +1,3 @@
+interface:
+  display_name: Research
+  short_description: Research from high-trust sources
diff --git a/skills/slack-gif-creator/LICENSE.txt b/skills/slack-gif-creator/LICENSE.txt
new file mode 100644
index 0000000..4f881c5
--- /dev/null
+++ b/skills/slack-gif-creator/LICENSE.txt
@@ -0,0 +1,202 @@
+
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diff --git a/skills/slack-gif-creator/SKILL.md b/skills/slack-gif-creator/SKILL.md
new file mode 100644
index 0000000..16660d8
--- /dev/null
+++ b/skills/slack-gif-creator/SKILL.md
@@ -0,0 +1,254 @@
+---
+name: slack-gif-creator
+description: Knowledge and utilities for creating animated GIFs optimized for Slack. Provides constraints, validation tools, and animation concepts. Use when users request animated GIFs for Slack like "make me a GIF of X doing Y for Slack."
+license: Complete terms in LICENSE.txt
+---
+
+# Slack GIF Creator
+
+A toolkit providing utilities and knowledge for creating animated GIFs optimized for Slack.
+
+## Slack Requirements
+
+**Dimensions:**
+- Emoji GIFs: 128x128 (recommended)
+- Message GIFs: 480x480
+
+**Parameters:**
+- FPS: 10-30 (lower is smaller file size)
+- Colors: 48-128 (fewer = smaller file size)
+- Duration: Keep under 3 seconds for emoji GIFs
+
+## Core Workflow
+
+```python
+from core.gif_builder import GIFBuilder
+from PIL import Image, ImageDraw
+
+# 1. Create builder
+builder = GIFBuilder(width=128, height=128, fps=10)
+
+# 2. Generate frames
+for i in range(12):
+    frame = Image.new('RGB', (128, 128), (240, 248, 255))
+    draw = ImageDraw.Draw(frame)
+
+    # Draw your animation using PIL primitives
+    # (circles, polygons, lines, etc.)
+
+    builder.add_frame(frame)
+
+# 3. Save with optimization
+builder.save('output.gif', num_colors=48, optimize_for_emoji=True)
+```
+
+## Drawing Graphics
+
+### Working with User-Uploaded Images
+If a user uploads an image, consider whether they want to:
+- **Use it directly** (e.g., "animate this", "split this into frames")
+- **Use it as inspiration** (e.g., "make something like this")
+
+Load and work with images using PIL:
+```python
+from PIL import Image
+
+uploaded = Image.open('file.png')
+# Use directly, or just as reference for colors/style
+```
+
+### Drawing from Scratch
+When drawing graphics from scratch, use PIL ImageDraw primitives:
+
+```python
+from PIL import ImageDraw
+
+draw = ImageDraw.Draw(frame)
+
+# Circles/ovals
+draw.ellipse([x1, y1, x2, y2], fill=(r, g, b), outline=(r, g, b), width=3)
+
+# Stars, triangles, any polygon
+points = [(x1, y1), (x2, y2), (x3, y3), ...]
+draw.polygon(points, fill=(r, g, b), outline=(r, g, b), width=3)
+
+# Lines
+draw.line([(x1, y1), (x2, y2)], fill=(r, g, b), width=5)
+
+# Rectangles
+draw.rectangle([x1, y1, x2, y2], fill=(r, g, b), outline=(r, g, b), width=3)
+```
+
+**Don't use:** Emoji fonts (unreliable across platforms) or assume pre-packaged graphics exist in this skill.
+
+### Making Graphics Look Good
+
+Graphics should look polished and creative, not basic. Here's how:
+
+**Use thicker lines** - Always set `width=2` or higher for outlines and lines. Thin lines (width=1) look choppy and amateurish.
+
+**Add visual depth**:
+- Use gradients for backgrounds (`create_gradient_background`)
+- Layer multiple shapes for complexity (e.g., a star with a smaller star inside)
+
+**Make shapes more interesting**:
+- Don't just draw a plain circle - add highlights, rings, or patterns
+- Stars can have glows (draw larger, semi-transparent versions behind)
+- Combine multiple shapes (stars + sparkles, circles + rings)
+
+**Pay attention to colors**:
+- Use vibrant, complementary colors
+- Add contrast (dark outlines on light shapes, light outlines on dark shapes)
+- Consider the overall composition
+
+**For complex shapes** (hearts, snowflakes, etc.):
+- Use combinations of polygons and ellipses
+- Calculate points carefully for symmetry
+- Add details (a heart can have a highlight curve, snowflakes have intricate branches)
+
+Be creative and detailed! A good Slack GIF should look polished, not like placeholder graphics.
+
+## Available Utilities
+
+### GIFBuilder (`core.gif_builder`)
+Assembles frames and optimizes for Slack:
+```python
+builder = GIFBuilder(width=128, height=128, fps=10)
+builder.add_frame(frame)  # Add PIL Image
+builder.add_frames(frames)  # Add list of frames
+builder.save('out.gif', num_colors=48, optimize_for_emoji=True, remove_duplicates=True)
+```
+
+### Validators (`core.validators`)
+Check if GIF meets Slack requirements:
+```python
+from core.validators import validate_gif, is_slack_ready
+
+# Detailed validation
+passes, info = validate_gif('my.gif', is_emoji=True, verbose=True)
+
+# Quick check
+if is_slack_ready('my.gif'):
+    print("Ready!")
+```
+
+### Easing Functions (`core.easing`)
+Smooth motion instead of linear:
+```python
+from core.easing import interpolate
+
+# Progress from 0.0 to 1.0
+t = i / (num_frames - 1)
+
+# Apply easing
+y = interpolate(start=0, end=400, t=t, easing='ease_out')
+
+# Available: linear, ease_in, ease_out, ease_in_out,
+#           bounce_out, elastic_out, back_out
+```
+
+### Frame Helpers (`core.frame_composer`)
+Convenience functions for common needs:
+```python
+from core.frame_composer import (
+    create_blank_frame,         # Solid color background
+    create_gradient_background,  # Vertical gradient
+    draw_circle,                # Helper for circles
+    draw_text,                  # Simple text rendering
+    draw_star                   # 5-pointed star
+)
+```
+
+## Animation Concepts
+
+### Shake/Vibrate
+Offset object position with oscillation:
+- Use `math.sin()` or `math.cos()` with frame index
+- Add small random variations for natural feel
+- Apply to x and/or y position
+
+### Pulse/Heartbeat
+Scale object size rhythmically:
+- Use `math.sin(t * frequency * 2 * math.pi)` for smooth pulse
+- For heartbeat: two quick pulses then pause (adjust sine wave)
+- Scale between 0.8 and 1.2 of base size
+
+### Bounce
+Object falls and bounces:
+- Use `interpolate()` with `easing='bounce_out'` for landing
+- Use `easing='ease_in'` for falling (accelerating)
+- Apply gravity by increasing y velocity each frame
+
+### Spin/Rotate
+Rotate object around center:
+- PIL: `image.rotate(angle, resample=Image.BICUBIC)`
+- For wobble: use sine wave for angle instead of linear
+
+### Fade In/Out
+Gradually appear or disappear:
+- Create RGBA image, adjust alpha channel
+- Or use `Image.blend(image1, image2, alpha)`
+- Fade in: alpha from 0 to 1
+- Fade out: alpha from 1 to 0
+
+### Slide
+Move object from off-screen to position:
+- Start position: outside frame bounds
+- End position: target location
+- Use `interpolate()` with `easing='ease_out'` for smooth stop
+- For overshoot: use `easing='back_out'`
+
+### Zoom
+Scale and position for zoom effect:
+- Zoom in: scale from 0.1 to 2.0, crop center
+- Zoom out: scale from 2.0 to 1.0
+- Can add motion blur for drama (PIL filter)
+
+### Explode/Particle Burst
+Create particles radiating outward:
+- Generate particles with random angles and velocities
+- Update each particle: `x += vx`, `y += vy`
+- Add gravity: `vy += gravity_constant`
+- Fade out particles over time (reduce alpha)
+
+## Optimization Strategies
+
+Only when asked to make the file size smaller, implement a few of the following methods:
+
+1. **Fewer frames** - Lower FPS (10 instead of 20) or shorter duration
+2. **Fewer colors** - `num_colors=48` instead of 128
+3. **Smaller dimensions** - 128x128 instead of 480x480
+4. **Remove duplicates** - `remove_duplicates=True` in save()
+5. **Emoji mode** - `optimize_for_emoji=True` auto-optimizes
+
+```python
+# Maximum optimization for emoji
+builder.save(
+    'emoji.gif',
+    num_colors=48,
+    optimize_for_emoji=True,
+    remove_duplicates=True
+)
+```
+
+## Philosophy
+
+This skill provides:
+- **Knowledge**: Slack's requirements and animation concepts
+- **Utilities**: GIFBuilder, validators, easing functions
+- **Flexibility**: Create the animation logic using PIL primitives
+
+It does NOT provide:
+- Rigid animation templates or pre-made functions
+- Emoji font rendering (unreliable across platforms)
+- A library of pre-packaged graphics built into the skill
+
+**Note on user uploads**: This skill doesn't include pre-built graphics, but if a user uploads an image, use PIL to load and work with it - interpret based on their request whether they want it used directly or just as inspiration.
+
+Be creative! Combine concepts (bouncing + rotating, pulsing + sliding, etc.) and use PIL's full capabilities.
+
+## Dependencies
+
+```bash
+pip install pillow imageio numpy
+```
diff --git a/skills/slack-gif-creator/core/easing.py b/skills/slack-gif-creator/core/easing.py
new file mode 100755
index 0000000..772fa83
--- /dev/null
+++ b/skills/slack-gif-creator/core/easing.py
@@ -0,0 +1,234 @@
+#!/usr/bin/env python3
+"""
+Easing Functions - Timing functions for smooth animations.
+
+Provides various easing functions for natural motion and timing.
+All functions take a value t (0.0 to 1.0) and return eased value (0.0 to 1.0).
+"""
+
+import math
+
+
+def linear(t: float) -> float:
+    """Linear interpolation (no easing)."""
+    return t
+
+
+def ease_in_quad(t: float) -> float:
+    """Quadratic ease-in (slow start, accelerating)."""
+    return t * t
+
+
+def ease_out_quad(t: float) -> float:
+    """Quadratic ease-out (fast start, decelerating)."""
+    return t * (2 - t)
+
+
+def ease_in_out_quad(t: float) -> float:
+    """Quadratic ease-in-out (slow start and end)."""
+    if t < 0.5:
+        return 2 * t * t
+    return -1 + (4 - 2 * t) * t
+
+
+def ease_in_cubic(t: float) -> float:
+    """Cubic ease-in (slow start)."""
+    return t * t * t
+
+
+def ease_out_cubic(t: float) -> float:
+    """Cubic ease-out (fast start)."""
+    return (t - 1) * (t - 1) * (t - 1) + 1
+
+
+def ease_in_out_cubic(t: float) -> float:
+    """Cubic ease-in-out."""
+    if t < 0.5:
+        return 4 * t * t * t
+    return (t - 1) * (2 * t - 2) * (2 * t - 2) + 1
+
+
+def ease_in_bounce(t: float) -> float:
+    """Bounce ease-in (bouncy start)."""
+    return 1 - ease_out_bounce(1 - t)
+
+
+def ease_out_bounce(t: float) -> float:
+    """Bounce ease-out (bouncy end)."""
+    if t < 1 / 2.75:
+        return 7.5625 * t * t
+    elif t < 2 / 2.75:
+        t -= 1.5 / 2.75
+        return 7.5625 * t * t + 0.75
+    elif t < 2.5 / 2.75:
+        t -= 2.25 / 2.75
+        return 7.5625 * t * t + 0.9375
+    else:
+        t -= 2.625 / 2.75
+        return 7.5625 * t * t + 0.984375
+
+
+def ease_in_out_bounce(t: float) -> float:
+    """Bounce ease-in-out."""
+    if t < 0.5:
+        return ease_in_bounce(t * 2) * 0.5
+    return ease_out_bounce(t * 2 - 1) * 0.5 + 0.5
+
+
+def ease_in_elastic(t: float) -> float:
+    """Elastic ease-in (spring effect)."""
+    if t == 0 or t == 1:
+        return t
+    return -math.pow(2, 10 * (t - 1)) * math.sin((t - 1.1) * 5 * math.pi)
+
+
+def ease_out_elastic(t: float) -> float:
+    """Elastic ease-out (spring effect)."""
+    if t == 0 or t == 1:
+        return t
+    return math.pow(2, -10 * t) * math.sin((t - 0.1) * 5 * math.pi) + 1
+
+
+def ease_in_out_elastic(t: float) -> float:
+    """Elastic ease-in-out."""
+    if t == 0 or t == 1:
+        return t
+    t = t * 2 - 1
+    if t < 0:
+        return -0.5 * math.pow(2, 10 * t) * math.sin((t - 0.1) * 5 * math.pi)
+    return math.pow(2, -10 * t) * math.sin((t - 0.1) * 5 * math.pi) * 0.5 + 1
+
+
+# Convenience mapping
+EASING_FUNCTIONS = {
+    "linear": linear,
+    "ease_in": ease_in_quad,
+    "ease_out": ease_out_quad,
+    "ease_in_out": ease_in_out_quad,
+    "bounce_in": ease_in_bounce,
+    "bounce_out": ease_out_bounce,
+    "bounce": ease_in_out_bounce,
+    "elastic_in": ease_in_elastic,
+    "elastic_out": ease_out_elastic,
+    "elastic": ease_in_out_elastic,
+}
+
+
+def get_easing(name: str = "linear"):
+    """Get easing function by name."""
+    return EASING_FUNCTIONS.get(name, linear)
+
+
+def interpolate(start: float, end: float, t: float, easing: str = "linear") -> float:
+    """
+    Interpolate between two values with easing.
+
+    Args:
+        start: Start value
+        end: End value
+        t: Progress from 0.0 to 1.0
+        easing: Name of easing function
+
+    Returns:
+        Interpolated value
+    """
+    ease_func = get_easing(easing)
+    eased_t = ease_func(t)
+    return start + (end - start) * eased_t
+
+
+def ease_back_in(t: float) -> float:
+    """Back ease-in (slight overshoot backward before forward motion)."""
+    c1 = 1.70158
+    c3 = c1 + 1
+    return c3 * t * t * t - c1 * t * t
+
+
+def ease_back_out(t: float) -> float:
+    """Back ease-out (overshoot forward then settle back)."""
+    c1 = 1.70158
+    c3 = c1 + 1
+    return 1 + c3 * pow(t - 1, 3) + c1 * pow(t - 1, 2)
+
+
+def ease_back_in_out(t: float) -> float:
+    """Back ease-in-out (overshoot at both ends)."""
+    c1 = 1.70158
+    c2 = c1 * 1.525
+    if t < 0.5:
+        return (pow(2 * t, 2) * ((c2 + 1) * 2 * t - c2)) / 2
+    return (pow(2 * t - 2, 2) * ((c2 + 1) * (t * 2 - 2) + c2) + 2) / 2
+
+
+def apply_squash_stretch(
+    base_scale: tuple[float, float], intensity: float, direction: str = "vertical"
+) -> tuple[float, float]:
+    """
+    Calculate squash and stretch scales for more dynamic animation.
+
+    Args:
+        base_scale: (width_scale, height_scale) base scales
+        intensity: Squash/stretch intensity (0.0-1.0)
+        direction: 'vertical', 'horizontal', or 'both'
+
+    Returns:
+        (width_scale, height_scale) with squash/stretch applied
+    """
+    width_scale, height_scale = base_scale
+
+    if direction == "vertical":
+        # Compress vertically, expand horizontally (preserve volume)
+        height_scale *= 1 - intensity * 0.5
+        width_scale *= 1 + intensity * 0.5
+    elif direction == "horizontal":
+        # Compress horizontally, expand vertically
+        width_scale *= 1 - intensity * 0.5
+        height_scale *= 1 + intensity * 0.5
+    elif direction == "both":
+        # General squash (both dimensions)
+        width_scale *= 1 - intensity * 0.3
+        height_scale *= 1 - intensity * 0.3
+
+    return (width_scale, height_scale)
+
+
+def calculate_arc_motion(
+    start: tuple[float, float], end: tuple[float, float], height: float, t: float
+) -> tuple[float, float]:
+    """
+    Calculate position along a parabolic arc (natural motion path).
+
+    Args:
+        start: (x, y) starting position
+        end: (x, y) ending position
+        height: Arc height at midpoint (positive = upward)
+        t: Progress (0.0-1.0)
+
+    Returns:
+        (x, y) position along arc
+    """
+    x1, y1 = start
+    x2, y2 = end
+
+    # Linear interpolation for x
+    x = x1 + (x2 - x1) * t
+
+    # Parabolic interpolation for y
+    # y = start + progress * (end - start) + arc_offset
+    # Arc offset peaks at t=0.5
+    arc_offset = 4 * height * t * (1 - t)
+    y = y1 + (y2 - y1) * t - arc_offset
+
+    return (x, y)
+
+
+# Add new easing functions to the convenience mapping
+EASING_FUNCTIONS.update(
+    {
+        "back_in": ease_back_in,
+        "back_out": ease_back_out,
+        "back_in_out": ease_back_in_out,
+        "anticipate": ease_back_in,  # Alias
+        "overshoot": ease_back_out,  # Alias
+    }
+)
diff --git a/skills/slack-gif-creator/core/frame_composer.py b/skills/slack-gif-creator/core/frame_composer.py
new file mode 100755
index 0000000..1afe434
--- /dev/null
+++ b/skills/slack-gif-creator/core/frame_composer.py
@@ -0,0 +1,176 @@
+#!/usr/bin/env python3
+"""
+Frame Composer - Utilities for composing visual elements into frames.
+
+Provides functions for drawing shapes, text, emojis, and compositing elements
+together to create animation frames.
+"""
+
+from typing import Optional
+
+import numpy as np
+from PIL import Image, ImageDraw, ImageFont
+
+
+def create_blank_frame(
+    width: int, height: int, color: tuple[int, int, int] = (255, 255, 255)
+) -> Image.Image:
+    """
+    Create a blank frame with solid color background.
+
+    Args:
+        width: Frame width
+        height: Frame height
+        color: RGB color tuple (default: white)
+
+    Returns:
+        PIL Image
+    """
+    return Image.new("RGB", (width, height), color)
+
+
+def draw_circle(
+    frame: Image.Image,
+    center: tuple[int, int],
+    radius: int,
+    fill_color: Optional[tuple[int, int, int]] = None,
+    outline_color: Optional[tuple[int, int, int]] = None,
+    outline_width: int = 1,
+) -> Image.Image:
+    """
+    Draw a circle on a frame.
+
+    Args:
+        frame: PIL Image to draw on
+        center: (x, y) center position
+        radius: Circle radius
+        fill_color: RGB fill color (None for no fill)
+        outline_color: RGB outline color (None for no outline)
+        outline_width: Outline width in pixels
+
+    Returns:
+        Modified frame
+    """
+    draw = ImageDraw.Draw(frame)
+    x, y = center
+    bbox = [x - radius, y - radius, x + radius, y + radius]
+    draw.ellipse(bbox, fill=fill_color, outline=outline_color, width=outline_width)
+    return frame
+
+
+def draw_text(
+    frame: Image.Image,
+    text: str,
+    position: tuple[int, int],
+    color: tuple[int, int, int] = (0, 0, 0),
+    centered: bool = False,
+) -> Image.Image:
+    """
+    Draw text on a frame.
+
+    Args:
+        frame: PIL Image to draw on
+        text: Text to draw
+        position: (x, y) position (top-left unless centered=True)
+        color: RGB text color
+        centered: If True, center text at position
+
+    Returns:
+        Modified frame
+    """
+    draw = ImageDraw.Draw(frame)
+
+    # Uses Pillow's default font.
+    # If the font should be changed for the emoji, add additional logic here.
+    font = ImageFont.load_default()
+
+    if centered:
+        bbox = draw.textbbox((0, 0), text, font=font)
+        text_width = bbox[2] - bbox[0]
+        text_height = bbox[3] - bbox[1]
+        x = position[0] - text_width // 2
+        y = position[1] - text_height // 2
+        position = (x, y)
+
+    draw.text(position, text, fill=color, font=font)
+    return frame
+
+
+def create_gradient_background(
+    width: int,
+    height: int,
+    top_color: tuple[int, int, int],
+    bottom_color: tuple[int, int, int],
+) -> Image.Image:
+    """
+    Create a vertical gradient background.
+
+    Args:
+        width: Frame width
+        height: Frame height
+        top_color: RGB color at top
+        bottom_color: RGB color at bottom
+
+    Returns:
+        PIL Image with gradient
+    """
+    frame = Image.new("RGB", (width, height))
+    draw = ImageDraw.Draw(frame)
+
+    # Calculate color step for each row
+    r1, g1, b1 = top_color
+    r2, g2, b2 = bottom_color
+
+    for y in range(height):
+        # Interpolate color
+        ratio = y / height
+        r = int(r1 * (1 - ratio) + r2 * ratio)
+        g = int(g1 * (1 - ratio) + g2 * ratio)
+        b = int(b1 * (1 - ratio) + b2 * ratio)
+
+        # Draw horizontal line
+        draw.line([(0, y), (width, y)], fill=(r, g, b))
+
+    return frame
+
+
+def draw_star(
+    frame: Image.Image,
+    center: tuple[int, int],
+    size: int,
+    fill_color: tuple[int, int, int],
+    outline_color: Optional[tuple[int, int, int]] = None,
+    outline_width: int = 1,
+) -> Image.Image:
+    """
+    Draw a 5-pointed star.
+
+    Args:
+        frame: PIL Image to draw on
+        center: (x, y) center position
+        size: Star size (outer radius)
+        fill_color: RGB fill color
+        outline_color: RGB outline color (None for no outline)
+        outline_width: Outline width
+
+    Returns:
+        Modified frame
+    """
+    import math
+
+    draw = ImageDraw.Draw(frame)
+    x, y = center
+
+    # Calculate star points
+    points = []
+    for i in range(10):
+        angle = (i * 36 - 90) * math.pi / 180  # 36 degrees per point, start at top
+        radius = size if i % 2 == 0 else size * 0.4  # Alternate between outer and inner
+        px = x + radius * math.cos(angle)
+        py = y + radius * math.sin(angle)
+        points.append((px, py))
+
+    # Draw star
+    draw.polygon(points, fill=fill_color, outline=outline_color, width=outline_width)
+
+    return frame
diff --git a/skills/slack-gif-creator/core/gif_builder.py b/skills/slack-gif-creator/core/gif_builder.py
new file mode 100755
index 0000000..5759f14
--- /dev/null
+++ b/skills/slack-gif-creator/core/gif_builder.py
@@ -0,0 +1,269 @@
+#!/usr/bin/env python3
+"""
+GIF Builder - Core module for assembling frames into GIFs optimized for Slack.
+
+This module provides the main interface for creating GIFs from programmatically
+generated frames, with automatic optimization for Slack's requirements.
+"""
+
+from pathlib import Path
+from typing import Optional
+
+import imageio.v3 as imageio
+import numpy as np
+from PIL import Image
+
+
+class GIFBuilder:
+    """Builder for creating optimized GIFs from frames."""
+
+    def __init__(self, width: int = 480, height: int = 480, fps: int = 15):
+        """
+        Initialize GIF builder.
+
+        Args:
+            width: Frame width in pixels
+            height: Frame height in pixels
+            fps: Frames per second
+        """
+        self.width = width
+        self.height = height
+        self.fps = fps
+        self.frames: list[np.ndarray] = []
+
+    def add_frame(self, frame: np.ndarray | Image.Image):
+        """
+        Add a frame to the GIF.
+
+        Args:
+            frame: Frame as numpy array or PIL Image (will be converted to RGB)
+        """
+        if isinstance(frame, Image.Image):
+            frame = np.array(frame.convert("RGB"))
+
+        # Ensure frame is correct size
+        if frame.shape[:2] != (self.height, self.width):
+            pil_frame = Image.fromarray(frame)
+            pil_frame = pil_frame.resize(
+                (self.width, self.height), Image.Resampling.LANCZOS
+            )
+            frame = np.array(pil_frame)
+
+        self.frames.append(frame)
+
+    def add_frames(self, frames: list[np.ndarray | Image.Image]):
+        """Add multiple frames at once."""
+        for frame in frames:
+            self.add_frame(frame)
+
+    def optimize_colors(
+        self, num_colors: int = 128, use_global_palette: bool = True
+    ) -> list[np.ndarray]:
+        """
+        Reduce colors in all frames using quantization.
+
+        Args:
+            num_colors: Target number of colors (8-256)
+            use_global_palette: Use a single palette for all frames (better compression)
+
+        Returns:
+            List of color-optimized frames
+        """
+        optimized = []
+
+        if use_global_palette and len(self.frames) > 1:
+            # Create a global palette from all frames
+            # Sample frames to build palette
+            sample_size = min(5, len(self.frames))
+            sample_indices = [
+                int(i * len(self.frames) / sample_size) for i in range(sample_size)
+            ]
+            sample_frames = [self.frames[i] for i in sample_indices]
+
+            # Combine sample frames into a single image for palette generation
+            # Flatten each frame to get all pixels, then stack them
+            all_pixels = np.vstack(
+                [f.reshape(-1, 3) for f in sample_frames]
+            )  # (total_pixels, 3)
+
+            # Create a properly-shaped RGB image from the pixel data
+            # We'll make a roughly square image from all the pixels
+            total_pixels = len(all_pixels)
+            width = min(512, int(np.sqrt(total_pixels)))  # Reasonable width, max 512
+            height = (total_pixels + width - 1) // width  # Ceiling division
+
+            # Pad if necessary to fill the rectangle
+            pixels_needed = width * height
+            if pixels_needed > total_pixels:
+                padding = np.zeros((pixels_needed - total_pixels, 3), dtype=np.uint8)
+                all_pixels = np.vstack([all_pixels, padding])
+
+            # Reshape to proper RGB image format (H, W, 3)
+            img_array = (
+                all_pixels[:pixels_needed].reshape(height, width, 3).astype(np.uint8)
+            )
+            combined_img = Image.fromarray(img_array, mode="RGB")
+
+            # Generate global palette
+            global_palette = combined_img.quantize(colors=num_colors, method=2)
+
+            # Apply global palette to all frames
+            for frame in self.frames:
+                pil_frame = Image.fromarray(frame)
+                quantized = pil_frame.quantize(palette=global_palette, dither=1)
+                optimized.append(np.array(quantized.convert("RGB")))
+        else:
+            # Use per-frame quantization
+            for frame in self.frames:
+                pil_frame = Image.fromarray(frame)
+                quantized = pil_frame.quantize(colors=num_colors, method=2, dither=1)
+                optimized.append(np.array(quantized.convert("RGB")))
+
+        return optimized
+
+    def deduplicate_frames(self, threshold: float = 0.9995) -> int:
+        """
+        Remove duplicate or near-duplicate consecutive frames.
+
+        Args:
+            threshold: Similarity threshold (0.0-1.0). Higher = more strict (0.9995 = nearly identical).
+                      Use 0.9995+ to preserve subtle animations, 0.98 for aggressive removal.
+
+        Returns:
+            Number of frames removed
+        """
+        if len(self.frames) < 2:
+            return 0
+
+        deduplicated = [self.frames[0]]
+        removed_count = 0
+
+        for i in range(1, len(self.frames)):
+            # Compare with previous frame
+            prev_frame = np.array(deduplicated[-1], dtype=np.float32)
+            curr_frame = np.array(self.frames[i], dtype=np.float32)
+
+            # Calculate similarity (normalized)
+            diff = np.abs(prev_frame - curr_frame)
+            similarity = 1.0 - (np.mean(diff) / 255.0)
+
+            # Keep frame if sufficiently different
+            # High threshold (0.9995+) means only remove nearly identical frames
+            if similarity < threshold:
+                deduplicated.append(self.frames[i])
+            else:
+                removed_count += 1
+
+        self.frames = deduplicated
+        return removed_count
+
+    def save(
+        self,
+        output_path: str | Path,
+        num_colors: int = 128,
+        optimize_for_emoji: bool = False,
+        remove_duplicates: bool = False,
+    ) -> dict:
+        """
+        Save frames as optimized GIF for Slack.
+
+        Args:
+            output_path: Where to save the GIF
+            num_colors: Number of colors to use (fewer = smaller file)
+            optimize_for_emoji: If True, optimize for emoji size (128x128, fewer colors)
+            remove_duplicates: If True, remove duplicate consecutive frames (opt-in)
+
+        Returns:
+            Dictionary with file info (path, size, dimensions, frame_count)
+        """
+        if not self.frames:
+            raise ValueError("No frames to save. Add frames with add_frame() first.")
+
+        output_path = Path(output_path)
+
+        # Remove duplicate frames to reduce file size
+        if remove_duplicates:
+            removed = self.deduplicate_frames(threshold=0.9995)
+            if removed > 0:
+                print(
+                    f"  Removed {removed} nearly identical frames (preserved subtle animations)"
+                )
+
+        # Optimize for emoji if requested
+        if optimize_for_emoji:
+            if self.width > 128 or self.height > 128:
+                print(
+                    f"  Resizing from {self.width}x{self.height} to 128x128 for emoji"
+                )
+                self.width = 128
+                self.height = 128
+                # Resize all frames
+                resized_frames = []
+                for frame in self.frames:
+                    pil_frame = Image.fromarray(frame)
+                    pil_frame = pil_frame.resize((128, 128), Image.Resampling.LANCZOS)
+                    resized_frames.append(np.array(pil_frame))
+                self.frames = resized_frames
+            num_colors = min(num_colors, 48)  # More aggressive color limit for emoji
+
+            # More aggressive FPS reduction for emoji
+            if len(self.frames) > 12:
+                print(
+                    f"  Reducing frames from {len(self.frames)} to ~12 for emoji size"
+                )
+                # Keep every nth frame to get close to 12 frames
+                keep_every = max(1, len(self.frames) // 12)
+                self.frames = [
+                    self.frames[i] for i in range(0, len(self.frames), keep_every)
+                ]
+
+        # Optimize colors with global palette
+        optimized_frames = self.optimize_colors(num_colors, use_global_palette=True)
+
+        # Calculate frame duration in milliseconds
+        frame_duration = 1000 / self.fps
+
+        # Save GIF
+        imageio.imwrite(
+            output_path,
+            optimized_frames,
+            duration=frame_duration,
+            loop=0,  # Infinite loop
+        )
+
+        # Get file info
+        file_size_kb = output_path.stat().st_size / 1024
+        file_size_mb = file_size_kb / 1024
+
+        info = {
+            "path": str(output_path),
+            "size_kb": file_size_kb,
+            "size_mb": file_size_mb,
+            "dimensions": f"{self.width}x{self.height}",
+            "frame_count": len(optimized_frames),
+            "fps": self.fps,
+            "duration_seconds": len(optimized_frames) / self.fps,
+            "colors": num_colors,
+        }
+
+        # Print info
+        print(f"\n✓ GIF created successfully!")
+        print(f"  Path: {output_path}")
+        print(f"  Size: {file_size_kb:.1f} KB ({file_size_mb:.2f} MB)")
+        print(f"  Dimensions: {self.width}x{self.height}")
+        print(f"  Frames: {len(optimized_frames)} @ {self.fps} fps")
+        print(f"  Duration: {info['duration_seconds']:.1f}s")
+        print(f"  Colors: {num_colors}")
+
+        # Size info
+        if optimize_for_emoji:
+            print(f"  Optimized for emoji (128x128, reduced colors)")
+        if file_size_mb > 1.0:
+            print(f"\n  Note: Large file size ({file_size_kb:.1f} KB)")
+            print("  Consider: fewer frames, smaller dimensions, or fewer colors")
+
+        return info
+
+    def clear(self):
+        """Clear all frames (useful for creating multiple GIFs)."""
+        self.frames = []
diff --git a/skills/slack-gif-creator/core/validators.py b/skills/slack-gif-creator/core/validators.py
new file mode 100755
index 0000000..a6f5bdf
--- /dev/null
+++ b/skills/slack-gif-creator/core/validators.py
@@ -0,0 +1,136 @@
+#!/usr/bin/env python3
+"""
+Validators - Check if GIFs meet Slack's requirements.
+
+These validators help ensure your GIFs meet Slack's size and dimension constraints.
+"""
+
+from pathlib import Path
+
+
+def validate_gif(
+    gif_path: str | Path, is_emoji: bool = True, verbose: bool = True
+) -> tuple[bool, dict]:
+    """
+    Validate GIF for Slack (dimensions, size, frame count).
+
+    Args:
+        gif_path: Path to GIF file
+        is_emoji: True for emoji (128x128 recommended), False for message GIF
+        verbose: Print validation details
+
+    Returns:
+        Tuple of (passes: bool, results: dict with all details)
+    """
+    from PIL import Image
+
+    gif_path = Path(gif_path)
+
+    if not gif_path.exists():
+        return False, {"error": f"File not found: {gif_path}"}
+
+    # Get file size
+    size_bytes = gif_path.stat().st_size
+    size_kb = size_bytes / 1024
+    size_mb = size_kb / 1024
+
+    # Get dimensions and frame info
+    try:
+        with Image.open(gif_path) as img:
+            width, height = img.size
+
+            # Count frames
+            frame_count = 0
+            try:
+                while True:
+                    img.seek(frame_count)
+                    frame_count += 1
+            except EOFError:
+                pass
+
+            # Get duration
+            try:
+                duration_ms = img.info.get("duration", 100)
+                total_duration = (duration_ms * frame_count) / 1000
+                fps = frame_count / total_duration if total_duration > 0 else 0
+            except:
+                total_duration = None
+                fps = None
+
+    except Exception as e:
+        return False, {"error": f"Failed to read GIF: {e}"}
+
+    # Validate dimensions
+    if is_emoji:
+        optimal = width == height == 128
+        acceptable = width == height and 64 <= width <= 128
+        dim_pass = acceptable
+    else:
+        aspect_ratio = (
+            max(width, height) / min(width, height)
+            if min(width, height) > 0
+            else float("inf")
+        )
+        dim_pass = aspect_ratio <= 2.0 and 320 <= min(width, height) <= 640
+
+    results = {
+        "file": str(gif_path),
+        "passes": dim_pass,
+        "width": width,
+        "height": height,
+        "size_kb": size_kb,
+        "size_mb": size_mb,
+        "frame_count": frame_count,
+        "duration_seconds": total_duration,
+        "fps": fps,
+        "is_emoji": is_emoji,
+        "optimal": optimal if is_emoji else None,
+    }
+
+    # Print if verbose
+    if verbose:
+        print(f"\nValidating {gif_path.name}:")
+        print(
+            f"  Dimensions: {width}x{height}"
+            + (
+                f" ({'optimal' if optimal else 'acceptable'})"
+                if is_emoji and acceptable
+                else ""
+            )
+        )
+        print(
+            f"  Size: {size_kb:.1f} KB"
+            + (f" ({size_mb:.2f} MB)" if size_mb >= 1.0 else "")
+        )
+        print(
+            f"  Frames: {frame_count}"
+            + (f" @ {fps:.1f} fps ({total_duration:.1f}s)" if fps else "")
+        )
+
+        if not dim_pass:
+            print(
+                f"  Note: {'Emoji should be 128x128' if is_emoji else 'Unusual dimensions for Slack'}"
+            )
+
+        if size_mb > 5.0:
+            print(f"  Note: Large file size - consider fewer frames/colors")
+
+    return dim_pass, results
+
+
+def is_slack_ready(
+    gif_path: str | Path, is_emoji: bool = True, verbose: bool = True
+) -> bool:
+    """
+    Quick check if GIF is ready for Slack.
+
+    Args:
+        gif_path: Path to GIF file
+        is_emoji: True for emoji GIF, False for message GIF
+        verbose: Print feedback
+
+    Returns:
+        True if dimensions are acceptable
+    """
+    passes, _ = validate_gif(gif_path, is_emoji, verbose)
+    return passes
diff --git a/skills/slack-gif-creator/requirements.txt b/skills/slack-gif-creator/requirements.txt
new file mode 100644
index 0000000..8bc4493
--- /dev/null
+++ b/skills/slack-gif-creator/requirements.txt
@@ -0,0 +1,4 @@
+pillow>=10.0.0
+imageio>=2.31.0
+imageio-ffmpeg>=0.4.9
+numpy>=1.24.0
\ No newline at end of file
diff --git a/skills/why/SKILL.md b/skills/why/SKILL.md
new file mode 100644
index 0000000..bc2a3eb
--- /dev/null
+++ b/skills/why/SKILL.md
@@ -0,0 +1,98 @@
+---
+name: why
+description: "Use for 'why does X work this way', 'why we picked Y', design rationale, regressions, postmortems, or data-backed thresholds. Discovers available MCPs and queries each evidence category (source control, issue tracker, long-form docs, real-time chat, infrastructure observability, error tracking, product analytics warehouse) in parallel, then returns a cited read on decisions and tradeoffs. Use how for runtime behavior."
+disable-model-invocation: true
+---
+
+# Why
+
+Investigate the motivation and intent behind code. The companion `how` skill answers what the code does and how it works. `why` answers what forces led to its shape.
+
+Each spawn below names a role line in the `pstack-models.mdc` rule and a default. Set `model` to that line's value, or to the default if the rule or the line is missing. Leave `model` unset when the value is `auto` or `inherit-parent`. If the Task tool rejects a slug, use the default and say so. If it rejects the default, use the closest valid slug of the same family from its error message.
+
+Every spawn is `generalPurpose` with `readonly: false` (agent mode). **Do not use readonly/Ask mode.** It strips MCP access, which disables the MCP-backed investigators and the synthesizer's citation spot-checks. Investigators still shouldn't write anything.
+
+## Operating Posture
+
+Operate as a **careful, cautious, and precise investigator**. Be honest about what you know vs what you're inferring. Read `references/epistemics.md` for the full confidence framework and phrasing guide. The synthesizer must follow it.
+
+## Step 1. Understand the Target and the Question
+
+The **target** is usually a chunk of code, a pattern, a feature, or a named design decision. The **question** is usually a design rationale, a tradeoff, a motivating edge case, an external constraint, dead code, or a broad history sweep.
+
+If the target is vague ("why do we do it this way?" with no clear referent), make your best guess from conversation context (open files, recent edits, cursor location, what was just discussed). State your interpretation briefly so the user can redirect if you're off, then proceed.
+
+## Step 2. Establish the Code Anchor
+
+Before spawning investigators, build the seed context inline: file paths and line ranges, key symbols (functions, classes, constants), the last few commits touching the target, PR numbers from merge commits (pattern `(#1234)` in the subject line), and any linked ticket IDs. Run `git blame -L <start>,<end> <file>` for last-touch commits, `git log --follow -p -- <file>` for full history with patches through renames, `git log --oneline -20 -- <file>` for recent commits with PR numbers visible, and `git log -1 --format=%B <commit>` to extract PR numbers. Then pull the PR bodies and discussion for any substantive commit: `gh pr view <number> --json title,body,author,createdAt,mergedAt,labels,closingIssuesReferences,comments,reviews`. This anchor goes to every investigator and to the synthesizer.
+
+## Step 3. Spawn Parallel Investigators
+
+**Default to the full parallel investigation.** The goal is a complete coverage map, not a minimal one. Document the null, don't skip the search.
+
+### Discovery
+
+Before spawning investigators, list the available MCPs from the Cursor environment. Use the available-tools map when present. Otherwise inspect the `mcps/` directory Cursor exposes for enabled MCP servers.
+
+Map each available MCP to one evidence category:
+
+1. Source control history
+2. Issue / ticket tracker
+3. Long-form documents
+4. Real-time team chat
+5. Infrastructure observability
+6. Error / exception tracking
+7. Product analytics warehouse
+
+Source control is always available through git and `gh`. For the other six, classify using the MCP name, server instructions, tool names, and resource descriptors. If an MCP could fit more than one category, choose the one matching its primary evidence. Record ambiguous cases in the coverage map.
+
+Spawn one investigator per category that has a matching MCP, all in a single message so they run concurrently, each owning exactly one tool or MCP. Don't ask one agent to cover multiple MCPs. `model`: the `why investigators` line, default `grok-4.7-xhigh-fast`.
+
+Each investigator's prompt is `references/investigator-prompt.md` filled in, plus:
+- the category playbook `references/sources/<source>.md` for the selected MCP, adapted from the examples in `references/source-playbook.md`
+- `references/sources/incident-postmortem.md` **if the target code looks defensive** (null checks, retry logic, timeout handling, rate limiting, feature flags, egress guards, OOM handlers)
+- the code anchor and the user's original question
+
+### Investigator roster, one per available evidence category
+
+Each category below is uniquely positioned to surface one shape of "why", shown after "Best at". Use it to know what to expect back, to name a gap when a category returns empty, and, only in the rare provably-irrelevant case, to justify a skip.
+
+1. **Source control** (git history, `gh` for PRs, code comments, tests). Always spawn, the only guaranteed source. Best at *implementation-time rationale captured during review*.
+2. **Issue / ticket tracker** (e.g. Linear, Jira, GitHub Issues, Plane, Shortcut MCP). Best at *the product or business forcing function*. Strongest when the why is external to engineering.
+3. **Long-form documents** (e.g. Notion, Confluence, Google Docs, Coda MCP). Best at *long-form design rationale*, written out before it becomes code.
+4. **Real-time team chat** (e.g. Slack, Discord, Microsoft Teams, Mattermost MCP). Best at *real-time deliberation that never reached a doc*. Especially important when the source control, ticket, and doc paper trail is thin.
+5. **Infrastructure observability** (e.g. Datadog, New Relic, Honeycomb, Grafana, Splunk MCP). Best at *infrastructure and runtime reality that motivated the code*. Strongest when the target reacts to an infra signal (timeouts, retries, rate limits, circuit breakers).
+6. **Error / exception tracking** (e.g. Sentry, Rollbar, Bugsnag, Airbrake MCP). Best at *the specific exceptions and error trajectories that motivated defensive or corrective code*. Strongest for catch blocks, null guards, type checks, retries, and other defenses.
+7. **Product analytics warehouse** (e.g. Databricks, Snowflake, BigQuery, ClickHouse, dbt, Redshift MCP). Best at *product and data reality that shaped the code*. Strongest for flag-gated code, experiment-driven ships, data migrations, and "where did this number come from" questions.
+
+### When to skip an investigator
+
+Only with an **explicit, written justification** that goes in the final Sources Consulted. Two valid reasons:
+
+- **No MCP is available for that category** in this environment. Flag it as a gap, not a choice: "Real-time team chat skipped. No matching MCP available, so the conversational record was not searchable."
+- **The source is provably irrelevant**, not just "probably irrelevant." A high bar: "Error / exception tracking skipped. Target is a build-time script with no runtime code path."
+
+If your scope assessment suggests a single-commit trivial target where the PR description already contains the complete answer, you may answer inline **only after** confirming all seven available category searches would be redundant. Say so explicitly. This should be rare.
+
+## Step 4. Synthesize
+
+Spawn one synthesizer. `model`: the `why synthesizer` line, default `claude-opus-5-5-xhigh`. It gets:
+1. The investigator findings, including any null results and any categories skipped with justification
+2. The code anchor
+3. The user's original question
+4. The epistemics framework from `references/epistemics.md`
+5. The synthesizer prompt template from `references/synthesizer-prompt.md`
+
+## Step 5. Present
+
+Present the synthesizer's output to the user. You may lightly edit for clarity or add context from the conversation, but **do not rewrite the confidence language**.
+
+## Output Format
+
+The structure is the one in `references/synthesizer-prompt.md`: The Question, The Code in Question, What We Found, What We Can Reasonably Infer, Competing Hypotheses, What We Don't Know, Sources Consulted, Confidence Summary. Adapt as needed, but keep the confidence separation intact, and keep Sources Consulted as one line per investigator, including the ones that returned nothing or were skipped, with the reason.
+
+After Sources Consulted, if the user's `why` question is a precursor to changing this code, convert the lineage findings into a Preserve / Change / Avoid / Risk constraint set for planning the change.
+
+## Common Failure Modes to Avoid
+
+- **Recency bias**. Assuming the most recent commit is authoritative. The current shape is often the accretion of many earlier decisions. Trace back.
diff --git a/skills/why/references/epistemics.md b/skills/why/references/epistemics.md
new file mode 100644
index 0000000..b18abf4
--- /dev/null
+++ b/skills/why/references/epistemics.md
@@ -0,0 +1,71 @@
+# Epistemics
+
+How to reason about confidence when evidence is historical, fragmentary, and sometimes contradictory, and how to tell the user without flattening it into false certainty. Code shows what it does, not *why it exists*. The why lives in commits, PRs, tickets, docs, and conversations, all incomplete, biased, and sometimes missing. Pretending otherwise produces confident-sounding guesses that mislead the user.
+
+## Confidence Tiers
+
+Every claim in the output must sit in one tier. The tier sets its section and its phrasing.
+
+### 1. Direct
+
+An explicit, textual citation answers the question: something an author actually *wrote* that says why, not "the code does X so the author must have wanted X." For example, a PR description "this fixes the bug where users with >1000 items couldn't paginate", a ticket "we're adding this because customer Acme requested it in their security review", a code comment "// clamp to 100 because the upstream API rejects larger values", a design doc "we chose option A over option B because we need persistence across restarts", or a chat message from the author "switching to this approach since the old one was flaky in tests".
+
+Phrasing: confident, present tense, cited. "This exists because X."
+
+### 2. Supported
+
+Multiple pieces of indirect evidence converge on one conclusion. No single source states it explicitly, but the pattern across sources makes it likely. For example, the PR title says "improve performance", the ticket is labeled "perf", and the surrounding commits all touch the same hot path. Or several tests added with the change all exercise very large inputs. Or the author's other PRs from the same week all name the same incident.
+
+Phrasing: confident but clearly derived, citing each item. "The evidence points strongly to X: [the specific pieces]."
+
+### 3. Inferred
+
+A reasonable reading of the context that nothing explicitly supports. It is *your interpretation*, not a fact from the record. For example, the PR doesn't say why, but the error was happening in production (per the incident channel timing) and the fix was rushed (merged the same day), so it was likely a hotfix. Or the function name suggests retry logic, and its retry count of 3 matches the team's 3-retry convention elsewhere in the codebase.
+
+Phrasing: hedged, with the chain explicit. "Given A and B, C seems likely because D."
+
+### 4. Speculative
+
+A plausible hypothesis on thin evidence, where other explanations fit equally well. Worth presenting, since the user may know which is right, but mark it as a guess. For example, "This might be a workaround for a browser bug that's since been fixed, but we found no contemporary evidence of that", or "It's possible this threshold was chosen to match an SLA commitment, but no SLA doc references it."
+
+Phrasing: "One possibility is X, but we have no direct evidence." Usually lives in Competing Hypotheses, alongside other possibilities.
+
+### 5. Unknown
+
+You looked and couldn't find out. A valid, important outcome. Document it.
+
+Phrasing: "We searched X, Y, and Z and found no evidence of why." Be specific about *what* you searched. "We couldn't find out" is less useful than "We searched the ticket tracker with keywords A and B, scanned the 6 PRs that touched this file since 2023, and grep'd the repo for string literals matching the threshold. None surfaced a rationale."
+
+## Phrasing Guide
+
+**Confidence words** imply Direct or Supported evidence and need a citation immediately adjacent. Never use them for inferences: "because", "the reason is", "was designed to" (claims author intent), "fixes" / "addresses" / "solves" (claims the change achieved its goal), "the team decided" (claims a group decision happened).
+
+**Hedges** signal interpretation, not report. Use them liberally in What We Can Reasonably Infer: "appears to", "seems to", "likely", "suggests", "is consistent with", "one reading is", "plausibly", "may have been", "the evidence points toward".
+
+**Words to avoid:**
+
+- "obviously". If it were obvious, the user wouldn't be asking
+- "clearly". Almost always precedes a claim that isn't clear
+- "of course". Same
+- "just" (as in "it's just X for performance"). Dismissive and usually hides uncertainty
+- "I think" / "I believe". You're synthesizing evidence, not giving a personal opinion. Use "the evidence suggests" instead.
+
+## Avoid Rationalization
+
+Code that "makes sense" today may have been written for reasons that no longer apply, or that were wrong at the time. Don't retrofit a clean rationale onto messy history. Don't assume the author did the "right" thing and work backward to justify it. Don't assume a consistent pattern was intentional when it might be copy-paste. Don't turn an absence of evidence into evidence of absence ("no one mentioned security concerns, so it must not have been a concern").
+
+## The Sycophancy Trap
+
+Users often embed a hypothesis: "Why do we do it this way, I assume it's for performance?" Don't simply confirm it. The guess is a prompt for investigation, not a conclusion to validate. Treat it as one candidate among others and check the evidence independently. If the evidence supports it, say so with citations. If not, say so, and present what the evidence *does* support.
+
+## When Evidence Contradicts
+
+Surface both sources in the output, with citations. Don't pick the one that fits a tidier narrative. For example, the ticket says "we need this for customer X's compliance requirement" and the PR says "cleaning up tech debt in this area". Both may be true (the ticket motivated the work, the PR gave the author's framing), or one may be wrong. Let the user make the call.
+
+## When Evidence Is Missing
+
+An honest "we don't know" is one of the most valuable outputs. The user learns the answer isn't in the obvious places, that they'll need to ask a human (the original author, the product owner, the team lead), or that they can decide the question isn't worth pursuing further. Failing to mark a gap, and instead filling it with a confident guess, actively harms the user, because they'll act on the guess. Name each gap concretely: the question, the sources searched, what you searched for in each, and what you found (nothing, or only tangential material).
+
+## Before Finalizing
+
+Before the synthesizer delivers the output, it checks every claim in "What We Found" and "What We Can Reasonably Infer". Each claim has a citation or moves to Inferred or Hypotheses. Its phrasing matches its tier. It doesn't treat code as evidence for its own intent (if it does, remove or reclassify it). The output has a "What We Don't Know" section. If no gaps are mentioned, that's suspicious. Either the evidence was unusually complete or something is being swept under the rug.
diff --git a/skills/why/references/investigator-prompt.md b/skills/why/references/investigator-prompt.md
new file mode 100644
index 0000000..e1c2bbf
--- /dev/null
+++ b/skills/why/references/investigator-prompt.md
@@ -0,0 +1,84 @@
+# Investigator Prompt Template
+
+Fill in the placeholders. Append the one category playbook `sources/<source>.md` for this investigator's evidence category (index: `source-playbook.md`). If the target code looks defensive (null checks, retry logic, timeout handling, rate limiting, feature flags, egress guards, OOM handlers), also append `sources/incident-postmortem.md` so the investigator runs incident-flavored queries inside its own source.
+
+---
+
+You are investigating the historical context and motivation behind a piece of code. Other investigators search other sources in parallel, and a separate synthesizer combines all findings into the final answer. Go deep on your assigned source only. Gather evidence accurately rather than writing prose.
+
+Work like a **careful, cautious, and precise investigator**. Surface evidence and describe it accurately, including the parts that don't fit a tidy story. Boring and exact beats plausible. One verbatim quote with a precise citation is worth more than a paragraph of summary.
+
+## The Question
+
+> {QUESTION}
+
+## The Code Anchor
+
+**Target files:** {FILES_WITH_LINE_RANGES}
+
+**Key symbols:** {SYMBOLS}
+
+**Initial commits touching this code (most recent first):**
+{COMMIT_LIST}
+
+**PR numbers extracted from commit messages:** {PR_NUMBERS}
+
+**Ticket IDs mentioned in commits or PR bodies (if any):** {TICKET_IDS}
+
+## Your Assigned Source
+
+{SOURCE_NAME}
+
+{SOURCE_PLAYBOOK_SECTION}
+
+## Investigation Loop
+
+1. **Cast a wide net first** so you don't miss related context. Only then narrow in on specific items.
+2. **Read the whole thing.** Read each PR, ticket, doc, or thread fully, not just the title or summary. The key evidence is often in a comment, a subtask, or a follow-up.
+3. **Follow links within your source only.** Pull the PR or commit a PR references, the parent or sibling of a ticket, the doc a doc links. Do NOT chase a cross-source reference. Record it under Additional Leads for that source's investigator. The one-investigator-per-category design depends on this. Chasing it duplicates work and confuses scope.
+4. **Quote verbatim**, with the exact location (PR number, ticket ID, URL, commit hash, file:line), so the reader can confirm the claim in seconds.
+5. **Record what you searched, not just what you found.** Write queries verbatim. An empty search is a finding.
+6. **Resist the story.** If two items in your source disagree, record both. Don't suppress the inconvenient one. If three items line up and a fourth contradicts them, the contradiction is the most interesting finding.
+
+Before reporting a finding as strong, ask whether you would expect to find it if your current reading were wrong, and how the evidence would differ. **Never invent.** If you're tempted to round a partial finding up into a confident statement, stop and label it partial. The synthesizer counts on your output being accurate.
+
+## Epistemic Discipline
+
+- **Mechanics are not motivation.** A commit changing `limit = 50` to `limit = 100` shows the change, not necessarily why. Look for the reason in the commit message, PR description, linked ticket, or review comments.
+- **No intent from code behavior or style.** You may read code to learn what the target *is*. "The author chose a functional approach" is an observation about code, not evidence of intent. Claim intent only where the author stated it.
+- **Preserve uncertainty.** Say when evidence is ambiguous, or when one reading is more plausible but not certain. Don't collapse ambiguity to look decisive.
+- **No silent substitutions.** If the question is about feature X and you only find evidence about feature Y, don't present Y's evidence as if it answers X.
+- **Gather, don't conclude.** Collect the raw material honestly and completely. Don't answer the question, form a final opinion on "the why", or pick sides in contradictions. Don't speculate beyond what the evidence supports. A hunch with no evidence isn't evidence. The synthesizer does the reasoning.
+
+## Output Format
+
+Return your findings in this structure. The synthesizer reads it directly.
+
+### Source
+Which source you investigated (source control, issue / ticket tracker, long-form documents, real-time team chat, infrastructure observability, error / exception tracking, product analytics warehouse, code comments, etc.).
+
+### What I Searched
+The queries you ran, items you opened, and places you looked. Be specific. This shows how thorough the search was and what is still unsearched.
+
+### Direct Evidence Found
+For each item that explicitly addresses the question:
+- **What it says**: verbatim quote or accurate paraphrase
+- **Where it's from**: PR #123, ticket ID, doc URL, chat permalink, commit hash, or file:line
+- **Author and date** (if available)
+- **Relevance**: one sentence on how it bears on the question
+
+### Indirect / Circumstantial Evidence
+For each item that bears on the question without answering it:
+- **What it is**: brief description
+- **Where it's from**: location
+- **What it suggests**: what a careful reader might infer, and the inference chain
+- **Alternative readings**: if the same evidence could support a different interpretation, note it
+
+### Contradictions
+Items that disagree with each other, with both citations.
+
+### Gaps
+What you searched for and didn't find, specifically: "Searched the issue tracker for [query] across [time range]. No matching issues."
+
+### Additional Leads
+Anything that suggests further investigation in a different source, for its investigator or a follow-up pass. For example, a chat thread that a PR mentions.
diff --git a/skills/why/references/source-playbook.md b/skills/why/references/source-playbook.md
new file mode 100644
index 0000000..aa3d877
--- /dev/null
+++ b/skills/why/references/source-playbook.md
@@ -0,0 +1,17 @@
+# Source playbooks
+
+The why skill spawns one investigator per available evidence category, each reading a single source-specific playbook below. The playbooks are concrete examples for common MCPs. Adapt them for a different MCP in the same category.
+
+| Category | Playbook | Example MCP it documents |
+|---|---|---|
+| Source control history | [`code-archaeology.md`](./sources/code-archaeology.md) | git, `gh` |
+| Issue / ticket tracker | [`linear.md`](./sources/linear.md) | Linear (adapt for Jira, GitHub Issues, Plane, Shortcut) |
+| Long-form documents | [`notion.md`](./sources/notion.md) | Notion (adapt for Confluence, Google Docs, Coda) |
+| Real-time team chat | [`slack.md`](./sources/slack.md) | Slack (adapt for Discord, Microsoft Teams, Mattermost) |
+| Infrastructure observability | [`datadog.md`](./sources/datadog.md) | Datadog (adapt for New Relic, Honeycomb, Grafana, Splunk) |
+| Error / exception tracking | [`sentry.md`](./sources/sentry.md) | Sentry (adapt for Rollbar, Bugsnag, Airbrake) |
+| Product analytics warehouse | [`databricks.md`](./sources/databricks.md) | Databricks SQL (adapt for Snowflake, BigQuery, ClickHouse, dbt) |
+
+Cross-cutting:
+
+- [`incident-postmortem.md`](./sources/incident-postmortem.md). Add this if the target code looks defensive (null checks, retry, timeout, rate limit, feature flag, egress guard, OOM handler).
diff --git a/skills/why/references/sources/code-archaeology.md b/skills/why/references/sources/code-archaeology.md
new file mode 100644
index 0000000..e3555f8
--- /dev/null
+++ b/skills/why/references/sources/code-archaeology.md
@@ -0,0 +1,51 @@
+# Code Archaeology (git + in-repo)
+
+## What this source contains
+
+- Commit history (messages, dates, authors, diffs)
+- PR descriptions, review comments, and discussion threads (via `gh`)
+- Inline code comments, TODOs, FIXMEs, deprecation notes
+- ADRs (architectural decision records) if the repo keeps them
+- Tests, whose names and assertions often encode the edge cases that motivated a change
+- Related files modified in the same commits (co-change signal)
+- CHANGELOG entries and release notes in the repo
+- Issue/ticket IDs in commit messages and PR bodies
+
+The most trustworthy source, tied directly to the code, and the most complete. Everything that went through the repo should be here.
+
+## How to search it
+
+Expand the seed commit list:
+
+```bash
+# History through renames
+git log --follow --oneline -- <file>
+
+# Pickaxe: commits that added or removed this exact text
+git log -S '<exact_string_from_code>' -- <file>
+
+# Or for patterns:
+git log -G '<regex>' -- <file>
+
+# Who wrote each line and when
+git blame -L <start>,<end> <file>
+
+# One commit's full diff
+git show <hash>
+
+# Commits between two points affecting this file
+git log <old>..<new> -p -- <file>
+```
+
+Look for out-of-band docs:
+
+```bash
+# ADRs often live in docs/adr/ or similar
+rg -l -i 'architecture.decision' --glob '*.md'
+
+# TODOs and FIXMEs near the target
+rg -n -C2 '(TODO|FIXME|HACK|XXX|NOTE)' <target_file>
+
+# Related tests
+rg -l '<symbol>' --glob '*test*'
+```
diff --git a/skills/why/references/sources/databricks.md b/skills/why/references/sources/databricks.md
new file mode 100644
index 0000000..2770763
--- /dev/null
+++ b/skills/why/references/sources/databricks.md
@@ -0,0 +1,70 @@
+# Databricks Analytics & System Tables
+
+## What this source contains
+
+Databricks is the product-analytics, data-pipeline, and warehouse-telemetry layer. It complements Datadog. Datadog is the *infra/runtime* view, Databricks is the *product/data* view (what users did, which experiments ran, how feature usage evolved, where a threshold constant came from).
+
+- **Product analytics events.** `your_warehouse.events.analytics_track_event` (raw) and typed, deduplicated per-event dbt models in `<your_analytics_db>.<schema>.<table>`. User behavior: feature invocations, clicks, accepts/rejects, submissions, client-reported errors.
+- **Usage & billing events.** `your_warehouse.events.usage_event` / `<your_analytics_db>.<schema>.stg_usage_events`, `your_warehouse.events.raw_model_event` / `<your_analytics_db>.<schema>.stg_raw_model_events`. For cost- or volume-driven decisions.
+- **Experiment / feature-flag data.** Exposure and outcome tables. **Schema is company-specific.** Probe with `SHOW TABLES` before assuming names.
+- **System tables.** `system.query.history`, `system.compute.warehouses`, `system.billing.*`, `system.access.audit`. Answer "was this query expensive?", "how often did anyone run this?", "when did warehouse load spike?"
+- **dbt lineage.** Models in `<your_analytics_db>.<schema>` reveal what pipelines depend on a table/field. Upstream changes frequently motivate consumer-code changes.
+- **Databricks notebooks.** Exploratory analyses engineers wrote before code changes. **Not queryable via the SQL MCP.** If you suspect the rationale lives in a notebook, name it as a gap.
+
+## How to search it
+
+Use the Databricks SQL MCP. Primary tool: `execute_sql_read_only`. If it returns a `statement_id`, poll with `poll_sql_result` rather than re-running.
+
+**Orient before querying.** Schemas are company-specific. Probe before trusting a table name:
+
+```sql
+SHOW TABLES IN <your_analytics_db>.<schema> LIKE '*<keyword>*';
+DESCRIBE TABLE <your_analytics_db>.<schema>.stg_<event>;
+```
+
+**Time-bound every query.** These tables are huge and unconstrained scans time out. Filter on `_timestamp` (events) or `start_time` (`system.query.history`) with a window bracketing the ship date, typically ~30 days before and after, wider only for strong reason.
+
+**Prefer typed dbt models over the raw table.** `<your_analytics_db>.<schema>.<table>` is deduplicated, typed, and liquid-clustered. `your_warehouse.events.analytics_track_event` has duplicates and untyped `properties_json`. Model-name pattern: `stg_<source>_<event_name_with_underscores>`, where `<source>` is `app`, `backend`, `website`, or `cli`. Confirm the exact model name with `SHOW TABLES` when the pattern alone doesn't resolve it. Drop to the raw table only when there's no dbt model yet, or you need events from inside the dbt refresh lag.
+
+**Column conventions on the typed dbt models** (knowing these avoids a `DESCRIBE` round-trip):
+
+- `_timestamp`, `_id`, `_auth_id`, `_request_id`, `event_name`. Standard on every model
+- `properties_<name>`. Typed, underscore-cased event properties (`properties_entrypoint`, `properties_size_bytes`, …)
+- `context_team_id`, `context_client_version`, `context_country`, `context_client_os`. Pre-extracted client context
+
+### Investigation patterns that tend to pay off
+
+Pick the table + column combination that matches the target:
+
+1. **Event usage trajectory.** Daily counts on the relevant `stg_*` model across a ±30d window around the PR merge. A step function from zero to steady volume within a day or two of the merge is strong circumstantial evidence the PR launched the feature. A decay to zero suggests a deprecation or deletion.
+2. **Guard-rail / defensive-check origin.** Distribution (median / p99 / max) of the relevant `properties_<name>` column in the 14 days *before* the PR. A p99 that matches the target's threshold constant suggests the number was chosen from data.
+3. **Experiment / feature-flag lookup.** `SHOW TABLES ... LIKE '*experiment*'` to find the exposure table, then pull exposure counts by variant for the relevant flag key near the PR date.
+4. **Query-history evidence for migrations, backfills, or perf rewrites.** `system.query.history` filtered by `statement_text ILIKE '%<table_or_symbol>%'` with a tight `start_time` window surfaces the expensive queries that likely motivated the change (sort by `total_duration_ms` or aggregate `SUM(read_bytes)`, `COUNT(*)`).
+5. **dbt lineage.** If the target reads from or writes into a `<your_analytics_db>.<schema>` model, the model's own git history (in this repo) often carries the rationale. Hand that lead back to the git investigator rather than chasing it yourself.
+
+## What good evidence looks like here
+
+Beyond the pattern shapes above:
+
+- An error-classifying event's count drops to near zero in the days after a defensive-code PR. Suggests the PR resolved that error class
+- An exposure table row names the target's feature-flag key with a "shipped" / "concluded" decision around the PR ship date
+
+## Common pitfalls
+
+- **Instrumented ≠ caused.** An event's existence means someone cared enough to log it, not that the target code exists *because* of it. Pair with a PR/commit citation from the git investigator before claiming causation.
+- **Silent instrumentation changes.** A step function in event volume may mean a new event started being logged, not that user behavior changed. Check for instrumentation PRs in the same window before reading the ramp as a feature-launch signal.
+- **Schema drift.** Event properties evolve. A column on the typed dbt model today may not have existed when the target was written. Older data may carry the property only inside raw `properties_json`.
+- **dbt refresh lag.** `<your_analytics_db>.<schema>.*` is rebuilt on a schedule (often hourly/daily). For events from the last few hours, fall back to `your_warehouse.events.*` and deduplicate by `_id`.
+- **Company-specific tables.** Experiment, feature-flag, billing, and usage tables vary. Reporting a result from a table whose existence you never confirmed is a classic failure mode. Probe with `SHOW TABLES` / `DESCRIBE TABLE` first.
+- **Retention cliff.** If the relevant window predates the table's retention or the dbt model's creation date, that's a *gap*, not a null result. Name it explicitly so the synthesizer doesn't read "no results" as "no activity."
+- **Notebooks aren't queryable.** The SQL MCP can't see Databricks notebooks. If you suspect the rationale lives in one, return a gap.
+
+## What to return
+
+For each relevant finding:
+- Type (product event / experiment exposure / usage or billing event / system-table row / dbt model)
+- Fully-qualified table name and the exact query you ran
+- Time window queried
+- Compact numeric summary (counts, percentiles, first/last-seen timestamps). **Don't dump raw rows.**
+- Temporal correlation with the target's ship date (e.g., "first row 2024-08-15, PR #49074 merged 2024-08-14")
+- Relevance + strength: direct / circumstantial / weak
diff --git a/skills/why/references/sources/datadog.md b/skills/why/references/sources/datadog.md
new file mode 100644
index 0000000..536466d
--- /dev/null
+++ b/skills/why/references/sources/datadog.md
@@ -0,0 +1,97 @@
+# Datadog Telemetry
+
+## What this source contains
+
+Datadog holds the runtime record, what actually happened in production, as opposed to what was planned or discussed.
+
+- **Metrics.** Counters, gauges, histograms instrumented by the team. A metric's *presence* is itself evidence that someone thought the number worth watching.
+- **Monitors & alerts.** Conditions the team decided warranted waking someone up. A monitor firing on `rate_limit_hit > 10/min` is direct evidence the team worried about that threshold.
+- **Dashboards.** Curated views of what the team considers important for a subsystem.
+- **APM traces & spans.** Request-level runtime data, for "why is this slow" / "why is there a timeout here" questions.
+- **Logs.** High-volume event records, often holding the error conditions that motivated defensive code.
+- **Incidents.** Formal incident records with timelines and linked postmortems.
+- **Notebooks.** The team's exploratory investigations, often with hypotheses and analyses.
+
+## How to search it
+
+Use the Datadog MCP. Start broad, then narrow.
+
+1. **Identify the owning service(s).**
+
+   ```
+   search_datadog_services (filter by name or team)
+   search_datadog_service_dependencies (see upstream/downstream)
+   ```
+
+2. **Dashboards and monitors first.**
+
+   ```
+   search_datadog_dashboards (query: feature name, service name, symbol)
+   search_datadog_monitors   (same queries)
+   ```
+
+   When a dashboard or monitor covers the target, note its queries and watched thresholds. The threshold is frequently the answer to "why is this clamped at N?"
+
+3. **Metrics around the target.**
+
+   ```
+   search_datadog_metrics (by name pattern, e.g., the feature or symbol)
+   get_datadog_metric_context (metadata: description, units, tags)
+   get_datadog_metric (timeseries; "was there a spike around the PR date?")
+   ```
+
+   Correlating a metric's trajectory with the target's add/change date is strong supporting evidence: "the `payment_timeout` metric spiked 2023-11-03, and the retry logic merged 2023-11-06."
+
+4. **Logs. Narrow, don't dump.**
+
+   ```
+   search_datadog_logs (raw log patterns near the target, set use_log_patterns=true)
+   analyze_datadog_logs (SQL-style aggregations, only when you need counts)
+   ```
+
+   Search with symbols, error strings, or feature names. **Strongly prefer time-bounded queries** (e.g., 30 days before/after the change).
+
+5. **APM spans and traces.**
+
+   ```
+   aggregate_spans    (stats: "how often does this endpoint fail?")
+   search_datadog_spans (inspect individual spans)
+   get_datadog_trace  (a specific trace ID)
+   ```
+
+   Useful for timeouts, retries, slow paths, and cross-service behavior.
+
+6. **Incidents.**
+
+   ```
+   search_datadog_incidents (by title, team, date range)
+   get_datadog_incident     (full detail for a specific incident)
+   ```
+
+   If the target looks defensive, search for incidents around the time it was added. An incident whose timeline includes "added defensive check for X" is near-direct evidence.
+
+## What good evidence looks like here
+
+- A monitor whose query and threshold match the constraint the code enforces (code clamps to 100, monitor alerts when requests exceed 100/min)
+- A dashboard created by the target's author, with widgets that correspond to what the code measures or guards against
+- A metric showing a production spike immediately before the code was merged, and stable values after
+- An incident record referencing the target code, the same symbols, or the same error strings
+- Logs showing a specific error pattern the defensive code would prevent, timestamped in the window before the change
+
+## Common pitfalls
+
+- **Correlation is not causation.** A spike before a PR and stabilization after is suggestive, not definitive, since other changes may have landed in the same window. Check neighboring PRs.
+- **Overfitting to the chart you found.** A chart reflects its maker's framing. A chart named "retry success rate" is evidence the team cared about retry success, not that it's why a specific line of code exists.
+- **Vanished telemetry.** Metrics can be renamed, deleted, or have short retention. If you can't find data from the relevant window, that's a gap, not a null result.
+- **Noise at scale.** Narrow log searches by service, tag, and time window aggressively. Use `analyze_datadog_logs` to aggregate rather than dumping raw logs.
+- **Instrumented != caused.** A metric shows someone cared enough to measure something, not that the code was added *because* of it. Cross-reference with commit/PR dates.
+
+## What to return
+
+For each relevant item:
+- Type (dashboard / monitor / metric / log pattern / trace / incident / notebook)
+- Title or name
+- Link or identifier (dashboard ID, monitor ID, metric name, incident ID)
+- Owner/author and created/modified date
+- The specific condition, query, or quote that bears on the question (verbatim where possible)
+- Relevance: what this suggests about the target code, and how strong the connection is
diff --git a/skills/why/references/sources/incident-postmortem.md b/skills/why/references/sources/incident-postmortem.md
new file mode 100644
index 0000000..e7397e7
--- /dev/null
+++ b/skills/why/references/sources/incident-postmortem.md
@@ -0,0 +1,5 @@
+# Incident & Postmortem Context
+
+- **Linear**: look for tickets labeled `incident`, `sev-*`, `postmortem-action-item`, `reliability`
+- **Slack**: search `#sev-*` and `#incident-*` channels around the dates the target code was added
+- **Git**: commits with messages like "fix for incident", "add defensive check", "revert" followed by "re-apply with..." are strong signals
diff --git a/skills/why/references/sources/linear.md b/skills/why/references/sources/linear.md
new file mode 100644
index 0000000..899c643
--- /dev/null
+++ b/skills/why/references/sources/linear.md
@@ -0,0 +1,48 @@
+# Linear Tickets
+
+## What this source contains
+
+- Issues describing features, bugs, and their motivation
+- Project docs attached to issues (often PRDs or specs)
+- Parent/sub-issue relationships (broader initiative → specific tickets)
+- Comments on issues (clarifications, scope changes, "why we're doing this" rationale)
+- Labels (e.g., `compliance`, `customer-request`, `perf`) that signal the type of motivation
+- Status updates that explain scope changes
+- Attachments and linked GitHub PRs
+
+Linear is where the product/business context often lives: the "we're doing this because customer X asked" or "this is for the Q3 compliance initiative" layer.
+
+## How to search it
+
+Use the Linear MCP.
+
+1. **Start with linked tickets.** If the seed commits or PRs reference ticket IDs (e.g., `ENG-1234`, `[BUG-567]`), fetch those first with `get_issue`. Read the full issue including comments.
+2. **List related issues by keyword.** Use `list_issues` with text search for the feature name, key symbol, or business term. Try multiple phrasings.
+3. **Walk the issue tree.** If you land on a sub-issue, fetch its parent. Sub-issues are tactical. Parents often carry the "why."
+4. **Read project docs.** If the issue belongs to a project, use `get_project` and check attached docs. Project-level documents are where specs and rationale are most often captured.
+5. **Check labels and milestones.** Labels hint at the category of motivation (customer-request, incident-followup, compliance). Milestones tie work to deadlines, which often reveal motivation.
+
+## What good evidence looks like here
+
+- An issue description stating the business problem: "Customer Acme needs X because of their SOC2 audit"
+- A comment recording a decision: "We decided to go with approach B because approach A would require touching the billing service"
+- A parent issue titled like an initiative: "Q3 Enterprise Readiness" or "Reduce Payment Failures"
+- An attached PRD or spec
+- Labels like `customer:acme`, `incident-followup`, `compliance`, `perf-regression`
+
+## Common pitfalls
+
+- **Scope drift.** The ticket the PR references may have been closed and reopened with a different scope. Read the whole history.
+- **Mechanical templates.** Some teams require "Why" sections but fill them with boilerplate. Generic text ("improve user experience") is probably not a real answer.
+- **Stale tickets.** Old tickets often reflect a version of the plan that changed. Check dates and cross-reference with the code's ship date.
+- **Closed-as-duplicate chains.** Follow the duplicate-of relationships back to the canonical ticket.
+- **Private workspace content.** If you can't access an issue, note that as a gap rather than guessing.
+
+## What to return
+
+For each relevant ticket:
+- Ticket ID and title
+- The problem/motivation quoted from the description or comments (not paraphrased. The synthesizer needs the exact text to cite)
+- Labels, parent issue, project
+- Author, created date, closed date
+- Link to the ticket if available
diff --git a/skills/why/references/sources/notion.md b/skills/why/references/sources/notion.md
new file mode 100644
index 0000000..d16230a
--- /dev/null
+++ b/skills/why/references/sources/notion.md
@@ -0,0 +1,55 @@
+# Notion Docs
+
+## What this source contains
+
+- PRDs (product requirement documents)
+- Technical specs and RFCs
+- Architectural decision records (ADRs)
+- Meeting notes from design reviews
+- Team pages with domain context
+- Postmortems from incidents
+- Runbooks that may explain defensive code
+- Strategy documents that set priorities
+
+Notion is where "why" often lives in long-form before it becomes code. A significant feature usually has a doc.
+
+## How to search it
+
+Use the Notion MCP.
+
+1. **Keyword searches with `notion-search`.** Try:
+   - The feature name
+   - Key symbols / class names from the target code
+   - Author handles (design docs are often authored before the code lands)
+   - Error strings or user-visible terms
+   - Time-bounded queries if you know when the code shipped
+2. **Fetch candidate pages with `notion-fetch`.** Read the full content, not the preview. Rationale is often buried mid-document.
+3. **Follow backlinks and child pages.** Design docs often have sub-pages for alternatives considered, appendices, or implementation notes.
+4. **Check related databases.** `notion-query-data-sources` and `notion-query-meeting-notes` can surface meeting notes that discussed the decision.
+5. **Search author-specific spaces.** If the PR author has a personal notebook (common at some companies), it may hold exploratory thinking that preceded the code.
+
+## What good evidence looks like here
+
+- A PRD with a "Problem statement" or "Motivation" section that matches the target code's purpose
+- An "Alternatives considered" or "Rejected approaches" section
+- A postmortem that names the target code as the fix for a specific incident
+- Meeting notes that record "we decided X because Y" and tie to the same author/date range as the PR
+- An ADR template filled out non-trivially (status, context, decision, consequences)
+
+## Common pitfalls
+
+- **Outdated docs.** Specs are often written before implementation and not updated. The doc may describe a plan that changed. Cross-check against the actual PR.
+- **Doc vs. reality drift.** A spec may say "we'll do X" but the code actually does Y. Flag the divergence. The synthesizer will surface the contradiction.
+- **Boilerplate templates.** Some orgs require a "Why" section that gets filled with fluff. Look for specificity.
+- **Unlinked docs.** The most relevant doc may not be linked from anywhere. Broad keyword searches help.
+- **Multiple drafts.** If a topic has multiple docs, find the one that was finalized or most recently updated. Check dates.
+- **Access-restricted pages.** If you can't access a page, note it as a gap.
+
+## What to return
+
+For each relevant doc:
+- Title and URL
+- Authors and last-updated date
+- The motivation text (verbatim quote), with page/section location
+- Relevant linked pages (so the synthesizer can cite them)
+- Whether the doc was finalized or draft
diff --git a/skills/why/references/sources/sentry.md b/skills/why/references/sources/sentry.md
new file mode 100644
index 0000000..8200fd9
--- /dev/null
+++ b/skills/why/references/sources/sentry.md
@@ -0,0 +1,66 @@
+# Sentry Error History
+
+## How to search it
+
+Use the Sentry MCP.
+
+1. **Orient.** If you don't know the project slug and organization:
+
+   ```
+   find_organizations
+   find_projects
+   ```
+
+2. **Search for issues related to the target.**
+
+   ```
+   search_issues (natural language, e.g., "errors in PaymentService timeout", "unhandled exceptions in uploadFile")
+   ```
+
+   Build queries from the exception class names the target handles, its function or class name, the error message strings it checks for, and its file path.
+
+3. **Narrow by release and time window.**
+
+   ```
+   search_issue_events (filter by release, time, environment, trace ID, tags)
+   get_issue_tag_values (for an issue, see distribution across versions, users, environments)
+   ```
+
+   For a suspected issue, check **first seen** and **last seen** (does the stop line up with the target's ship date?), the **affected releases** (which saw it, and which was the fix?), and the **frequency trajectory** (did it spike, then get resolved?).
+
+4. **Pull the full event for context.**
+
+   ```
+   get_sentry_resource (pass a Sentry URL or type+ID)
+   ```
+
+   Does the stack trace pass through the target code? Do the tags and breadcrumbs match the conditions the target defends against?
+
+5. **Check releases that landed near the target.**
+
+   ```
+   find_releases (around the commit date of the target)
+   ```
+
+   Cross-reference release version with the PR's merge date.
+
+6. **Use Seer sparingly.**
+
+   ```
+   analyze_issue_with_seer
+   ```
+
+   Seer's AI root-cause analyses are a hypothesis generator. Treat them as inference, not authoritative. The actual events and stack traces are the primary evidence.
+
+## What to return
+
+For each relevant issue:
+- Issue ID and title
+- Project and organization
+- First seen / last seen timestamps
+- Event count (and sampling rate if known)
+- Affected releases
+- A representative stack trace snippet showing relevance to the target (verbatim excerpt, not summary)
+- First/last-seen correlation with the target's ship date
+- Link to the issue
+- Any author comments or resolution notes
diff --git a/skills/why/references/sources/slack.md b/skills/why/references/sources/slack.md
new file mode 100644
index 0000000..cadd561
--- /dev/null
+++ b/skills/why/references/sources/slack.md
@@ -0,0 +1,17 @@
+# Slack Conversations
+
+## How to search it
+
+Slack MCP tools vary. Check which Slack MCP is available and inspect its tool schema first. It may require `mcp_auth`. If authentication fails, stop and report the gap.
+
+1. **Author-bounded search.** Messages from the PR author around the PR merge date. Limits scope dramatically and often hits gold.
+3. **PR URL search.** Slack often links PRs when they're reviewed or discussed. Search for the PR URL (or just `/pull/<number>`).
+4. **Error string search.** If the code handles a specific error, search for the error string. Incident threads often surface.
+
+## Common pitfalls
+
+- **Channel archaeology limits.** Very old messages may be gone due to retention policies. If you can't find anything before a certain date, note the retention cliff.
+- **Unsearched DMs.** Many decisions happen in DMs that aren't searchable. You'll miss them. That's a known limitation.
+- **Speculative jokes as "decisions."** Slack is casual. "Lol just do the thing" isn't a decision, even if it preceded the commit. Look for considered discussion.
+- **Context collapse in single messages.** Without the thread, a single message often reads differently than in context. Always fetch threads.
+- **Auth failures.** If the MCP isn't authenticated, stop. Don't make up findings. Report that Slack wasn't searchable.
diff --git a/skills/why/references/synthesizer-prompt.md b/skills/why/references/synthesizer-prompt.md
new file mode 100644
index 0000000..287a350
--- /dev/null
+++ b/skills/why/references/synthesizer-prompt.md
@@ -0,0 +1,129 @@
+# Synthesizer Prompt Template
+
+Fill in the placeholders.
+
+---
+
+You are answering a "why" question about a piece of code by synthesizing findings from investigators who searched different historical sources (source control, issue / ticket tracker, long-form documents, real-time team chat, infrastructure observability, error / exception tracking, product analytics warehouse, and code comments). Produce a confidence-weighted, evidence-cited narrative that says honestly what the evidence supports, and just as honestly what it doesn't.
+
+## The Question
+
+> {QUESTION}
+
+## The Code Anchor
+
+**Target files:** {FILES_WITH_LINE_RANGES}
+
+**Key symbols:** {SYMBOLS}
+
+## Investigator Findings
+
+{ALL_INVESTIGATOR_FINDINGS}
+
+## Sources That Weren't Searched
+
+{SKIPPED_SOURCES_WITH_REASONS}
+
+## Epistemics Framework
+
+You MUST follow `references/epistemics.md`. Read it in full before writing. The key rules:
+
+1. Every claim must sit in one tier: **Direct**, **Supported**, **Inferred**, **Speculative**, **Unknown**. The tier sets its section and its phrasing.
+2. Every Direct/Supported claim must have a citation (PR #, ticket ID, doc URL, chat permalink, commit hash, or file:line).
+3. Inferred and Speculative claims must use hedged language ("appears to", "likely", "suggests", "one possibility is").
+4. Never cite code as evidence for its own intent.
+5. Document gaps. Don't fill them with plausible-sounding guesses.
+6. Treat a hypothesis embedded in the user's question as a candidate, not a conclusion. Check it independently.
+
+## Instructions
+
+1. **Read all investigator findings** and weigh the evidence yourself. Investigators gathered raw evidence, not conclusions.
+2. **Merge overlaps.** When several investigators cite the same PR, ticket, or doc, give it one authoritative reference.
+3. **Surface contradictions.** When two items disagree, show both. Don't pick one.
+4. **Calibrate each claim.** Find its evidence and tier. Direct: cite it and state it plainly. Inferred: hedge it and show the inference. Speculative: mark it. No evidence: put it in the gaps.
+5. **Verify citations by spot-checking.** If you're uncertain a cited item exists or says what the investigator claims, check it by reading the codebase or calling MCP tools. Don't propagate errors. Do not write files, commit, or modify external state.
+6. **Don't overreach.** The user will act on this. Leave an open question open rather than fill it with a confident guess.
+
+## Output Format
+
+Write for the user, in this exact structure:
+
+---
+
+### The Question
+
+Restate the user's question in one or two sentences.
+
+### The Code in Question
+
+File paths, line ranges, key symbols. Two or three lines to orient a cold reader.
+
+### What We Found
+
+Claims with textual evidence, each quoted or paraphrased and cited precisely:
+
+- **[Direct]** {Claim}. Source: [PR #123](url) / ticket ID / file:line. {Brief quote or paraphrase.}
+- **[Supported]** {Claim}. Evidence: {list of items and what each contributes}.
+
+`[Direct]` is single-source, explicit evidence. `[Supported]` is multiple indirect items converging on a conclusion.
+
+### What We Can Reasonably Infer
+
+Claims stated nowhere but well-supported by indirect evidence. Each bullet must make the inference chain visible ("Given A and B, it's likely that C") and must use hedged language ("appears to", "likely", "suggests", "is consistent with"):
+
+- **[Inferred]** {Hedged claim}. Reasoning: {the specific evidence and the inference step}.
+
+Skip this section if there's nothing to infer.
+
+### Competing Hypotheses
+
+If the evidence fits multiple stories, present each. Don't force a winner the record doesn't support. For each hypothesis:
+
+- **Hypothesis:** {one-sentence statement}
+- **Evidence for:** {specific items}
+- **Evidence against or missing:** {what would need to be true but isn't, or what counter-signals exist}
+
+Skip this section if there's a single clear answer.
+
+### What We Don't Know
+
+Specific gaps: "We searched the issue tracker for [query1], [query2], [query3] and found no issue discussing the rate-limit threshold", not "We don't know why." Include:
+
+- Questions that went unanswered
+- Searches that returned nothing
+- Sources that were unavailable, and why (such as a missing real-time team chat MCP)
+- People who would likely know but whom you can't ask
+
+### Sources Consulted
+
+What was actually searched, so the user can judge coverage and redirect:
+
+- **Source control history**: {file paths}, {number of commits reviewed}, PRs #{numbers}, and code comments searched. Or "Not searched. This should not happen because git and `gh` are always expected."
+- **Issue / ticket tracker**: {ticket IDs and keyword searches}. Or "Not searched. No matching MCP available in this environment."
+- **Long-form documents**: {page titles and search queries}. Or "Not searched. No matching MCP available in this environment."
+- **Real-time team chat**: {channels searched, date ranges, queries}. Or "Not searched. No matching MCP available in this environment."
+- **Infrastructure observability**: {dashboards, monitors, metrics, logs, traces, or incidents searched}. Or "Not searched. No matching MCP available in this environment."
+- **Error / exception tracking**: {issues, events, or releases searched}. Or "Not searched. No matching MCP available in this environment."
+- **Product analytics warehouse**: {fully-qualified tables queried, the time windows, and the numeric summaries (counts, percentiles, first/last-seen timestamps) that bore on the question}. Or "Not searched. No matching MCP available in this environment."
+
+### Confidence Summary
+
+One or two sentences on overall confidence, e.g.:
+
+> "The core rationale (A) is well-supported by direct PR and ticket evidence. The specific threshold value (100) is inferred from the surrounding context but not explicitly documented. The question of whether this was driven by a customer request could not be answered. No relevant issue tracker or long-form doc content surfaced, and real-time team chat search was unavailable."
+
+---
+
+## Quality Check Before Returning
+
+Revise if any item fails:
+
+1. Does every claim in "What We Found" have a citation? If not, add one or move it to "Inferred" or "Hypotheses."
+2. Is the phrasing tier-appropriate? (Direct claims can use "because". Inferred claims cannot.)
+3. Did you surface every contradiction you noticed, or quietly pick one?
+4. Does "What We Don't Know" name specific gaps? If it's empty or missing, be suspicious. Historical investigations almost always have gaps.
+5. Did you check any hypothesis embedded in the question against the evidence rather than rubber-stamp it?
+6. Did you cite code as evidence for its own intent? Remove it. Code is mechanics, not motivation.
+7. Is the tone calibrated? A confident-sounding answer on weak evidence is the exact failure this skill exists to prevent.
+
+The value of this output is its honesty, not its authority. A reader who takes it to the original author, an engineering lead, or a product manager should know what's known, what's inferred, and what's missing, so they can ask the right follow-up questions. Optimize for being useful, not for looking decisive.