diff --git a/Makefile b/Makefile
index 7f5727e2..dbfca420 100644
--- a/Makefile
+++ b/Makefile
@@ -1,4 +1,4 @@
-.PHONY: help install install-dev langgraph-dev test test-unit test-provider openai anthropic nv_build test-integration test-cov test-ci lint lint-fix format format-check clean build docker-build docker-smoke
+.PHONY: help install install-dev langgraph-dev test test-unit test-provider openai anthropic nv_build test-integration test-cov test-ci verify lint lint-fix format format-check clean build docker-build docker-smoke
 
 # Prefer uv if available, else use pip (set when Makefile is parsed)
 UV := $(shell command -v uv 2>/dev/null)
@@ -37,6 +37,7 @@ help:
 	@echo "  make test-provider [openai|anthropic|nv_build] - Run live provider tests"
 	@echo "  make test-integration - Run integration tests only (invokes full graph, may call LLMs)"
 	@echo "  make test-cov       - Run tests with coverage report"
+	@echo "  make verify         - Check deterministic and neural-envelope safety invariants"
 	@echo "  make lint           - Run linters (ruff only)"
 	@echo "  make lint-fix       - Auto-fix lint errors with ruff"
 	@echo "  make format         - Format code with ruff"
@@ -105,6 +106,10 @@ test-cov:
 test-ci:
 	pytest -m "not integration and not provider" --cov=src/skillspector --cov-report=term-missing --cov-report=xml tests/
 
+# Run the fast executable specification and adversarial-oracle property suite.
+verify:
+	pytest tests/verification/
+
 # Run linters (fast: ruff only)
 lint:
 	@echo "Running ruff..."
diff --git a/README.md b/README.md
index dd3cd4b8..664d60ca 100644
--- a/README.md
+++ b/README.md
@@ -19,6 +19,7 @@ SkillSpector is part of the [NVIDIA Verified Skills pipeline](https://docs.nvidi
 - **[Scan agent skills before installation](https://docs.nvidia.com/skills/scanning-agent-skills)** — Hosted guide: when to scan, how to read a report, and how to gate installs.
 - **[Development guide](docs/DEVELOPMENT.md)** — Architecture, package layout, and how to extend the analyzer pipeline.
 - **[Analysis resource bounds](docs/ANALYSIS_RESOURCE_BOUNDS.md)** — Fail-closed bundle, parser, nested-artifact, ledger, and finding ceilings.
+- **[Verification boundary](docs/VERIFICATION.md)** — Executable safety contracts around deterministic scoring and LLM enrichment.
 - **[Pi extension](docs/PI_EXTENSION.md)** — Install SkillSpector as a Pi tool for scanning skills from inside agent sessions.
 
 ## Features
diff --git a/docs/VERIFICATION.md b/docs/VERIFICATION.md
new file mode 100644
index 00000000..110de23f
--- /dev/null
+++ b/docs/VERIFICATION.md
@@ -0,0 +1,35 @@
+# Verification boundary
+
+SkillSpector combines deterministic analyzers with optional model inference. Verification
+claims must keep those surfaces separate:
+
+| Stratum | Surface | Contract | Mechanism |
+| --- | --- | --- | --- |
+| S1 | risk scoring and banding | the implementation matches the executable reference model | differential property tests |
+| S2 | deterministic findings crossing the LLM meta-analyzer | arbitrary valid model outcomes cannot remove, invent, or weaken deterministic evidence | adversarial-oracle property tests |
+| S3 | semantic conclusions produced by an LLM | no universal correctness claim | evaluation datasets and provider tests |
+
+Run the fast S1/S2 contract with:
+
+```bash
+make verify
+```
+
+## Phase 1 invariants
+
+- Risk scores are integers in `[0, 100]` and map exhaustively to the documented bands.
+- Diminishing returns apply independently per rule with weights `1`, `0.5`, and `0.25`.
+- Within a rule, findings are ordered by their unweighted score contribution (severity,
+  confidence, and executable provenance together). This makes the score permutation-invariant
+  and monotone: adding non-negative evidence cannot lower the score by taking a larger
+  diminishing-return weight away from stronger evidence.
+- Blocking floors for proven SC8, BH2, and BH3 conditions survive ordinary weighted scoring.
+- Executable-file multiplication is scoped by both source provenance and path.
+- The LLM meta-analyzer may enrich a deterministic finding, but cannot remove it, change its
+  identity or severity, lower its confidence, or create a new deterministic finding.
+
+The reference model in `tests/verification/risk_reference.py` intentionally imports no
+production scoring constants or helpers. A scoring-policy change must update that readable
+specification deliberately. The suite does not claim to verify static detector recall, LLM
+truthfulness, provider availability, or arbitrary malformed data rejected before the typed
+meta-analyzer boundary.
diff --git a/pyproject.toml b/pyproject.toml
index df3d3286..071ceff9 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -63,6 +63,7 @@ dev = [
     "pytest>=9.0.0",
     "pytest-asyncio>=1.3.0",
     "pytest-cov>=7.0.0",
+    "hypothesis>=6.151.9",
     "ruff>=0.15.0",
     "mypy>=1.19.0",
     "build>=1.4.0",
diff --git a/src/skillspector/nodes/report.py b/src/skillspector/nodes/report.py
index 284cdde1..12b30850 100644
--- a/src/skillspector/nodes/report.py
+++ b/src/skillspector/nodes/report.py
@@ -453,19 +453,17 @@ def _compute_risk_score(
     to [0, 1]). Findings with confidence <= 0 are skipped entirely — they do not
     contribute to the score but remain in the reported findings list.
 
-    Within each rule_id bucket, findings are processed in severity-descending
-    order so that the highest-severity occurrence always receives the full weight.
+    Within each rule_id bucket, findings are processed by their unweighted score
+    contribution (severity points x confidence x executable multiplier). This gives
+    the strongest evidence the largest diminishing-return weight, makes the score
+    independent of analyzer output order, and ensures adding non-negative evidence
+    cannot lower the score by displacing a stronger occurrence.
 
     Base points per severity: CRITICAL=50, HIGH=25, MEDIUM=10, LOW=5.
     1.3x multiplier applied only to findings from executable script files;
     findings from documentation files (markdown, text, json, yaml, toml)
     are scored at base weight to avoid punishing security documentation.
     """
-    severity_rank = {"CRITICAL": 0, "HIGH": 1, "MEDIUM": 2, "LOW": 3}
-    sorted_findings = sorted(
-        findings,
-        key=lambda f: (f.rule_id or "UNKNOWN", severity_rank.get((f.severity or "LOW").upper(), 4)),
-    )
 
     def component_source_scope(component: Mapping[str, object]) -> str:
         for key in ("source_identity", "source_url", "source_digest"):
@@ -491,6 +489,24 @@ def finding_source_scope(finding: Finding) -> str:
                 cm.get("executable", False)
             )
 
+    def finding_strength(finding: Finding) -> float:
+        confidence = max(0.0, min(1.0, finding.confidence))
+        severity = (finding.severity or "LOW").upper()
+        strength = _SEVERITY_POINTS.get(severity, 5) * confidence
+        if has_executable_scripts and file_executable.get(
+            (finding_source_scope(finding), finding.file), False
+        ):
+            strength *= 1.3
+        return strength
+
+    sorted_findings = sorted(
+        findings,
+        key=lambda f: (
+            f.rule_id or "UNKNOWN",
+            -finding_strength(f),
+        ),
+    )
+
     rule_occurrence_count: dict[str, int] = {}
     score = 0.0
 
diff --git a/tests/nodes/test_report.py b/tests/nodes/test_report.py
index 785868e2..f9c4b8b5 100644
--- a/tests/nodes/test_report.py
+++ b/tests/nodes/test_report.py
@@ -327,6 +327,17 @@ def test_same_rule_low_before_critical_sorted_correctly(self) -> None:
         # Sorted: CRITICAL first (50*1.0) + LOW second (5*0.5=2.5) = 52.5 -> 52
         assert score == 52
 
+    def test_weak_critical_does_not_lower_score_by_displacing_stronger_high(self) -> None:
+        """Weight allocation follows score strength, not severity alone."""
+        stronger_high = _finding("TM1", "HIGH", confidence=0.1)
+        weak_critical = _finding("TM1", "CRITICAL", confidence=0.01)
+
+        before, _, _ = _compute_risk_score([stronger_high], False)
+        after, _, _ = _compute_risk_score([stronger_high, weak_critical], False)
+
+        assert before == 2
+        assert after == 2
+
     def test_exact_band_boundary_21_is_medium(self) -> None:
         findings = [
             _finding("R1", "MEDIUM", confidence=1.0),
diff --git a/tests/verification/__init__.py b/tests/verification/__init__.py
new file mode 100644
index 00000000..1dd4dc5b
--- /dev/null
+++ b/tests/verification/__init__.py
@@ -0,0 +1,4 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+"""Executable safety specifications for SkillSpector's deterministic envelope."""
diff --git a/tests/verification/risk_reference.py b/tests/verification/risk_reference.py
new file mode 100644
index 00000000..74bac930
--- /dev/null
+++ b/tests/verification/risk_reference.py
@@ -0,0 +1,112 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+"""Small independent reference model for the risk-scoring contract.
+
+This model deliberately does not import production scoring constants or helpers. A
+production change must therefore update this readable specification deliberately,
+rather than making differential tests agree through shared implementation details.
+"""
+
+from __future__ import annotations
+
+from collections.abc import Mapping, Sequence
+from dataclasses import dataclass, field
+
+
+@dataclass(frozen=True)
+class ReferenceFinding:
+    """Only the finding fields that influence the risk score."""
+
+    rule_id: str = "UNKNOWN"
+    severity: str = "LOW"
+    confidence: float = 0.5
+    file: str = "SKILL.md"
+    source_identity: str | None = None
+    source_url: str | None = None
+    source_digest: str | None = None
+    evidence: Mapping[str, object] = field(default_factory=dict)
+
+
+_POINTS = {"CRITICAL": 50, "HIGH": 25, "MEDIUM": 10, "LOW": 5}
+_WEIGHTS = (1.0, 0.5, 0.25)
+_BANDS = ((81, "CRITICAL"), (51, "HIGH"), (21, "MEDIUM"), (0, "LOW"))
+_RECOMMENDATIONS = {
+    "CRITICAL": "DO_NOT_INSTALL",
+    "HIGH": "DO_NOT_INSTALL",
+    "MEDIUM": "CAUTION",
+    "LOW": "SAFE",
+}
+
+
+def _confidence(value: float) -> float:
+    return max(0.0, min(1.0, value))
+
+
+def _source_scope(item: ReferenceFinding | Mapping[str, object]) -> str:
+    for key in ("source_identity", "source_url", "source_digest"):
+        value = getattr(item, key, None) if isinstance(item, ReferenceFinding) else item.get(key)
+        if isinstance(value, str) and value:
+            return f"{key}:{value}"
+    return ""
+
+
+def _score_floor(finding: ReferenceFinding) -> int:
+    if finding.rule_id == "SC8":
+        return 51
+    if finding.severity.upper() != "CRITICAL":
+        return 0
+    if finding.evidence.get("activation_state") != "conditional":
+        return 0
+    if finding.rule_id == "BH2" and finding.evidence.get("proof_status") == "closed":
+        return 51
+    if finding.rule_id == "BH3":
+        return 51
+    return 0
+
+
+def reference_risk_score(
+    findings: Sequence[ReferenceFinding],
+    has_executable_scripts: bool,
+    component_metadata: Sequence[Mapping[str, object]] = (),
+) -> tuple[int, str, str]:
+    """Evaluate the documented scoring contract without production helpers."""
+    executable = {
+        (_source_scope(component), str(component.get("path", ""))): bool(
+            component.get("executable", False)
+        )
+        for component in component_metadata
+    }
+
+    def strength(item: ReferenceFinding) -> float:
+        severity = item.severity.upper() if item.severity else "LOW"
+        value = _POINTS.get(severity, 5) * _confidence(item.confidence)
+        if has_executable_scripts and executable.get((_source_scope(item), item.file), False):
+            value *= 1.3
+        return value
+
+    ordered = sorted(
+        findings,
+        key=lambda item: (item.rule_id or "UNKNOWN", -strength(item)),
+    )
+    occurrences: dict[str, int] = {}
+    score = 0.0
+    floor = 0
+    for finding in ordered:
+        confidence = _confidence(finding.confidence)
+        if confidence <= 0.0:
+            continue
+        floor = max(floor, _score_floor(finding))
+        rule_id = finding.rule_id or "UNKNOWN"
+        index = occurrences.get(rule_id, 0)
+        occurrences[rule_id] = index + 1
+        if index >= len(_WEIGHTS):
+            continue
+        contribution = _POINTS.get(finding.severity.upper(), 5) * _WEIGHTS[index] * confidence
+        if has_executable_scripts and executable.get((_source_scope(finding), finding.file), False):
+            contribution *= 1.3
+        score += contribution
+
+    final = min(100, max(floor, int(score)))
+    band = next(label for threshold, label in _BANDS if final >= threshold)
+    return final, band, _RECOMMENDATIONS[band]
diff --git a/tests/verification/test_safety_properties.py b/tests/verification/test_safety_properties.py
new file mode 100644
index 00000000..17704248
--- /dev/null
+++ b/tests/verification/test_safety_properties.py
@@ -0,0 +1,247 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+"""Differential and adversarial properties for the scanner's safety envelope."""
+
+from __future__ import annotations
+
+import random
+from dataclasses import replace
+
+from hypothesis import given, settings
+from hypothesis import strategies as st
+
+from skillspector.llm_analyzer_base import Batch
+from skillspector.models import Finding
+from skillspector.nodes.meta_analyzer import LLMMetaAnalyzer
+from skillspector.nodes.report import _compute_risk_score
+from tests.verification.risk_reference import ReferenceFinding, reference_risk_score
+
+SEVERITIES = ("CRITICAL", "HIGH", "MEDIUM", "LOW", "UNKNOWN")
+
+
+@st.composite
+def reference_findings(draw: st.DrawFn) -> ReferenceFinding:
+    rule_id = draw(st.sampled_from(("R1", "R2", "SC8", "BH2", "BH3", "")))
+    severity = draw(st.sampled_from(SEVERITIES))
+    confidence = draw(
+        st.floats(min_value=-1.0, max_value=2.0, allow_nan=False, allow_infinity=False)
+    )
+    file = draw(st.sampled_from(("SKILL.md", "run.py", "nested/run.py")))
+    identity = draw(st.one_of(st.none(), st.sampled_from(("scope-a", "scope-b"))))
+    evidence: dict[str, object] = {}
+    if rule_id in {"BH2", "BH3"}:
+        evidence["activation_state"] = draw(st.sampled_from(("conditional", "inactive")))
+    if rule_id == "BH2":
+        evidence["proof_status"] = draw(st.sampled_from(("closed", "open")))
+    return ReferenceFinding(
+        rule_id=rule_id,
+        severity=severity,
+        confidence=confidence,
+        file=file,
+        source_identity=identity,
+        evidence=evidence,
+    )
+
+
+def _production_finding(item: ReferenceFinding) -> Finding:
+    return Finding(
+        rule_id=item.rule_id,
+        message="verification finding",
+        severity=item.severity,
+        confidence=item.confidence,
+        file=item.file,
+        source_identity=item.source_identity,
+        source_url=item.source_url,
+        source_digest=item.source_digest,
+        evidence=dict(item.evidence),
+    )
+
+
+@given(st.lists(reference_findings(), max_size=12), st.booleans())
+@settings(max_examples=500, deadline=None)
+def test_risk_core_matches_independent_reference_model(
+    findings: list[ReferenceFinding], has_executable_scripts: bool
+) -> None:
+    metadata: list[dict[str, object]] = [
+        {
+            "path": item.file,
+            "source_identity": item.source_identity,
+            "executable": item.file.endswith(".py"),
+        }
+        for item in findings
+    ]
+    expected = reference_risk_score(findings, has_executable_scripts, metadata)
+    actual = _compute_risk_score(
+        [_production_finding(item) for item in findings], has_executable_scripts, metadata
+    )
+    assert actual == expected
+    assert 0 <= actual[0] <= 100
+
+
+@given(st.lists(reference_findings(), max_size=12), st.booleans(), st.randoms())
+@settings(max_examples=250, deadline=None)
+def test_risk_score_is_permutation_invariant(
+    findings: list[ReferenceFinding], has_executable_scripts: bool, rng: random.Random
+) -> None:
+    metadata: list[dict[str, object]] = [
+        {
+            "path": item.file,
+            "source_identity": item.source_identity,
+            "executable": item.file.endswith(".py"),
+        }
+        for item in findings
+    ]
+    shuffled = list(findings)
+    rng.shuffle(shuffled)
+    assert reference_risk_score(findings, has_executable_scripts, metadata) == reference_risk_score(
+        shuffled, has_executable_scripts, metadata
+    )
+    assert _compute_risk_score(
+        [_production_finding(item) for item in findings], has_executable_scripts, metadata
+    ) == _compute_risk_score(
+        [_production_finding(item) for item in shuffled], has_executable_scripts, metadata
+    )
+
+
+@given(st.lists(reference_findings(), max_size=12), reference_findings(), st.booleans())
+@settings(max_examples=500, deadline=None)
+def test_adding_arbitrary_evidence_cannot_lower_score(
+    findings: list[ReferenceFinding],
+    added: ReferenceFinding,
+    has_executable_scripts: bool,
+) -> None:
+    combined = [*findings, added]
+    metadata: list[dict[str, object]] = [
+        {
+            "path": item.file,
+            "source_identity": item.source_identity,
+            "executable": item.file.endswith(".py"),
+        }
+        for item in combined
+    ]
+
+    reference_before = reference_risk_score(findings, has_executable_scripts, metadata)[0]
+    reference_after = reference_risk_score(combined, has_executable_scripts, metadata)[0]
+    production_before = _compute_risk_score(
+        [_production_finding(item) for item in findings], has_executable_scripts, metadata
+    )[0]
+    production_after = _compute_risk_score(
+        [_production_finding(item) for item in combined], has_executable_scripts, metadata
+    )[0]
+
+    assert reference_after >= reference_before
+    assert production_after >= production_before
+
+
+@st.composite
+def llm_outcomes(draw: st.DrawFn) -> list[dict[str, object]]:
+    return draw(
+        st.lists(
+            st.fixed_dictionaries(
+                {
+                    "pattern_id": st.sampled_from(("R1", "R2", "INVENTED")),
+                    "is_vulnerability": st.booleans(),
+                    "confidence": st.floats(
+                        min_value=0.0, max_value=1.0, allow_nan=False, allow_infinity=False
+                    ),
+                    "start_line": st.integers(min_value=1, max_value=5),
+                    "_file": st.sampled_from(("run.py", "other.py")),
+                    "explanation": st.text(max_size=30),
+                    "remediation": st.text(max_size=30),
+                }
+            ),
+            max_size=12,
+        )
+    )
+
+
+@given(llm_outcomes())
+@settings(max_examples=500, deadline=None)
+def test_arbitrary_llm_outcomes_cannot_weaken_deterministic_findings(
+    outcomes: list[dict[str, object]],
+) -> None:
+    originals = [
+        Finding(
+            rule_id="R1",
+            message="first",
+            severity="HIGH",
+            confidence=0.82,
+            file="run.py",
+            start_line=1,
+            evidence={"origin": "deterministic"},
+        ),
+        Finding(
+            rule_id="R2",
+            message="second",
+            severity="LOW",
+            confidence=0.41,
+            file="run.py",
+            start_line=2,
+            evidence={"origin": "deterministic"},
+        ),
+    ]
+    batch = Batch(file_path="run.py", content="", findings=originals)
+    analyzer = LLMMetaAnalyzer.__new__(LLMMetaAnalyzer)
+    result = analyzer.apply_filter(originals, [(batch, outcomes)])
+    by_id = {finding.finding_id: finding for finding in result}
+
+    assert set(by_id) == {finding.finding_id for finding in originals}
+    for original in originals:
+        returned = by_id[original.finding_id]
+        assert returned.rule_id == original.rule_id
+        assert returned.severity == original.severity
+        assert returned.confidence >= original.confidence
+        assert returned.evidence == original.evidence
+
+
+def test_reference_model_detects_the_historical_equal_severity_order_bug() -> None:
+    weak = ReferenceFinding(rule_id="R1", severity="CRITICAL", confidence=0.01)
+    strong = replace(weak, confidence=1.0)
+    assert reference_risk_score([weak, strong], False) == reference_risk_score(
+        [strong, weak], False
+    )
+    assert _compute_risk_score([_production_finding(weak), _production_finding(strong)], False) == (
+        50,
+        "MEDIUM",
+        "CAUTION",
+    )
+    assert _compute_risk_score([_production_finding(strong), _production_finding(weak)], False) == (
+        50,
+        "MEDIUM",
+        "CAUTION",
+    )
+
+
+def test_higher_severity_evidence_cannot_steal_weight_from_stronger_evidence() -> None:
+    strong_high = ReferenceFinding(rule_id="R1", severity="HIGH", confidence=0.1)
+    weak_critical = ReferenceFinding(rule_id="R1", severity="CRITICAL", confidence=0.01)
+
+    before = reference_risk_score([strong_high], False)
+    after = reference_risk_score([strong_high, weak_critical], False)
+
+    assert before == (2, "LOW", "SAFE")
+    assert after == (2, "LOW", "SAFE")
+    assert _compute_risk_score([_production_finding(strong_high)], False) == before
+    assert (
+        _compute_risk_score(
+            [_production_finding(strong_high), _production_finding(weak_critical)], False
+        )
+        == after
+    )
+
+
+def test_every_integer_score_has_exactly_one_documented_band() -> None:
+    bands = ((81, "CRITICAL"), (51, "HIGH"), (21, "MEDIUM"), (0, "LOW"))
+    for score in range(101):
+        actual = next(label for threshold, label in bands if score >= threshold)
+        expected = (
+            "LOW"
+            if score <= 20
+            else "MEDIUM"
+            if score <= 50
+            else "HIGH"
+            if score <= 80
+            else "CRITICAL"
+        )
+        assert actual == expected
diff --git a/uv.lock b/uv.lock
index d79fdef4..ed90828a 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,5 +1,5 @@
 version = 1
-revision = 2
+revision = 3
 requires-python = ">=3.12, <3.15"
 resolution-markers = [
     "python_full_version >= '3.14' and sys_platform == 'win32'",
@@ -863,6 +863,78 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/d2/fd/6668e5aec43ab844de6fc74927e155a3b37bf40d7c3790e49fc0406b6578/httpx_sse-0.4.3-py3-none-any.whl", hash = "sha256:0ac1c9fe3c0afad2e0ebb25a934a59f4c7823b60792691f779fad2c5568830fc", size = 8960, upload-time = "2025-10-10T21:48:21.158Z" },
 ]
 
+[[package]]
+name = "hypothesis"
+version = "6.165.10"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "sortedcontainers" },
+]
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 version = "1.6.1"
@@ -2713,6 +2785,8 @@ dependencies = [
 dev = [
     { name = "build" },
     { name = "hatchling" },
+    { name = "hypothesis" },
+    { name = "langgraph-cli", extra = ["inmem"] },
     { name = "mcp" },
     { name = "mypy" },
     { name = "poetry" },
@@ -2722,12 +2796,12 @@ dev = [
     { name = "ruff" },
     { name = "twine" },
 ]
-mcp = [
-    { name = "mcp" },
-]
 langgraph-dev = [
     { name = "langgraph-cli", extra = ["inmem"] },
 ]
+mcp = [
+    { name = "mcp" },
+]
 
 [package.metadata]
 requires-dist = [
@@ -2735,6 +2809,7 @@ requires-dist = [
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     { name = "hatchling", marker = "extra == 'dev'", specifier = ">=1.31.0" },
     { name = "httpx", specifier = ">=0.28.0" },
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     { name = "langchain-aws", specifier = ">=0.2.0" },
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@@ -2756,8 +2831,8 @@ requires-dist = [
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-    { name = "skillspector", extras = ["mcp"], marker = "extra == 'dev'" },
     { name = "skillspector", extras = ["langgraph-dev"], marker = "extra == 'dev'" },
+    { name = "skillspector", extras = ["mcp"], marker = "extra == 'dev'" },
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     { name = "typer", specifier = ">=0.23.0,<0.24" },
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 name = "sse-starlette"
 version = "3.3.4"