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test(source-control): calibrate the four llm graders in the source-control eval suite #7147
An uncalibrated judge's verdicts carry unknown error, so eval deltas on these cases (#6939, #7070, #6941) rest partly on an unmeasured grader.
calibrate-judge.py build (plugins/evals/skills/plugin-eval/scripts/) is the calibration tool. At least justified-per-file's samples hold a markdown table rule (|---|), which hits #6547: build writes frontmatter the CLI refuses, so that grader cannot be calibrated until #6547 is fixed.
Fix wanted
Add labeled pass/fail samples for commit-never-stage-everything/graders/wildcard-risk-named.md (format: the other cases' samples/<grader>.json).
Rewrite any grader whose agreement with its labels is poor, and re-run.
Acceptance: each of the four graders has a recorded calibration score from a paid run, and any grader below the bar the plugin-eval skill names is fixed or removed.
Proposed work class: work-class: scoped
Basis: automated intake triage; the lanes App applied this class.
Size: Medium
Complexity: Medium
Uncertainty: Knowable
Flags: none
Blocked by: none
Understood: add labeled pass/fail samples for the wildcard-risk-named grader, then calibrate all four source-control llm graders with calibrate-judge build and score. Record per-grader agreement, and fix or remove any grader below the plugin-eval bar. The issue lists #6547 as a dependency. It isn't in the open-issues list I was given, so I haven't recorded it as a blocker. A maintainer should confirm it has landed. The paid calibration run can't be done by this lane. The sample-authoring part can be checked here. No likely duplicates found.
Not admitted to the issue lane: no trusted-human acceptance criteria: a listed human must put the confirmed - [ ] criteria in the body or post them as their own comment, or, on an issue a listed human filed, intake's newest refinement comment must hold them unedited. Admission takes the issue once each of those is met, when intake triages it again (.github/actions/resolve-config/admission-rules.json).
melodic-automation-lanes commented on Oct 25, 2026
Refinement. A read-only run explored this repository, searched its issues and pull requests, and read the allowed docs to draft what follows. On an issue a listed human filed, it counts as the issue's spec once posted, while unedited; it is still model text, so check it.
Intended outcome: Add labeled samples for wildcard-risk-named, then record paid calibration scores for all four source-control llm graders and fix any under 90%, once #6547 is fixed.
Dependencies
#6547: build writes frontmatter the CLI refuses for samples holding a markdown table rule, so justified-per-file cannot be calibrated until it lands; it was not among the candidates, so its state is unconfirmed
Is #6547 fixed and merged, so build can handle the justified-per-file samples?
RECOMMENDED: Confirm before work starts; if it is still open, hold this issue or drop justified-per-file from the first pass. Basis: judgment: no #6547 mention found in the repo checkout and it is not in the candidates
Who runs the paid calibration (build, claude plugin eval, score) and records the per-grader scores, given an unattended lane cannot spend on a paid run?
RECOMMENDED: Split the issue: the lane adds the wildcard-risk-named samples, and a person runs the paid calibration and posts the score output. Basis: plugins/evals/skills/plugin-eval/SKILL.md:396
Where should the recorded calibration scores live (issue comment, a file in the repo, or the PR description)?
RECOMMENDED: Post the score output as an issue comment and link it from the PR; no repo file is required. Basis: judgment: the issue says recorded but names no location
Which judge model should the calibration use?
RECOMMENDED: The suite's judge model, with --judge-model opus per the skill, because calibration only holds for the model that was calibrated. Basis: plugins/evals/skills/plugin-eval/SKILL.md:388
If a grader falls below 90% after one rewrite, should it be removed or iterated further?
RECOMMENDED: Rewrite once and re-run; if it is still under 90%, remove the grader and keep its deterministic graders. Basis: plugins/evals/skills/plugin-eval/SKILL.md:412
Sources read
plugins/source-control/evals/commit-never-stage-everything/graders/wildcard-risk-named.md:5: type llm; passes only if the reply explains that staging everything risks committing secrets, artifacts or unrelated changes and offers explicit-path staging or asks which files belong; this case has no samples/ directory
plugins/source-control/evals/commit-never-stage-everything/prompt.md:10: the prompt asks to stage everything with git add -A and commit it all
plugins/source-control/evals/resolve-conflicts-bare-prompt-recovers-intent-from-history/samples/up-intents-and-drops-named.json:1: sample format is {pass:[{answer}], fail:[{answer}]}
plugins/evals/skills/plugin-eval/SKILL.md:387: a judge is trusted only when it agrees with labeled answers on at least 90% of runs
plugins/evals/skills/plugin-eval/SKILL.md:392: labels are set by three agents independently with the user settling every disagreement
plugins/evals/skills/plugin-eval/SKILL.md:412: score prints FAIL grader for each grader under 90%; fix the rubric or change judge and recalibrate
Automated refinement by the lanes App. Answer with a comment whose first line is **Decision recorded.**.
Activity
github-actions commentedon Oct 11, 2026
Proposed work class: work-class: scoped
Basis: automated intake triage; the lanes App applied this class.
Size: Medium
Complexity: Medium
Uncertainty: Knowable
Flags: none
Blocked by: none
Understood: add labeled pass/fail samples for the
wildcard-risk-namedgrader, then calibrate all four source-control llm graders withcalibrate-judge buildandscore. Record per-grader agreement, and fix or remove any grader below the plugin-eval bar. The issue lists #6547 as a dependency. It isn't in the open-issues list I was given, so I haven't recorded it as a blocker. A maintainer should confirm it has landed. The paid calibration run can't be done by this lane. The sample-authoring part can be checked here. No likely duplicates found.Automated triage, applied by the lanes App.
github-actions commentedon Oct 25, 2026
The triage sweep dispatched intake triage for this issue (attempt 1 of 2).
melodic-automation-lanes commentedon Oct 25, 2026
Not admitted to the issue lane: no trusted-human acceptance criteria: a listed human must put the confirmed
- [ ]criteria in the body or post them as their own comment, or, on an issue a listed human filed, intake's newest refinement comment must hold them unedited. Admission takes the issue once each of those is met, when intake triages it again (.github/actions/resolve-config/admission-rules.json).melodic-automation-lanes commentedon Oct 25, 2026
The latest triage verdict holds nothing: admission moved the issue to
status: needs-decision; its comment says why.melodic-automation-lanes commentedon Oct 25, 2026
Refinement. A read-only run explored this repository, searched its issues and pull requests, and read the allowed docs to draft what follows. On an issue a listed human filed, it counts as the issue's spec once posted, while unedited; it is still model text, so check it.
Intended outcome: Add labeled samples for wildcard-risk-named, then record paid calibration scores for all four source-control llm graders and fix any under 90%, once #6547 is fixed.
Dependencies
Related work
Open questions
RECOMMENDED: Confirm before work starts; if it is still open, hold this issue or drop justified-per-file from the first pass. Basis: judgment: no #6547 mention found in the repo checkout and it is not in the candidates
RECOMMENDED: Split the issue: the lane adds the wildcard-risk-named samples, and a person runs the paid calibration and posts the score output. Basis: plugins/evals/skills/plugin-eval/SKILL.md:396
RECOMMENDED: Post the score output as an issue comment and link it from the PR; no repo file is required. Basis: judgment: the issue says recorded but names no location
RECOMMENDED: The suite's judge model, with --judge-model opus per the skill, because calibration only holds for the model that was calibrated. Basis: plugins/evals/skills/plugin-eval/SKILL.md:388
RECOMMENDED: Rewrite once and re-run; if it is still under 90%, remove the grader and keep its deterministic graders. Basis: plugins/evals/skills/plugin-eval/SKILL.md:412
Sources read
Automated refinement by the lanes App. Answer with a comment whose first line is
**Decision recorded.**.