no-mistakes: Skill for Claude Code

.agents/skills/eval-corpus/SKILL.md

eval-corpus is a skill for Claude Code, Codex from kunchenguid/no-mistakes. It costs 29 tokens per session (1,552 once invoked), scanned A, original, MIT.

A local system for collecting examples from coding-agent runs and storing their source information, labels, and replay data for evaluation. An evaluation corpus is a set of test cases used to measure whether software behaves correctly.

In plain words
What is it for?
Capturing evaluation cases automatically, preserving provenance, building diversified and tuning sets, matching cases, storing replays, and controlling collection through configuration settings.
Why use it?
It keeps evaluation examples tied to the review round that produced them and continues collecting after a run has finished, while limiting time and storage use.

Skill for Claude CodeCodex

Written for Claude Code: user-invocable in frontmatter. Also seen: installed under .agents/ (shared by several agents).

This is kunchenguid/no-mistakes's own configuration. It tells Claude Code and Codex how to work on no-mistakes itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything no-mistakes configures →

About the project

no-mistakes is a local Git proxy that validates changes in an isolated worktree before forwarding a push to the real remote and opening a pull request. It is for developers and coding agents that want automated checks, safe fixes, CI repair, and human review before changes are published.

kunchenguid/no-mistakes · 8,349 stars · on GitHub · kunchenguid.github.io

Reuse

Borrowing it

Nothing to install: this file belongs to kunchenguid/no-mistakes. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/kunchenguid/no-mistakes/main/.agents/skills/eval-corpus/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/kunchenguid/no-mistakes

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for eval-corpus

README.md
[![agentmods](https://agentmods.dev/badge/skills/kunchenguid/no-mistakes/eval-corpus/github.svg)](https://agentmods.dev/skills/kunchenguid/no-mistakes/eval-corpus)
Your own site
<a href="https://agentmods.dev/skills/kunchenguid/no-mistakes/eval-corpus"><img src="https://agentmods.dev/badge/skills/kunchenguid/no-mistakes/eval-corpus/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for eval-corpus

Your own site · 80×15
<a href="https://agentmods.dev/skills/kunchenguid/no-mistakes/eval-corpus"><img src="https://agentmods.dev/badge/skills/kunchenguid/no-mistakes/eval-corpus.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 29 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,552 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 1 finding, up to medium

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • medium Rogue Agent · line 11
    Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.
    Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00029 $0.01552
Opus 5 $0.00015 $0.00776
Sonnet 5 $0.00006 $0.00310
Haiku 4.5 $0.00003 $0.00155

Measured 9d ago against content hash 0599079a671f, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

eval-corpus scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.agents/skills/eval-corpus/SKILL.md · 20 lines

How it starts

The opening of the file, as written. The whole thing — 20 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Local Eval Corpus Collection (internal/eval)

  • Collection is automatic and default-on through eval.capture_provenance / eval.auto_capture / eval.max_cases / eval.diversified_size in config.yaml, never an environment variable: the daemon's launchd/systemd unit is re-rendered on install and update and preserves only proxy variables (internal/daemon/service.go proxyEnvKeys), so an env-gated corpus silently stops collecting after an update. The keys are global-only - Merge copies them straight from GlobalConfig, and an eval block in a repo's .no-mistakes.yaml is ignored.
  • Provenance is unrecoverable: executor.go writes it with the review round or never. A round recorded with capture_provenance off can never be captured, so the rejection names the setting rather than the round's age.
  • The trigger is RunManager.autoCaptureEvalCase, called last in the run goroutine after the outcome is already reported: it recovers its own panic (the enclosing recover would otherwise mark a finished run failed), bounds itself with evalAutoCaptureTimeout off the run context, serializes runs on evalCaptureMu (shared pool + registry), and logs rather than propagates. ErrNoCapturableReview separates "nothing to freeze" (DEBUG) from a real fault (WARN). Automatic and manual capture call the same eval.Capture. A merged PR also best-effort relabels already-captured cases via RunManager.relabelEvalRun (same mutex/timeout); eval relabel is the CLI path.
  • The unit of truth is finding-level gold, not park/pass, and it is keyed on the round's recorded fix-vs-skip decision plus merge state, never on whether a later round still raises the finding (a fix and a ship both make it disappear): a user-selected Fix is true-positive gold (no merge required); an auto-fix selection on a merged run is true-positive gold even if a later round re-raised or rewrote it; a raised auto-fix/ask-user finding the human did NOT select, on a merged run, is false-positive gold - deliberately reversing the older "never auto-FP from a skip" stance, because in this operator's corpus an approved-and-shipped finding IS a false positive; a human-added finding is false-negative gold; skip/approve/abort without a merge and any round with no recorded decision stay unlabeled / pending; no-op findings are never labeled; unmatched candidate findings stay queued - never inferred as false positives - and a confirmed post-PR miss ingested via eval miss ingest is also false-negative gold (recorded-post-pr-miss). Owner: internal/eval (goldFromRound, hasRecordedDecision, IngestPostPRMiss, ScoreCandidate); user-facing language is docs/src/content/docs/reference/eval.md.
  • diversified is gold-only and pinned (empty gold -> empty set + eval sets warning, never unlabeled fill). Those pins are the held-out official set; leftover labeled cases are tune. ListCases trims pins to the live eval.diversified_size cap (at most one per stratum when reconciling to 0 or a lower cap); RefreshDiversified is only for an explicit rebuild. Never fit matcher thresholds or review prompts on diversified. Report F1 as the headline metric only when false-positive gold exists; otherwise recall + precision bounds. RelabelRun recomputes derived merge labels and drops the obsolete ones. Matcher assignment is ONE globally optimal bipartite matching over all gold and candidate findings, weighted so an exact match outweighs any number of fuzzy ones; per-strength-tier greedy assignment understated recall and must not come back. Regressions: TestListCasesDiversified_*, TestGoldFromRoundLabelsByRecordedDecision, TestCaptureWritesAutoFixMergedAsTruePositive, TestCaptureWritesShippedUnfixedAsFalsePositive, TestCaptureWritesShippedUnfixedEvenWhenTheFinalRoundNoLongerRaisesIt, TestCaptureLabelsSelectedAutoFixAsTruePositiveEvenWhenLaterRoundReRaisesIt, TestRelabelReplacesShippedUnfixedWhenTheRoundLaterRecordsAFixDecision, TestMergeGoldClearsStoredShippedUnfixedWhenRecomputedUnlabeled, TestRelabelClearsStoredShippedUnfixedFPWhenRecomputedUnlabeled, TestScoreCandidateDoesNotLetFuzzyEarlierGoldStealExactLaterMatch, TestScoreCandidateRecoversMatchTheTieredMatcherLost, TestMaxWeightAssignmentMatchesBruteForceOptimum, TestEvaluationSummaryWithholdsHeadlineF1WithoutFalsePositiveGold, TestCaptureDoesNotLabelSkipOrApproveAsPass, TestCaptureWritesFalseNegativeGoldForUserAddedFinding, TestCaptureSkipsIncompleteReviewRoundAndKeepsCompletedSibling, TestIngestPostPRMissWritesFalseNegativeGoldOnGreenReview, TestCaptureAndReport*, CLI TestEvalCaptureAndSetsSpeakInFindingGoldTerms, TestEvalMissIngestLabelsFalseNegativeGold.
  • A case stores no Git bundle. Bundles were a full history copy per review pass (~8 MB each here) and cannot be trimmed, because a bundle built with negative refs records prerequisites an empty restore gate lacks. Cases of one repository instead share <NM_HOME>/eval/pools/<fingerprint>.git, pinned by refs/no-mistakes/eval/<caseID>/{head,source-head,base,trusted-config}; the marginal case costs ~8 KB. Store.Prune applies max_cases oldest-first but protects active replay reservations and cases with recorded evaluations, so the cap is a retention target rather than a hard bound.
  • Capture stays read-only against the gate, so objects reach the pool through a throwaway bare clone plus a refspec fetch - never a bare-object-id fetch, whose want policy is off by default and version-dependent.
  • Every eval subcommand is idempotent and tested so (internal/eval/idempotency_test.go, CLI TestEvalCaptureSetsReportAndRelabelAreIdempotentAtTheCLI): capture/relabel converge in place, sets reads self-stabilize their pins, and replay is additive-by-cohort but never rewrites case labels or manifests - queued unmatched-finding counts derive from the evaluations table (Store.pendingFindingCounts), never from a stored counter. The eval sets and eval run dashboards render in internal/cli/eval_render.go, sharing the stats box idioms (renderTitledBox); the diversified headline's instant self-score is SelfScoreRecordedReviews scoring each case's recorded review against its own gold.
  • Regressions: TestCaptureDoesNotCopyRepositoryHistoryPerCase, TestPruneBoundsTheCorpusOldestFirstAndKeepsEvaluatedCases, TestDropCaseObjectsReleasesOnlyItsOwnPins, TestAutoCaptureEvalCase* (internal/daemon), TestEvalDefaultsCollectWithoutSetup, TestRepoConfigCannotChangeEvalCollection, e2e TestEvalAutoCaptureJourney.

Read the full file on GitHub · 20 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 9d ago First seen · 20 lines · 29 tokens per session scan A 0599079a671f

Subscribe to this mod's changes

eval-corpus is a skill published in the GitHub repository kunchenguid/no-mistakes (8,349 stars, last pushed today), licensed MIT. It adds 29 tokens to every session and 1,552 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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