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.
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.
curl -O https://raw.githubusercontent.com/kunchenguid/no-mistakes/main/.agents/skills/eval-corpus/SKILL.mdgit clone --depth 1 https://github.com/kunchenguid/no-mistakesWrote 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.
[](https://agentmods.dev/skills/kunchenguid/no-mistakes/eval-corpus)<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.
<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>- NVIDIA SkillSpector warn
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.
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.
| Model | Per session | Once 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 |
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.
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_sizeinconfig.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.goproxyEnvKeys), so an env-gated corpus silently stops collecting after an update. The keys are global-only -Mergecopies them straight fromGlobalConfig, and anevalblock in a repo's.no-mistakes.yamlis ignored. - Provenance is unrecoverable:
executor.gowrites it with the review round or never. A round recorded withcapture_provenanceoff 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 withevalAutoCaptureTimeoutoff the run context, serializes runs onevalCaptureMu(shared pool + registry), and logs rather than propagates.ErrNoCapturableReviewseparates "nothing to freeze" (DEBUG) from a real fault (WARN). Automatic and manual capture call the sameeval.Capture. A merged PR also best-effort relabels already-captured cases viaRunManager.relabelEvalRun(same mutex/timeout);eval relabelis 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-userfinding 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-opfindings are never labeled; unmatched candidate findings stay queued - never inferred as false positives - and a confirmed post-PR miss ingested viaeval miss ingestis also false-negative gold (recorded-post-pr-miss). Owner:internal/eval(goldFromRound,hasRecordedDecision,IngestPostPRMiss,ScoreCandidate); user-facing language isdocs/src/content/docs/reference/eval.md. diversifiedis gold-only and pinned (empty gold -> empty set +eval setswarning, never unlabeled fill). Those pins are the held-out official set; leftover labeled cases aretune. ListCases trims pins to the liveeval.diversified_sizecap (at most one per stratum when reconciling to 0 or a lower cap);RefreshDiversifiedis only for an explicit rebuild. Never fit matcher thresholds or review prompts ondiversified. 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*, CLITestEvalCaptureAndSetsSpeakInFindingGoldTerms,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 byrefs/no-mistakes/eval/<caseID>/{head,source-head,base,trusted-config}; the marginal case costs ~8 KB.Store.Pruneappliesmax_casesoldest-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, CLITestEvalCaptureSetsReportAndRelabelAreIdempotentAtTheCLI): 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. Theeval setsandeval rundashboards render ininternal/cli/eval_render.go, sharing the stats box idioms (renderTitledBox); the diversified headline's instant self-score isSelfScoreRecordedReviewsscoring each case's recorded review against its own gold. - Regressions:
TestCaptureDoesNotCopyRepositoryHistoryPerCase,TestPruneBoundsTheCorpusOldestFirstAndKeepsEvaluatedCases,TestDropCaseObjectsReleasesOnlyItsOwnPins,TestAutoCaptureEvalCase*(internal/daemon),TestEvalDefaultsCollectWithoutSetup,TestRepoConfigCannotChangeEvalCollection, e2eTestEvalAutoCaptureJourney.
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.
- 9d ago First seen · 20 lines · 29 tokens per session scan A 0599079a671f
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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