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ev-adversarial-audit

Run this skill for Phase 3 of the evidence verification pipeline — the adversarial audit step. Use when you need to check the evidence data lake for bad data before ingestion: dead links, wrong URL formats (tree/ vs blob/), subjective wording ("elite", "high-quality"), stale migration notes, benchmark catalog misuse/vendor-claim leakage, or skills whose star evidence conflicts with classified evidence level. Triggers on phrases like: "audit the data lake", "adversarial check", "ev-adversarial-audit", "check for noise in evidence", "flag bad evidence", "run the audit phase", "quality check the by-type files", or any reference to Phase 3 of the pipeline.

gaia-research/gaia-skill-tree

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Adversarial Evidence Audit (ev-adversarial-audit)

Phase 3 audits the evidence lake from a Devil's Advocate perspective before URL health validation and ingestion.

Type-First Evidence Lake Contract (#1148)

The evidence lake is type-first. Audit evidence/by-type/<canonical-evidence-type>.md files. Legacy evidence/tier_*.md files may still exist as coexistence artifacts, but they are not the semantic routing key.

Audit Target

Split reviewer work across evidence/by-type/<type>.md files, not tier files. Suggested sharding:

Repo/adoption signals: repo-own, github-stars-own, social-signal
Technical proof: benchmark-result, arxiv, peer-review
Governance/proxy proof: proxy-containment, verifier-attestation
Composition/self proof: fusion-recipe, self-attestation

Findings to Flag

Dead or malformed URLs, including GitHub tree/ links where a blob/ source is required.
Subjective/evaluative wording not supported by the source.
Evidence type mismatches or legacy alias leakage.
Star evidence that conflicts with live verification notes from Phase 2.
Benchmark catalog misuse after Phase 2B: benchmark-result rows citing unknown, candidate, registered, rejected, or retired sources as if they score; vendor claims presented as reproducible benchmarks; scoring provenance on non-verified catalog entries; missing reproducibility fields or missing/dubious percentile values.
Stale migration notes that still treat tier_*.md as the semantic working set.
Multi-target peer-review packet misuse: wrong evidenceType, empty targets, invalid skillId, duplicate (source.url, skillId, evidenceType) rows, or forbidden strength/scoring fields (trustNumber, grade, class, tier, level, stars, rank).
Imposter over-purging & audit taxonomy conflation (RFC #1809): ensure legitimate early-stage, self-hosted, or installable: false skills (Tier 2 Packaging Gap, Tier 3 Under-Evidenced Stub) are not mislabeled as Tier 1 Malicious Imposters or flagged for deletion.

Append concise findings to the source report. Do not mutate registry files.

Optional Semantic Evidence Review Sidecar

An operator may optionally run the read-only Jev advisory runner to flag semantic anomalies in evidence rows (docs/agents/jev.md):

# Offline dry-run by default ($0 spend); add --live to query TypeSafe API
python scripts/jev_advisory.py --mode evidence --input <evidence-rows.json>

This output serves as review triage hints only. It never marks evidence rows verified, does not replace deterministic HTTP health or Phase 2B benchmark verification, does not touch Trust Magnitude scoring, and emits an actionable handoff to worker-luna on uncertainty.

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ci-churn

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Measure CI churn cost for a pull request: how many commits and how many CI-compute seconds were burned on avoidable "fix the CI" push rounds. Use when you want to understand the efficiency cost of a PR's iteration cycle, get actionable pre-push check suggestions, or include a churn summary in a pipeline report. Triggers: "ci churn", "how much CI did we burn", "avoidable commits", "ci efficiency", "wasted CI time", "churn report", "/ci-churn". Called automatically by /fp-drift at pipeline close.

日本語の概要は準備中です。原文の説明を表示しています。