跳到正文
cccskills
免费在 GitHub 公开

gaia-audit

Focused single-target audit of one Gaia skill, named skill, or real-skill catalog item. Use this skill when: auditing a specific skill by ID or name, a skill looks overpromoted, evidence feels thin or dead, a contributor's skill seems outdated or superseded, the rank doesn't match the source quality, evidence is mapped to the wrong Evidence Type, a skill at 3★+ is missing a working GitHub link, someone asks "does this skill deserve its stars?", or you want the smallest possible correction with validation — not a bulk sweep. This is NOT for bulk curation passes (use /gaia-curate for that) or meta-level registry sweeps (use /gaia-meta-audit). It is specifically for "fix this one skill, get it right."

中文简介正在准备中。当前显示原文说明。

集成工具GitHub

实测评分 未实测

查看安装方法

包含的文件(1)

  • SKILL.md6.6 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

gaia-audit

Perform a focused, single-target audit of one Gaia skill or catalog item and apply the smallest correct source-level fix. The goal is accuracy over coverage: one skill, done properly.

Why single-target?

Bulk passes introduce unintended side effects and make diffs hard to review. A single-target audit lets you go deep — verifying the actual source material, not just the metadata — and produces a minimal, reviewable change. If you find adjacent issues while auditing, note them but do not fix them in this pass.

Workflow

1. Locate the target

Determine which registry layer owns the skill:

  • Generic (starless) canonical skill: registry/nodes/<id>.json (or via registry/gaia.json)
  • Named skill implementation: registry/named/<contributor>/<skill>.md
  • Real-skill catalog item: registry/real-skills.json

Run rg <skill-id> across the repo to find every occurrence — frontmatter references, named files, catalog entries, and timeline events — before touching anything.

2. Ask the diagnostic questions

Work through these in order. Stop when you find a violation — that's the correction to make.

  • Does the skill exist at all in the registry?
  • Is it generic (no stars) or named (2★–6★)? Generic skills have no level; only named implementations carry stars.
  • Does it map to the claimed Gaia capability? (Check CONTEXT.md for canonical vocabulary.)
  • Does the evidence actually support the claimed rank (stars)? Re-read each source URL — homepage and directory listings are weak; SKILL.md blobs, papers, benchmarks, and release notes are strong.
  • Is it outdated, superseded, or a duplicate of something already in the registry?
  • For named skills at 3★+: is there a working blob/branch/subpath GitHub link? A dead or missing link is grounds for demotion.
  • For fusion/upgrade opportunities: note if related basic skills could cleanly consolidate, but do not act on it in this pass unless the target is explicitly the consolidation candidate.
  • Does the skill have structural, documentation, or packaging issues? Classify against the Four-Tier Audit Taxonomy (GOVERNANCE.md §4.2 / RFC #1809):
    • Tier 1 (Malicious / Imposter Squatter): Bad-faith aggregation, hijacking other maintainers' repos/stars, or falsified attribution → Expungement from registry.
    • Tier 2 (Packaging / Install Shape Gap): Authentic concept and author, but missing standard packaging or broken links → For ≤2★, tag installable: false per CONTRIBUTING.md §12. For 3★+, demote to 2★ (or 1★ per Star Bar) and request repackaging. Never purge or label as an imposter.
    • Tier 3 (Early-Stage / Under-Evidenced Stub): Authentic author, thin docs or preliminary evidence → Keep at baseline 1★ (Awakened), request doc enrichment, or tag needs-info. Never purge.
    • Tier 4 (Product-Coupled / Non-Generalized): Real tool documentation needing generalization (#1766, #1801) → Enforce maker attribution via check_product_attribution.py and route to generalization.
  • Mandatory Contributor Check: If the author is a known contributor, co-founder, or team member, deletion is strictly prohibited without direct maintainer outreach and documented review.

Skip the rarity field entirely — it is deprecated and carries no review signal (see CONTEXT.md § Rarity).

3. Present findings before acting

Write out what you found and the proposed correction before making any edit. This gives the user a chance to redirect. If no correction is warranted, say so explicitly with the reason — "no action" is a valid and useful audit outcome.

4. Apply the correction via CLI

All mutations go through the CLI, not direct file edits — this preserves timeline logging and schema integrity.

# Adjust rank (named skills only — use contributor/skill format, not bare generic IDs)
gaia dev calibrate <contributor/skill_id> <N>★

# Change generic type (basic / fusion)
gaia dev reclassify <generic_id> <type>

# Update named status (awakened / named / etc.)
gaia dev update-named <contributor/skill_id> --status <status>

# Add or replace evidence
gaia dev evidence <skill_id> "<url>" --type <arxiv|repo|github-stars|...> --trust <value>

# Log a demotion event in the timeline
gaia dev timeline <skill_id> --action demote --notes "<reason>"

For dead evidence links: remove or replace the offending entry, then log a timeline event. Do not patch a demerits field — that field is being removed from the schema.

5. Regenerate and verify

gaia dev build       # sync all derivative files after registry edits
gaia dev validate
gaia dev validate --intake
gaia dev test all
git diff --check

Demotion enforcement rules

These are hard rules — they apply regardless of how prominent or well-known the skill is:

  • 3★+ without a working links.github blob URL → hard-demote to 1★ (Awakened) — a missing verified blob link is a hard reset, not a soft step-down (META.md §2.4); apply via gaia dev update-named --status awakened, then re-earn rank with a valid link
  • Unique-branch skill demoted below 4★ → it loses its Unique-branch rank status; if its structure no longer warrants fusion, reclassify the generic to basic (gaia dev reclassify accepts only basic / fusion)
  • Dead evidence links → remove/replace the entry and add a demote timeline event
  • Placeholder or non-verifiable evidence only → demote to Awakened (1★) via gaia dev update-named --status awakened
  • Suite components: the suite itself does not need links.github, but each component must have a specific blob/branch/subpath URL

Output

Conclude with a concise report covering:

  • Which skill was audited and what layer it lives in
  • Evidence reviewed (URLs checked and their quality)
  • Correction applied, or the explicit reason no change was warranted
  • CLI commands run
  • Validation results

评价

还没有评价。欢迎分享你的使用感受。

评论

无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。

还没有评论。来写第一条吧。

提交后会立即公开。请勿填写邮箱、电话等个人信息。URL 最多 2 个。

0 / 1000 字

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

同一仓库的技能

ci-churn

免费

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.

中文简介正在准备中。当前显示原文说明。

gaia-research/gaia-skill-tree232026年10月23日 更新

Produce the rendered evidence a human-gated frontend PR owes its reviewer: capture each changed surface (plus the before state for anything modified, plus mobile width), then generate one self-contained local HTML page the founder can open and review without checking anything out. Use when a PR is gated by the "Frontend changes are HUMAN-GATED" section of CLAUDE.md, or when the user says "/design-gate", "screenshot evidence for this PR", "prep this for founder review", "build the design evidence page", "what does this look like before and after". The page comes from a template script, not hand-written markup. Output is session-local and never committed. Do NOT use for non-gated changes: typo/grammar/factual fixes in an unchanged block, dead link targets, alt/ARIA fixes with no visual change, cache-bust bumps, or refactors with no rendered diff.

中文简介正在准备中。当前显示原文说明。

gaia-research/gaia-skill-tree232026年10月23日 更新

One-fix-at-a-time, approval-gated design fix recovery. Given a checklist of fixes (handover doc, CHECKLIST.md, or inline list), applies each individually, presents a diff, and waits for an explicit APPROVE / REJECT / NO CHANGES decision before committing. Trigger phrases: /design-iteration, "go through the checklist one by one", "fix these one at a time", "bucket recovery", "design fix pass".

中文简介正在准备中。当前显示原文说明。

gaia-research/gaia-skill-tree232026年10月23日 更新

Playbook for correcting a named skill's star level to match its computed Trust Magnitude grade, with an independent post-calibration verification pass and a CLI-logged timeline event (never hand-written). Use when: recalibrating a skill's level, fixing a stale or drifted star rating, running a batch of `gaia dev calibrate` corrections, or confirming a past calibration actually landed. Triggers: "calibrate <skill>", "recalibrate the trust magnitude backlog", "promote/demote <skill> to N stars", "why is <skill> still N stars", "verify a calibration actually landed", "Hall of Heroes ranking is wrong".

中文简介正在准备中。当前显示原文说明。

gaia-research/gaia-skill-tree232026年10月23日 更新

Close out a completed EPIC / sprint end-to-end: author a fact-verified meta-post announcing what shipped, archive the EPIC's handovers, snapshot orchestrator memory, gate the integration→main merge on CI-green + explicit human go, drive the version bump (merge-commit conventions that auto-sync classifies), and verify the release reached the registry/PyPI. Use when the user says "close out EPIC <n>", "wrap up the sprint", "ship the meta shift", "/epic-close", or asks to finalize an integration branch that is ready to merge to main. Generalizes the EPIC 1002 (Yggdrasil II) close-out. This skill orchestrates — it authors founder docs directly (superadmin) and delegates code/regen to workers; it NEVER merges to main without the human gate.

中文简介正在准备中。当前显示原文说明。

gaia-research/gaia-skill-tree232026年10月23日 更新

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.

中文简介正在准备中。当前显示原文说明。

gaia-research/gaia-skill-tree232026年10月23日 更新

查看 gaia-research 的全部技能

报告此技能的问题