本文へ移動
cccskills
無料GitHub で公開

ev-collection

Phase 1 of the Gaia evidence verification pipeline. Use this skill whenever you need to gather, consolidate, or refresh raw evidence for named skills in the registry. Trigger phrases: "collect evidence", "populate the data lake", "gather sources", "run ev-collection", "Phase 1", "compile the evidence index", "refresh the unified lake", "aggregate skill evidence", "build the evidence database". Also invoke as the first step before running ev-star-verification, ev-adversarial-audit, or ev-link-validation — nothing downstream is meaningful without a fresh, compiled index. Covers GitHub repo links, stargazer signals, YouTube showcases, arXiv papers, peer reviews, benchmark results, blog/newsletter posts, and self-attestation artifacts.

インストール方法を見る

含まれるファイル(1)

  • SKILL.md5.6 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

Evidence Collection (ev-collection)

Phase 1 materializes and compiles the evidence lake. It is the foundation for star verification, adversarial audit, and link validation.

Type-First Evidence Lake Contract (#1148)

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

Multi-Target Peer-Review Source Packets (#1418)

If one real peer-review source URL covers multiple named skills, materialize a temporary peer-review partition before compile instead of cloning intake rows or editing canonical evidence artifacts:

python evidence/scripts/peer_review_source_packets.py \
  --manifest /path/to/manifest.json \
  --by-type-dir /tmp/ev1418/by-type

This helper is peer-review-only, writes scratch peer-review.md, and emits one row per reviewed skill even when the same URL legitimately appears under each target. Reject strength/scoring fields anywhere in the packet (trustNumber, grade, class, tier, level, stars, rank). Do not commit scratch manifests or generated partitions without human approval.

What Phase 1 does

  1. Reads active source inputs, collector channels, and named-skill evidence rows.
  2. Normalizes evidence row types, including legacy aliases such as repo → repo-own and github-stars → github-stars-own.
  3. Writes deterministic primary partitions under evidence/by-type/.
  4. Compiles evidence/unified_evidence_lake.md from by-type partitions.
  5. May emit evidence/tier_*.md only for coexistence with older scripts.

Commands

python evidence/scripts/generate_source_dump.py \
  --output-dir evidence \
  --by-type-dir evidence/by-type

python evidence/scripts/compile_data_lake.py \
  --sources evidence/by-type \
  --lake evidence

For deterministic temp-dir validation or tests, add --skip-live-stars and --no-legacy-tiers to generate_source_dump.py. Do not commit generated lake artifacts unless a human gate explicitly approves them.

Pre-registry intake candidates (#1786)

A skill proposed by an intake isn't in registry/named/ yet, so the default Phase 1 read (registry markdown + gaia.json) never sees its rows — there was previously no way to run Phases 1-4 on exactly the rows an L4-approved intake is meant to gate. Feed a candidate manifest in alongside the registry:

python evidence/scripts/generate_source_dump.py \
  --output-dir evidence \
  --by-type-dir evidence/by-type \
  --candidate-manifest /tmp/intake-candidates.json

Manifest shape — a JSON (or .jsonl) file, one of:

  • an object with a candidates / entries / rows list,
  • a bare JSON array,
  • JSONL records (one JSON object per line, .jsonl extension).

Each entry is a skill-shaped dict, the same shape a named skill's own frontmatter carries:

{
  "id": "contributor/candidate-skill",
  "name": "Candidate Skill",
  "contributor": "contributor",
  "genericSkillRef": "generic-skill-id",
  "evidence": [
    {"type": "repo-own", "source": "https://github.com/contributor/tool", "date": "2026-09-01", "notes": "..."}
  ]
}

id is required; genericSkillRef is optional (only needed to inherit generic-level evidence rows). Candidate rows merge into the same by-type partitions, deduped and normalized identically to a registered skill's own evidence — they are visually indistinguishable in the output except by id. A candidate with no level field lands in the default tier bucket; that is cosmetic bucketing only and confers no rank.

To validate a candidate's URLs before or without a full Phase 1 run, evidence/scripts/validate_sources.py --manifest <path> accepts the identical manifest shape — see /ev-link-validation.

Canonical source URL by evidence type (#1787)

Each evidence type has one canonical URL shape; using the wrong shape breaks that type's scoring inputs or collides with same-source dedup unexpectedly. See docs/agents/curation-guidelines.md §5 for the full table (repo-own, github-stars-own, proxy-containment, verifier-attestation, benchmark-result, arxiv, peer-review, self-attestation, social-signal, npm-downloads, fusion-recipe). Note: same-source dedup is (type, url)-keyed — two rows collapse only when they share BOTH the same type AND the same canonical URL; a repo-own row and a github-stars-own row at the identical URL are different keys and both count independently.

Completion Criteria

  • evidence/by-type/<type>.md is current and is the primary downstream input.
  • evidence/unified_evidence_lake.md was compiled from by-type files, not stale tier files.
  • Collector rows are not re-parsed by default when by-type files exist, avoiding duplicate ingestion from coexistence dual-writes.

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

ci-churn

無料

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。

gaia-research/gaia-skill-tree232026年10月13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.

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。

gaia-research/gaia-skill-tree232026年10月13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".

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。

gaia-research/gaia-skill-tree232026年10月13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".

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。

gaia-research/gaia-skill-tree232026年10月13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.

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。

gaia-research/gaia-skill-tree232026年10月13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.

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。

gaia-research/gaia-skill-tree232026年10月13日 更新

gaia-research のスキルをすべて見る

このスキルの問題を報告する