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trust-appraise

Dry-run Trust Magnitude for proposed named skills or suites before curation. Use when deciding whether a proposed suite deserves A/S treatment, when checking Fusion Score structural inputs, when comparing repo stars vs suite component counts, or when asked to appraise a candidate before adding it to the registry.

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含まれるファイル(1)

  • SKILL.md3.0 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。

/trust-appraise

Runs a non-mutating Trust Magnitude dry run. Works in two modes:

  1. Registry node mode (--skill) — appraise an already-curated skill using its live evidence from registry/nodes/. Use this during L4 human review to surface real TM and per-row artifact scores before signing off.
  2. Suite proposal mode (--repo) — appraise a proposed suite from live GitHub signals before curation. This is the pre-curation companion to /trust-appraise-all.

Usage

# Appraise one or more curated registry nodes (use during L4 human review)
PYTHONPATH=src python3 scripts/trust_appraise.py \
  --skill rico-favor/implement-with-discernment \
  --skill caioribeiroclw-pixel/evidence-attestation

# Appraise a proposed suite (pre-curation)
PYTHONPATH=src python3 scripts/trust_appraise.py \
  --repo gsd-build/get-shit-done \
  --components 5 \
  --evidence-path docs/INVENTORY.md

# Default suite appraisal set
PYTHONPATH=src python3 scripts/trust_appraise.py

# Machine-readable output
PYTHONPATH=src python3 scripts/trust_appraise.py --skill foo/bar --json

What it scores

Registry node mode (--skill): reads registry/nodes/ directly and calls the same computeTrustMagnitude + computeRowArtifactScores used by the live registry. Shows per-row artifact scores so you can see exactly which evidence entries are contributing TM and which are scoring 0.

Suite proposal mode (--repo): builds a temporary skill object combining live GitHub signals:

  • github-stars-own scored under logarithmic diminishing-returns adoption (min(175.0, 35.0 * log10(stars/10.0)) capped at 175 TM; #1705), with skillCountInRepo so mothership discount applies.
  • repo-own from live GitHub contributor and contribution counts.
  • fusion-recipe from the proposed curated component count — structural only, contributing 0 TM. It feeds the informational Fusion Score, never Trust Magnitude (META.md §2.1e).
  • Component repository evidence is bounded to 50.0 TM per suite component to prevent sub-suite cap evasion. Grade S (5★) requires an independent witness row (benchmark-result, verifier-attestation, or Grade A peer-review) and cannot be reached by repository stars alone.

Important caveat

A high dry-run score is not promotion approval. Use this skill to surface evidence quality and TM bias before L4 human review, not to bypass review. In particular: arxiv entries with 0 citations score 0 TM regardless of trustNumber; social-signal entries require verifiable view counts — fabricated numbers directly inflate scores.

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。

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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。

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

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