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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.

The English summary is being prepared. Showing the original description.

gaia-research/gaia-skill-tree
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SKILL.md (original)

Review the instructions the agent will receive before you install.

/trust-appraise

Runs a non-mutating Trust Magnitude dry run. Formulas, dedup, inheritance, grade floors and star gates are not restated here. The canonical source is docs/agents/curation-authority-map.md, which is checked against src/gaia_cli/trustMagnitude.py by tests/test_curation_authority_map.py.

Two modes:

  1. Registry mode (--skill) appraises an already-curated skill. A contributor/skill id reads registry/named/<contributor>/<skill>.md and resolves its genericSkillRef against registry/nodes/. A bare id reads the generic node. Use it during L4 human review to see the real TM and per-row artifact scores before signing off.
  2. Proposal mode (--repo) appraises a proposed suite from live GitHub signals before curation. It is the pre-curation companion to /trust-appraise-all.

Usage

# Appraise one or more curated skills (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

Scope labels: always report them

Every result carries scope, genericResolution and totalStatus (authority map §2):

ModescopegenericResolutiontotalStatus
--repo proposalnamed-own/proposalnot evaluatedpartial
--skill named, generic ref resolveseffective namedresolvedfinal
--skill named, generic ref missing from the registrynamed-ownnot evaluatedpartial
--skill named with no genericSkillRefeffective namednot applicablefinal
--skill generic nodegenericnot applicablefinal

A partial total is never the effective named TM. Say "generic: not evaluated", not "inheritance disallowed". Generic-layer evidence that a skill later inherits still counts, even when it does not prove implementation usage.

Proposal-mode metrics and their status

The proposal builds a temporary skill from its own rows only:

  • github-stars-own at the evidence blob URL
  • repo-own at the repository root
  • a structural fusion-recipe row, which scores 0 TM and feeds only the informational Fusion Score

skillCountInRepo is recorded and never divides a score. Each collected metric is reported in metricClaims with a verified / reported / inferred / unknown status and its repro command (authority map §5):

  • contributorContributionSum is the sum of GitHub per-contributor contributions. It is not a count of unique commits. The engine's repo-own input field is named commits, so the proposal fills that input from the sum and labels it inferred. Never report it as "N commits".
  • Repository metrics describe the whole repository. Skill-file installability (a verified links.github blob/ URL) is a separate check.

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, never to bypass review. arxiv rows with 0 citations score 0 TM whatever their trustNumber. social-signal rows need verifiable view counts, and fabricated numbers inflate scores directly. 4★ strictly requires grade A (TM ≥ 100), and grade S (5★) needs an independent witness. Both rules are in authority map §3.

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