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trust-appraise-all

Use this skill when the user wants the full existing-registry Trust Magnitude inspector: understand why a named Gaia skill has a particular star rank, what its Trust Magnitude (TM) score is, which evidence rows are contributing (or dead), how far it is from the next grade, or what evidence to add to move the needle. Also use when asked for a full leaderboard of all named skills ranked by TM. Triggers: "why does X have 3 stars", "what's the TM for X", "inspect TM", "explain the score for X", "what evidence would rank up X", "show leaderboard", "rank all skills by TM", "what's dragging down X's score", "dead evidence rows", "how close is X to grade A/S".

gaia-research/gaia-skill-tree

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含まれるファイル（1）

SKILL.md5.7 KB

SKILL.md（原文）

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/trust-appraise-all

Breaks down the Trust Magnitude (TM) score for any named Gaia skill, or produces a full ranked leaderboard. TM is the composite evidence score that gates star promotions — understanding it tells you exactly why a skill is ranked where it is and what would change that.

Two Modes

Inspect Mode — single skill deep-dive

/trust-appraise-all <skillId>

<skillId> is the contributor/name path (e.g. garrytan/gstack, mattpocock/grill-me, obra/superpowers).

Output includes:

Each evidence row with the full score chain: base magnitude → × type weight → × freshness → × mothership/creator/engagement → × inheritMultiplier → × plateau → final score
Dead rows (score = 0.0) and why (missing views, stars below threshold, deranked verifier, etc.)
Fusion-recipe summary if suiteComponents is present: component count, graded origins, Fusion Score (informational, 0 TM)
Total TM, overall grade, and exact points to the next grade threshold
Most efficient evidence type to add for the next TM jump

Leaderboard Mode — all skills ranked

/trust-appraise-all --leaderboard

Full ranked table grouped by grade band:

Band	TM Threshold
S	>= 250
A	>= 100
B	>= 50
C	>= 20
Ungraded	< 20

Columns: Rank, Skill ID, TM, Grade, Level, Contributor.

How to Run It

# Inspect a single skill
GAIA_OPERATOR_OVERRIDE=1 python scripts/inspectTrustMagnitude.py --skill <skillId>

# Full leaderboard
GAIA_OPERATOR_OVERRIDE=1 python scripts/inspectTrustMagnitude.py --leaderboard

The script loads named skills from registry/named/, resolves fusion recipes from registry/nodes/, and calls explainTrustMagnitude() in src/gaia_cli/trustMagnitude.py.

Agent Instructions

For /trust-appraise-all <skillId>:

Run the inspect command for the requested skill.
Present a concise table of evidence rows: type, source, and final score. Group live rows above dead rows.
Call out dead rows and explain the specific reason each one contributes 0 (e.g. "social-signal: 820 views — below 1k floor").
State the next-grade gap plainly: "needs X more TM to reach grade Y (currently Z)".
Recommend the single highest-impact evidence type to add, with reasoning (e.g. "one peer-review with 3 reviewers adds ~90 TM — more than doubling current score").

For /trust-appraise-all --leaderboard:

Run the leaderboard command.
Present the output grouped by grade band.
Flag any skills with many evidence rows but low TM — dead rows are likely culprits worth a follow-up inspect.

Trust Magnitude Formula Reference

artifact_score = magnitude × type_weight × freshness × mothership × creator × engagement
TM = Σ(artifact_scores), with social-signal capped at 80 across all rows

Per-type magnitude formulas

Type	Magnitude Formula	Weight	Cap
fusion-recipe	— (never scores)	0	0
github-stars-own	min(175.0, 35.0 × log10(stars/10)) (stars > 10)	1.0	175
proxy-containment	(externalStars/1000)×0.8 (min 10k stars)	1.0	160
verifier-attestation	30 × verifiers	1.5	—
benchmark-result	percentile (field required — omitting it scores 0)	1.4	100
arxiv	citations / 5	1.0	100
peer-review	25 × reviewers	1.2	—
repo-own	commits/200 + contributors²×2	0.6	60
self-attestation	10	0.5	10
social-signal	log10(views)×8 (min 1k views)	1.0	80 (sum cap)

fusion-recipe scores 0 TM. Under Yggdrasil III, structure is reported by a separate, informational scalar — Fusion Score — never by Trust Magnitude. Over N distinct non-variant nodes in the resolved structural closure: FS = 0 when N = 0; FS = 20 × N when 1 ≤ N ≤ 10; FS = 200 + 20 × √(N − 10) when N > 10. Fusion Score gates no star, no rank, and no Trust Grade. Whenever you report one, also state that published TM fell for suites and fusions because this row went to 0 TM — no evidence row, star count, or rank was edited (META.md §2.1e).

Grade thresholds

Grade	TM Floor	Additional Gates
S	250	distinctTypes >= 3 AND independent witness (`benchmark-result`, `verifier-attestation`, or Grade A `peer-review`)
A	100	—
B	50	—
C	20	—
Ungraded	< 20	—

Common dead-row causes to watch for

social-signal with views < 1000 → scores 0 regardless of type weight
Suite component shared repository evidence is capped at 50.0 TM per component across github-stars-own and repo-own (#1705).
github-stars-own and repo-own pointing to the same URL → deduped; only the higher score counts
benchmark-result missing percentile field → magnitude = 0
Large suite with github-stars-own at the repo root → per-skill contribution tiny due to / skill_count_in_repo divisor; use social-signal or peer-review instead

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