/gaia-tm-inspect

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

/gaia-tm-inspect <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

/gaia-tm-inspect --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 /gaia-tm-inspect <skillId>:

1. Run the inspect command for the requested skill.
2. Present a concise table of evidence rows: type, source, and final score. Group live rows above dead rows.
3. Call out dead rows and explain the specific reason each one contributes 0 (e.g. "social-signal: 820 views — below 1k floor").
4. State the next-grade gap plainly: "needs X more TM to reach grade Y (currently Z)".
5. 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 /gaia-tm-inspect --leaderboard:

1. Run the leaderboard command.
2. Present the output grouped by grade band.
3. 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