| name | trust-appraise-all |
| description | 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".
|
| version | 1.1.0 |
| genericSkillRef | registry-inspection |
/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, raw fusion magnitude
- 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
GAIA_OPERATOR_OVERRIDE=1 python scripts/inspectTrustMagnitude.py --skill <skillId>
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 | 20×N (N≤10); 200+20√(N-10) (N>10) | 1.5 | — |
| github-stars-own | stars / 1000 | 1.0 | 200 |
| 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) |
Grade thresholds
| Grade | TM Floor | Additional Gates |
|---|
| S | 250 | distinctTypes >= 3 AND has non-self-producible evidence |
| 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 weightgithub-stars-own and repo-own pointing to the same URL → deduped; only the higher score countsbenchmark-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
