agentsclimarketplace

Ai forge audit

Skill robcsaszar/ai-forge/skills/ai-forge-audit

Batch-evaluate all skills and agents in the repo with ai-forge-judge and render a single consolidated grade report sorted by grade (worst first) so effort is directed correctly. Use when reviewing overall skill/agent quality, finding where to invest improvement effort, or after bulk changes. Triggers are audit all skills, grade report, skill health check, where to focus, audit agents. Don't use for a single artifact — that's ai-forge-judge.From its SKILL.md

Install
npx -y skills add robcsaszar/ai-forge --skill ai-forge-audit

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

SKILL.md

5.5 KB, ~1.4k tokens by cl100k_base, as published. Nobody here has run it

AI Forge Audit

Batch-run ai-forge-judge across every skill and agent in the repo. One consolidated report — grades, top issues, priority order. Read-only: no fixes applied.

Requires the ai-forge-judge skill. If it is not available in this environment (no ai-forge-judge under any platform's skills/ root, and invoking it fails), stop: "ai-forge-judge skill not found — install it before running audit."


Workflow

Phase 1 — Discover

Discovery is platform-agnostic — scan every platform's roots, not just Claude's. Two constraints shape how:

  • Glob excludes hidden directories. A pattern naming .claude/… from the repo root matches nothing; you MUST pass path pointing inside each root.
  • Agent extensions differ by platform. Skills are always SKILL.md; agents are .md, .agent.md, or .toml.

For each root below, Glob with the listed path + pattern. A root that doesn't exist raises a Directory does not exist error — treat that as "no artifacts under this root" and continue to the next; it is not a failure. Conventions mirror the Platform Detection table in ai-forge-create/references/agents-taxonomy.md.

Skillspattern: */SKILL.md for each path: .claude/skills · .github/skills · .codex/skills · .gemini/skills · .agents/skills

Agentspath and pattern per platform:

pathpattern
.claude/agents*.md
.github/agents*.agent.md
.codex/agents*.toml
.gemini/agents*.md

The artifact name is the match's parent directory (skills) or file stem (agents). .agents/skills is a cross-tool alias for .gemini/skills — if a name appears under both, count it once (.agents/ wins). When the same skill name exists under two different platforms, report each separately, labelled by platform.

If zero artifacts found across all roots: output "No skills or agents found." and stop.

Phase 2 — Evaluate

For each discovered artifact, sequentially (do not parallelize — parallel evaluation risks context overflow and makes progress unreadable):

  1. Invoke ai-forge-judge on it.
  2. From the judge output, extract and record:
    • Artifact name
    • Type (Skill / Agent)
    • Grade (A/B/C/D/F)
    • Score (X/Y, Z%)
    • Numbered Improvements: up to 5 items (prefer compounding improvements — fixing X enables Y to score higher; deprioritize cosmetic when structural issues exist)

Emit a one-line status per artifact as each evaluation completes:

✓ ai-forge-create     B  (87/120, 73%)  [Skill]
✓ writer              A  (108/120, 90%)  [Agent]
✓ ai-forge-apply      C  (74/120, 62%)  [Skill]

If N > 10: output "About to run N evaluations — this may take several minutes. Proceed? (y/n)" and stop if denied.

If an artifact's judge run errors: record ERR and the error message; continue to the next. Do not abort.

Phase 3 — Render Report

Sort results by grade ascending: F → D → C → B → A. Worst grades appear first.

Split artifacts into two groups:

  • Needs Work: grade below B (< 80%)
  • Passing: grade B or above (≥ 80%)

Output a single markdown report:

# AI Forge Audit
_<YYYY-MM-DD> — <N> artifacts evaluated (<S> skills, <A> agents)_

## Grade Summary

| Artifact | Type | Grade | Score |
|----------|------|-------|-------|
| ai-forge-X | Skill | F | 55/120 (46%) |
| writer | Agent | C | 78/120 (65%) |
| ai-forge-Z | Skill | B | 98/120 (82%) |

## Needs Work

### ai-forge-X — F (55/120, 46%) [Skill]

1. <improvement 1 from judge>
2. <improvement 2>
3. <improvement 3>

### writer — C (78/120, 65%) [Agent]

1. <improvement 1>
2. <improvement 2>

## Passing (B+)

| Artifact | Type | Grade | Score |
|----------|------|-------|-------|
| ai-forge-Z | Skill | B | 98/120 (82%) |

## Skipped

| Artifact | Reason |
|----------|--------|
| some-dir | No SKILL.md found |

If all artifacts pass: omit the "Needs Work" section. If no artifacts pass: omit the "Passing" section. If nothing was skipped: omit the "Skipped" section.


NEVER

  • NEVER display full judge reports inline for each artifact Instead: Extract only grade, score, and top 1–5 numbered improvements per artifact. Why: Unfiltered judge output for 6+ artifacts floods the context window.

  • NEVER apply any fixes during an audit run Instead: Output the report only. Direct the user to ai-forge-apply or ai-forge-update for remediation. Why: Mixing diagnosis with treatment makes it impossible to know what the baseline was.

  • NEVER sort by name or alphabetically Instead: Sort by grade ascending (F first, A last) within each section. Why: Alphabetical sort buries the worst artifacts; the report's job is to surface where effort is needed most.

  • NEVER abort the audit when a single artifact errors Instead: Record ERR for that artifact and continue to the next. Why: A failed judge run on one artifact should not discard results already collected for others.

What ships with it: 1 file

10.5 KB alongside SKILL.md, 1 of them executable

scripts/

Keep looking

Skills are one crate of 325,949. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.