Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/melodic-software/claude-code-plugins/auditnpx skills add melodic-software/claude-code-plugins --skill auditgit clone --depth 1 https://github.com/melodic-software/claude-code-pluginsWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00205 | $0.02545 |
| Opus 5 | $0.00102 | $0.01273 |
| Sonnet 5 | $0.00041 | $0.00509 |
| Haiku 4.5 | $0.00020 | $0.00254 |
Grade A, and why
audit scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 142 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pre-computed context
Current branch: !git branch --show-current 2>/dev/null || echo "unknown"
Effective config: !"${CLAUDE_SKILL_DIR}/scripts/detect.sh" --show-config >/dev/null 2>&1 && { "${CLAUDE_SKILL_DIR}/scripts/detect.sh" --show-config 2>/dev/null | head -8; :; } || echo "detector unavailable"
Purpose
Detect and remove AI-writing tells in checked-in markdown prose. Two detection layers over one
rule inventory (reference/catalog.md, distilled from Wikipedia's
"Signs of AI writing", revision-pinned, plus the catalog's "Cursor unslop additions" section
and its repo-owned, evidence-graded "Model-era additions" section of current-generation model
vocabulary):
- Deterministic:
${CLAUDE_SKILL_DIR}/scripts/detect.shruns the catalog'sv1: scriptrules. Its findings carry argued severity tiers (the detector-findings convention's crosswalk) and persist as a conforming findings file. What the relay APPLIES is narrow; what it ROUTES is not.rule-utm-paramsalone is auto-applicable, and every other rule is/ai-slop:audit fixwork — but the crosswalk now declares that ownership, so the relay hands those rows to this skill'sfixaction rather than to its cleanup route, which prefers/simplify, a code-simplification skill, and applies the rows itself when/simplifyis absent. Neither branch loads this skill's rewrite guide. The findings file is how a consumer sees them and how they reach the one surface that can rewrite them. - Judgment rubric: the catalog's
v1: rubrictells, applied by reading the prose. Rubric findings reach the human report only, never the findings file.
Both layers sit behind the catalog's policy-level quotation exemption: wording rules never scan blockquotes, double-quoted spans, or inline code spans (typography rules still do), so a document that quotes a tell to document it, and a changelog that backticks the phrase a fix removed, stay marker-free by construction.
What ships with it
13 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- context/persist-findings.md 4.8 KB
- evals/evals.json 11 KB
- evals/fixtures/em-dash-substitution.md 245 B
- evals/fixtures/fix-guarded-rewrite.md 327 B
- evals/fixtures/knowledge-cutoff-prose.md 433 B
- evals/fixtures/report-only.md 294 B
- evals/fixtures/rubric-boundary.md 346 B
- evals/fixtures/triads.md 395 B
- reference/catalog.md 55 KB
- reference/rewrite-guide.md 11 KB
- scripts/detect.sh 33 KB runs code
- scripts/detect.test.sh 56 KB runs code
- scripts/emit-findings.sh 12 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 142 lines · 205 tokens per session scan A 92f81d155493
audit is a skill published in the GitHub repository melodic-software/claude-code-plugins (12 stars, last pushed 2d ago), licensed MIT. It adds 205 tokens to every session and 2,545 once invoked, about $0.0010 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
deep-research
Conducts iterative deep research on any topic using web search, progressive exploration, and structured synthesis. Use when asked for comprehensive research, deep investigation, thorough analysis, or multi-source exploration of any topic. Triggers: research, investigate, deep dive, comprehensive analysis, explore…
error-ux
Principles and patterns for writing error messages that help users recover. Use when auditing, writing, or improving error messages in code. Triggers: error messages, user experience, error handling, exception messages, validation errors.
adversarial-patterns
Library of realistic adversarial attack vectors and anti-patterns to avoid. Contains examples of valid attacks and subtle gaming patterns to reject.
documentation-testing
Provides heuristics for identifying incomplete or broken documentation. Use when validating README setup instructions, testing onboarding flows, or auditing documentation quality. Triggers: docs, readme, onboarding, setup validation, documentation audit.
adversarial-analysis
Analyze code to identify explicit contracts, implicit usage patterns, and realistic boundary conditions. Contains concrete formulas for calculating input realism limits. Use before generating adversarial tests.
propagate-then-search
For constraint problems: eliminate impossibilities before guessing, reduce search space through inference, fail fast on contradictions.