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/audit-passnpx skills add melodic-software/claude-code-plugins --skill audit-passgit 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 | $0.00180 | $0.07454 |
| Opus 5 | $0.00090 | $0.03727 |
| Sonnet 5 | $0.00036 | $0.01491 |
| Haiku 4.5 | $0.00018 | $0.00745 |
Grade A, and why
audit-pass 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 — 449 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
One bounded, ordered pass over a target repository's Claude Code instruction surface, coordinated across three scopes and resumable mid-run. It adds no criteria of its own: every check is delegated to the plugin that owns it. What it contributes is what invoking those skills by hand yields none of: a three-scope inventory before any check runs, a run-time-derived exclusion set, stable finding identity, suppression memory, incremental persistence and resume, and one human gate for the pass. All of it is specified in reference/run-contract.md.
Reference index. Load on demand
| File | Load when |
|---|---|
| reference/terms.md | Before any other contract file. Every one of them uses run, target, lane, scan set, live surface and live surface set without redefining them. |
| reference/finding-identity.md | Emitting, comparing, or suppressing a finding: the (check, claim, sites) tuple, surface, anchor, and the derived finding_id. |
| reference/run-contract.md | Deciding which contract file owns a rule, or resolving a §N cross-reference from inside one. |
| reference/arguments.md | A run is invoked with an argument whose precedence, default, or refusal is not settled by the Arguments table. |
| reference/exclusion-set.md | Deriving the scan set, or justifying why a path was skipped. |
| reference/run-state-and-resumability.md | Starting, leasing, or resuming a run, and diagnosing a lock that outlived its holder. |
| reference/report-location-and-schema.md | Writing the report, or judging what --report-to may target. |
| reference/determinism-tiers.md | Assigning a finding's tier, or running the self-check's comparison. |
| reference/suppression.md | Reading or writing the target's .claude/audit-pass.md record. |
| reference/doctor-handoff.md | Reaching the /doctor handoff, whether it is present or absent. |
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.
- evals/evals.json 40 KB
- reference/arguments.md 5.9 KB
- reference/determinism-tiers.md 20 KB
- reference/doctor-handoff.md 7.1 KB
- reference/exclusion-set.md 5.8 KB
- reference/finding-identity.md 14 KB
- reference/report-location-and-schema.md 13 KB
- reference/run-contract.md 1.3 KB
- reference/run-state-and-resumability.md 27 KB
- reference/suppression.md 13 KB
- reference/terms.md 1.2 KB
- scripts/run-state.sh 31 KB runs code
- scripts/run-state.test.sh 24 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 · 449 lines · 180 tokens per session scan A 9591182f12d2
audit-pass is a skill published in the GitHub repository melodic-software/claude-code-plugins (12 stars, last pushed 2d ago), licensed MIT. It adds 180 tokens to every session and 7,454 once invoked, about $0.0009 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.