Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add jeffrigby/somepulp-agents/plugin install codebase-healthWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/jeffrigby/somepulp-agents/deep-audit)<a href="https://agentmods.dev/skills/jeffrigby/somepulp-agents/deep-audit"><img src="https://agentmods.dev/badge/skills/jeffrigby/somepulp-agents/deep-audit/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/jeffrigby/somepulp-agents/deep-audit"><img src="https://agentmods.dev/badge/skills/jeffrigby/somepulp-agents/deep-audit.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What 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 5.1 | $0.00032 | $0.02103 |
| Opus 5.5 | $0.00013 | $0.00841 |
| Sonnet 5.5 | $0.00006 | $0.00421 |
| Haiku 4.5 | $0.00003 | $0.00210 |
Grade A, and why
deep-audit scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 191 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deep Audit (Orchestrator)
Run a comprehensive, on-demand codebase audit by inspecting the project, deciding which specialist agents apply, launching them, and aggregating their findings into a single dated report.
This is resource-intensive and should only run when explicitly requested. For quick checks on recent changes, just ask Claude to review the diff.
Aspects requested (optional): "$ARGUMENTS"
Audit Aspects
- security →
security-auditor(secrets, injection, XSS, weak crypto, CVEs) - perf →
performance-analyzer(algorithms, N+1, async, memory, bundles) - deps →
library-modernizer(custom code → mature lib, deprecated APIs,@typesduplication) - quality →
code-quality-reviewer(smells, complexity, duplication, error handling) - dead →
dead-code-cleanupin detect-only mode (unused imports/exports/files/deps, with verification) - all → run every applicable specialist (default if no aspects given)
The launch mode is parallel by default — /deep-audit produces a batch report, so there's no reason to wait. All selected specialists are launched at once via a single message with multiple Agent calls. Append the literal token sequential to fall back to one-at-a-time execution (useful when debugging a specialist or when a transcript is easier to read serially).
Workflow
1. Parse arguments
Split $ARGUMENTS on whitespace. Tokens that match an aspect (security, perf, deps, quality, dead, all) select that specialist. The token sequential switches launch mode to one-at-a-time. Anything else is treated as a free-form scope hint to pass into each specialist (e.g., a path, glob, or "src/api only").
If no aspects are given, treat it as all.
2. Pre-analysis (orchestrator does this directly)
Read the project skeleton so each specialist gets a useful brief:
- Tech stack: presence of
package.json,tsconfig.json,requirements.txt,pyproject.toml,go.mod,Cargo.toml, etc. - Project conventions: read
CLAUDE.mdand top-levelREADME.mdif present. - Existing baselines: if a lint/typecheck script exists in
package.json, run it once and capture errors as a baseline (don't re-flag those). - Inventory: a Glob count of code files by extension to gauge audit size.
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.
- 1mo ago First seen · 191 lines · 32 tokens per session scan A 681c8dac4114
deep-audit is a skill published in the GitHub repository jeffrigby/somepulp-agents (8 stars, last pushed 1mo ago), licensed MIT. It adds 32 tokens to every session and 2,103 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-09-09.
Other skills, from other repositories
audit
Project health audit and health check — architecture, performance, tests, dependencies, code quality. Use when assessing overall project health, before releases, or after refactors.
anti-patterns
Catalogue of known SDLC anti-patterns that greatcto agents must actively reject when reviewing architecture, plans, code, or post-mortems. Used by architect (pre-impl), pm (planning), senior-dev (impl), l3-support (post-incident).
skeptical-triage
Reusable 3-round self-challenge + arbiter pattern for filtering false positives from findings/verdicts. Use when the cost of a false-positive gate block exceeds the cost of 4 extra LLM turns.
roslyn-query
Query .NET/C# codebases using Roslyn AST analysis via dotnet run file. Use for tracing data flow, auditing API usage, finding pattern violations, or ad-hoc codebase queries.
apex-review
Cross-cutting review of recent work — catches gaps between specialists. Use when asked to "review what we built", "check the work", "pre-launch review", or after completing a significant chunk of work.
shipyard-debugging
Investigates root cause systematically before any fix is proposed. Use when encountering any bug, test failure, unexpected behavior, or error message. Also use for tracebacks, exceptions, "not working" complaints, build failures, performance problems, or integration issues, and whenever the next step would be to…