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 skills add jdanigo/hydraia --skill production-auditgit clone --depth 1 https://github.com/jdanigo/hydraiaWrote 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/jdanigo/hydraia/production-audit)<a href="https://agentmods.dev/skills/jdanigo/hydraia/production-audit"><img src="https://agentmods.dev/badge/skills/jdanigo/hydraia/production-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/jdanigo/hydraia/production-audit"><img src="https://agentmods.dev/badge/skills/jdanigo/hydraia/production-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 | $0.00041 | $0.01666 |
| Opus 5 | $0.00020 | $0.00833 |
| Sonnet 5 | $0.00008 | $0.00333 |
| Haiku 4.5 | $0.00004 | $0.00167 |
Grade A, and why
production-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 8d 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.
This is a copy
98% identical to production-audit — 3 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 208 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Production Audit
Use this skill when the user asks whether an application is ready to ship, what could break in production, or what must be fixed before a launch. This is a maintainer-safe rewrite of the stale community production-audit idea: it keeps the useful production-readiness lens and removes unpinned external execution and third-party data sharing.
When to Use
- The user asks "is this production-ready", "what would break in prod", "what did we miss", "audit this repo", or "ready to ship?"
- A feature was merged and needs a pre-deploy or post-merge risk pass.
- A public launch, demo, customer rollout, or investor walkthrough is close.
- CI is green but the user wants production risk, not only test status.
- A deployed URL, release branch, PR, or current checkout is available for evidence gathering.
When Not to Use
- During active implementation when the right lens is line-level secure coding;
use
security-reviewfirst. - For pure libraries, templates, docs-only repos, or scaffolds unless the user wants packaging/release readiness rather than application readiness.
- When the user asks for a formal compliance audit. This skill is engineering triage, not legal, financial, medical, or regulatory certification.
- When the only available evidence is a product idea with no repo, deployment, CI, or runtime surface.
How It Works
Build the audit from local and user-authorized evidence. Do not run unpinned remote code, upload repository contents to third-party services, or call external scanners unless the user explicitly approves that specific tool and data flow.
Use this order:
- Establish the release surface.
- Read recent changes and current branch state.
- Inspect runtime, auth, data, payment, background-job, AI, and deployment boundaries that actually exist in the repo.
- Check CI, tests, migrations, environment documentation, and rollback path.
- Produce a short ship/block recommendation with specific fixes.
Evidence Checklist
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.
- 8d ago First seen · 208 lines · 41 tokens per session scan A 4b5d2bf0530c
production-audit is a skill published in the GitHub repository jdanigo/hydraia (8 stars, last pushed today), licensed MIT. It adds 41 tokens to every session and 1,666 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 98% identical to production-audit, differing in 3 lines, and is treated as a copy.
Other skills, from other repositories
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cloudflare-workers-dev-experience
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deploy
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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.
audit-round
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