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 priyanshuchawda/codeaudit --skill ai-app-security-reviewgit clone --depth 1 https://github.com/priyanshuchawda/codeauditWrote 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/priyanshuchawda/codeaudit/ai-app-security-review)<a href="https://agentmods.dev/skills/priyanshuchawda/codeaudit/ai-app-security-review"><img src="https://agentmods.dev/badge/skills/priyanshuchawda/codeaudit/ai-app-security-review/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/priyanshuchawda/codeaudit/ai-app-security-review"><img src="https://agentmods.dev/badge/skills/priyanshuchawda/codeaudit/ai-app-security-review.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 | $0.00177 |
| Opus 5 | $0.00020 | $0.00088 |
| Sonnet 5 | $0.00008 | $0.00035 |
| Haiku 4.5 | $0.00004 | $0.00018 |
Grade A, and why
ai-app-security-review 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.
What it actually says
AI App Security Review
Required Checks
- Prompt injection boundaries between user, retrieved, system, and tool content.
- Tool-call allowlists, parameter validation, and destructive-action approval.
- Retrieval/document grounding and citation expectations.
- Model provider error handling without raw provider leakage.
- User input boundaries before prompts, tools, database writes, and outbound requests.
- Logging and telemetry redaction for prompts, documents, tokens, cookies, and keys.
Output
Group findings by trust boundary. Prefer specific mitigations such as schema validation, allowlisted tools, redacted logs, and explicit human approval gates.
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 · 22 lines · 41 tokens per session scan A df4d52e36ec0
ai-app-security-review is a skill published in the GitHub repository priyanshuchawda/codeaudit (1 stars, last pushed 4mo ago), licensed MIT. It adds 41 tokens to every session and 177 once invoked, about $0.0002 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 2026-08-31.
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