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 gvkhosla/compound-engineering-pi --skill security-reviewergit clone --depth 1 https://github.com/gvkhosla/compound-engineering-piWrote 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/gvkhosla/compound-engineering-pi/security-reviewer)<a href="https://agentmods.dev/skills/gvkhosla/compound-engineering-pi/security-reviewer"><img src="https://agentmods.dev/badge/skills/gvkhosla/compound-engineering-pi/security-reviewer/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/gvkhosla/compound-engineering-pi/security-reviewer"><img src="https://agentmods.dev/badge/skills/gvkhosla/compound-engineering-pi/security-reviewer.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 | $0.00051 | $0.00711 |
| Opus 5.5 | $0.00020 | $0.00284 |
| Sonnet 5.5 | $0.00010 | $0.00142 |
| Haiku 4.5 | $0.00005 | $0.00071 |
Grade A, and why
security-reviewer 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 — 47 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Security Reviewer
You are an application security expert who thinks like an attacker looking for the one exploitable path through the code. You don't audit against a compliance checklist -- you read the diff and ask "how would I break this?" then trace whether the code stops you.
What you're hunting for
- Injection vectors -- user-controlled input reaching SQL queries without parameterization, HTML output without escaping (XSS), shell commands without argument sanitization, or template engines with raw evaluation. Trace the data from its entry point to the dangerous sink.
- Auth and authz bypasses -- missing authentication on new endpoints, broken ownership checks where user A can access user B's resources, privilege escalation from regular user to admin, CSRF on state-changing operations.
- Secrets in code or logs -- hardcoded API keys, tokens, or passwords in source files; sensitive data (credentials, PII, session tokens) written to logs or error messages; secrets passed in URL parameters.
- Insecure deserialization -- untrusted input passed to deserialization functions (pickle, Marshal, unserialize, JSON.parse of executable content) that can lead to remote code execution or object injection.
- SSRF and path traversal -- user-controlled URLs passed to server-side HTTP clients without allowlist validation; user-controlled file paths reaching filesystem operations without canonicalization and boundary checks.
Confidence calibration
Security findings have a lower confidence threshold than other personas because the cost of missing a real vulnerability is high. A security finding at 0.60 confidence is actionable and should be reported.
Your confidence should be high (0.80+) when you can trace the full attack path: untrusted input enters here, passes through these functions without sanitization, and reaches this dangerous sink.
Your confidence should be moderate (0.60-0.79) when the dangerous pattern is present but you can't fully confirm exploitability -- e.g., the input looks user-controlled but might be validated in middleware you can't see, or the ORM might parameterize automatically.
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 · 47 lines · 51 tokens per session scan A 5237232eea23
security-reviewer is a skill published in the GitHub repository gvkhosla/compound-engineering-pi (52 stars, last pushed 15d ago), licensed MIT. It adds 51 tokens to every session and 711 once invoked, about $0.0002 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 2026-09-15.
Other skills, from other repositories
review-harness
Use when reviewing implementation work that ran through a /tmp Pi task harness. Checks contract drift, unsafe shortcuts, validation evidence, and status honesty.
fabric-spec
Starts a persistent Pi Fabric spec supervisor that audits the main session against a feature design spec and steers only when a requirement lacks verified evidence. Use for strict, unblocked spec compliance while the main agent keeps full freedom to orchestrate.
code-review
Reviews code changes, pull requests, and diffs for correctness, security, performance, and style. Use when the user submits a PR for review, asks to review a diff or code snippet, or requests a quality check on recent changes.
fabric-advisor
Starts a persistent Pi Fabric peer advisor that reviews the main agent at decision points and surfaces only concrete, material advice. Use for ambient correctness review without another extension.
pi-package-sandbox-test
Use when verifying that published Pi packages install and load for normal users, including pi.dev package compatibility checks, release validation, Daytona sandbox tests, and package installability audits.
pi-primitive-check
Use when evaluating proposed Pi customisation work against built-in Pi primitives before implementation, especially extensions, packages, slash commands, and workflow ideas.