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 ce-reviewgit 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/ce-review)<a href="https://agentmods.dev/skills/gvkhosla/compound-engineering-pi/ce-review"><img src="https://agentmods.dev/badge/skills/gvkhosla/compound-engineering-pi/ce-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/gvkhosla/compound-engineering-pi/ce-review"><img src="https://agentmods.dev/badge/skills/gvkhosla/compound-engineering-pi/ce-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 | $0.00019 | $0.04509 |
| Opus 5.5 | $0.00008 | $0.01804 |
| Sonnet 5.5 | $0.00004 | $0.00902 |
| Haiku 4.5 | $0.00002 | $0.00451 |
Grade A, and why
ce:review scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 560 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review Command
<command_purpose> Perform exhaustive code reviews using multi-agent analysis, ultra-thinking, and Git worktrees for deep local inspection. </command_purpose>
Introduction
Senior Code Review Architect with expertise in security, performance, architecture, and quality assurance
Prerequisites
Main Tasks
1. Determine Review Target & Setup (ALWAYS FIRST)
<review_target> #$ARGUMENTS </review_target>
Immediate Actions:
<task_list>
- Determine review type: PR number (numeric), GitHub URL, file path (.md), or empty (current branch)
- Check current git branch
- If ALREADY on the target branch (PR branch, requested branch name, or the branch already checked out for review) → proceed with analysis on current branch
- If DIFFERENT branch than the review target → offer to use worktree: "Use git-worktree skill for isolated Call
skill: git-worktreewith branch name" - Fetch PR metadata using
gh pr view --jsonfor title, body, files, linked issues - Set up language-specific analysis tools
- Prepare security scanning environment
- Make sure we are on the branch we are reviewing. Use gh pr checkout to switch to the branch or manually checkout the branch.
Ensure that the code is ready for analysis (either in worktree or on current branch). ONLY then proceed to the next step.
</task_list>
Protected Artifacts
<protected_artifacts> The following paths are compound-engineering pipeline artifacts and must never be flagged for deletion, removal, or gitignore by any review agent:
docs/brainstorms/*-requirements.md— Requirements documents created by/ce:brainstorm. These are the product-definition artifacts that planning depends on.docs/plans/*.md— Plan files created by/ce:plan. These are living documents that track implementation progress (checkboxes are checked off by/ce:work).docs/solutions/*.md— Solution documents created during the pipeline.
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.
- 2mo ago First seen · 560 lines · 19 tokens per session scan A 6f0aa887d427
ce:review is a skill published in the GitHub repository gvkhosla/compound-engineering-pi (52 stars, last pushed 1mo ago), licensed MIT. It adds 19 tokens to every session and 4,509 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 2026-08-30.
Other skills, from other repositories
review-harness
Use when reviewing work performed through a Pi task harness. Checks accepted scope, executed validation records, final artifacts and remaining risks without trusting self-reported completion notes.
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.
my-pi-nopeek
Use when loading .env credentials, running authenticated commands, or keeping secret values out of Pi context.
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.
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.
pstack-interrogate
Use for "interrogate", "adversarial review", "multi-model review", "challenge this", "stress test this code", "find blind spots", or "tear this apart". Multiple LLM reviewers challenge changes from independent angles.