Compound Engineering is a plugin that organizes software work into brainstorming, planning, implementation, review, and recording lessons for future changes. It is used with AI coding agents including Claude Code, Cursor, and Codex, and the catalogue entries provide parts of its agent, skill, command, and hook workflow.
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 EveryInc/compound-engineering-plugin --skill ce-dogfoodgit clone --depth 1 https://github.com/EveryInc/compound-engineering-pluginWrote 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/everyinc/compound-engineering-plugin/ce-dogfood)<a href="https://agentmods.dev/skills/everyinc/compound-engineering-plugin/ce-dogfood"><img src="https://agentmods.dev/badge/skills/everyinc/compound-engineering-plugin/ce-dogfood/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/everyinc/compound-engineering-plugin/ce-dogfood"><img src="https://agentmods.dev/badge/skills/everyinc/compound-engineering-plugin/ce-dogfood.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium MCP Rug Pull · line 20 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
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 | $0.00057 | $0.01769 |
| Opus 5.5 | $0.00023 | $0.00708 |
| Sonnet 5.5 | $0.00011 | $0.00354 |
| Haiku 4.5 | $0.00006 | $0.00177 |
Grade A, and why
ce-dogfood 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 — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Dogfood
Act as a QA engineer who dogfoods the active branch end-to-end, autonomously, until it is genuinely ready.
Outcome: every user-visible change this branch introduced has been driven in a real browser along its whole journey, judged for correctness and for how it feels to the product's personas, with small breakages fixed, regression-tested, and committed. Done: every matrix scenario is Pass, Fixed, Skipped, or in a terminal Blocked state; the project's automated suite has been run once and its result recorded; and the report at <root>/dogfood-reports/<YYYY-MM-DD>-<branch-slug>-dogfood.md is finalized against its template and committed. A green matrix over a red suite finalizes as a not-ready verdict rather than a ready one. Chasing that suite green is not this run's job.
This is diff-scoped, not whole-app exploration. You test what this branch introduced or modified versus the trunk.
Read references/phases.md before Phase 0 (Scope) and follow it. It defines every phase in detail; the run cannot be executed correctly from the phase list below.
Boundaries
- Drive the browser exclusively through the
agent-browserCLI — never Chrome MCP tools (mcp__claude-in-chrome__*), another browser MCP, or a built-in browser-control tool, even when the platform offers one. Use the direct binary, nevernpx agent-browser(the direct binary uses the fast Rust client). - Never dogfood the trunk on a branch-name or blank target — there is no diff. A PR target always has a base, so it is always diffable even when its head branch is named
main. - A numeric target stays a PR identity through isolation and checkout — never collapse it to its head ref, whose name may itself be
main. - Never switch the primary checkout out from under the user. This skill decides only whether to offer isolation — no for a blank or current-branch target (you are already on it), yes for a PR or another named ref — and
ce-worktreehandles the mechanics and reports the verdict. On a declined offer, check the target out in place, confirming first if uncommitted changes would be disturbed. - Screenshots and other transient artifacts go to OS temp (
mktemp -d "${TMPDIR:-/tmp}/ce-dogfood-XXXXXX"), never the repo root; copy one in only to embed it in the report. - Auto-fix only what is small, well-understood, and low-risk. A change that needs an architectural or schema decision, alters product behavior or UX intent, spans many files, or has plausible competing solutions is escalated to the report's Decisions for a human section, never implemented to clear a matrix item.
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 Changed 7b8723b454bf
- 1mo ago Changed · +4 lines cbe8049ce772
- 1mo ago First seen · 63 lines · 57 tokens per session scan A 97f46ebf228f
ce-dogfood is a skill published in the GitHub repository EveryInc/compound-engineering-plugin (25,741 stars, last pushed today), licensed MIT. It adds 57 tokens to every session and 1,769 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 2026-08-30.
Other skills, from other repositories
proof-e2e
Build E2E test specs for critical user journeys — Playwright or Cypress, page objects, setup/teardown, CI config. Use when asked to "write E2E tests", "end-to-end testing", "browser tests", "UI tests", or "Playwright tests".
aif-web-testing
Test web apps with Playwright, Vitest and k6: E2E, unit, integration, load, security, visual and a11y.
browse
Drive a real browser through Aside: open a page, read it, click through a flow, take screenshots, check console errors. (gstack).
cua-driver
Use Cua Driver for desktop or browser tasks that are awkward or unavailable through Bash/APIs, or when the user explicitly wants GUI interaction: app testing, visual bug reproduction, form filling, calendar entry, screenshots, and demo recording. Also covers Cua setup; not OpenAI Codex Computer Use or web research.
create-verification-skill
Create a repo-local verification skill and exhaustive feature map for driving a real app through its UI, CLI, or API. Use for "create a verification skill", "make a verify skill for this repo", or "document how agents can verify this app".
browser-qa
A browser-based quality check for deployed web pages and user flows. It uses browser automation to test rendering, navigation, forms, interactions, responsive behaviour, and accessibility-related issues.