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-code-reviewgit 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-code-review)<a href="https://agentmods.dev/skills/everyinc/compound-engineering-plugin/ce-code-review"><img src="https://agentmods.dev/badge/skills/everyinc/compound-engineering-plugin/ce-code-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/everyinc/compound-engineering-plugin/ce-code-review"><img src="https://agentmods.dev/badge/skills/everyinc/compound-engineering-plugin/ce-code-review.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 Excessive Agency · line 28 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00062 | $0.01669 |
| Opus 5.5 | $0.00025 | $0.00668 |
| Sonnet 5.5 | $0.00012 | $0.00334 |
| Haiku 4.5 | $0.00006 | $0.00167 |
Grade A, and why
ce-code-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 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 — 41 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Review
Help the caller deliver a correct change within the agreed scope. Find defects and improvements whose consequences justify action; judge the code against its intended behavior and project requirements, not a preferred rewrite. Serious defects remain important even when the diff is small. An adequate change needs no findings.
Done when: required review and validation are complete, retained findings are supported by the source, and the caller has a clear result with any remaining coverage limits. Apply only when the invocation authorizes it, under the rules below.
Artifact Root
Resolve the CE artifact root <root> before composing any artifact path.
- Read
docs_rootfrom<repo-root>/.compound-engineering/config.yamlonly (<repo-root>=git rev-parse --show-toplevel). Do not read it fromconfig.local.yaml. Unset -><root>isdocs, exactly as before. - Validate a set value: a repo-relative directory whose real, symlink-resolved path stays inside the repo and is neither the repo root nor under
.git/. Otherwise stop with an error namingdocs_rootand the value -- never fall back todocs. - Use
<root>as the sole artifact location: create it if absent, compose each path as<root>/<subdir>with this skill's own subdirectory, and never also readdocs.
Execution spine
Follow these steps in order; the references supply the detail but never change the order. Read each reference when you enter the step whose own work it governs; a read made before that step does not satisfy it, and a reference you only hand to a leaf is not one you read.
- Read
references/modes-and-output.mdfirst. It settles arguments, conflicts, the quick-review short-circuit, the Review depth gate, and what this invocation returns. - Stage 1. Read
references/scope.mdand resolve the reviewed diff, the scope mode, and the deterministic scope signals. Then apply that Review depth gate before Stage 2. Lite ends the run without the later spine references; lite and focused each run from this context byreferences/depth-paths.md. - Stage 2. Read
references/intent-and-plan.md, write the intent summary every reviewer receives, and discover the plan Stage 6 verifies requirements against. - Stage 3. Read
references/persona-catalog.mdandreferences/select-and-route.md, then select the reviewers the change's risks call for, find the applicable standards files, and decide how the adversarial review will run. - Stage 3d. When adversarial is selected for a local reviewed tree, start and persist the sanctioned cross-model job that
references/cross-model-review.mddefines, before any local persona dispatch. Invoking this skill is itself the authorization for its configured or allowlisted peer route, once you have made the required disclosure of the recipient and of the code that leaves the machine. Do not ask the user to confirm a second time, and do not skip the peer because the user did not repeat that authorization. An explicit user prohibition on external review overrides it, as does a checkout that setscross_model_review_mode: offwith no live opt-in; both are resolved before you bind a route. A started peer replaces the local adversarial persona at this stage, and only a real failure to scope, allowlist, reach, authenticate, or start it leaves the local fallback in the roster; a later stage may still restore the local reviewer under the conditions that reference states. - Stage 4. Read
references/dispatch-reviewers.md. Dispatch the selected local reviewers as one concurrent batch collected in this turn, sized to the host's active-agent cap. Every successful launch is collected only when its terminal outcome is in hand: a valid compact return is consumed, a tool error or malformed output is recorded as a failed reviewer, and a launch acknowledgement alone is not a result. Use the host's blocking collection capability for asynchronous receipts within the bound that reference states, after which an uncollected reviewer is a failed reviewer; a terminal outcome may arrive as the call's return, a blocking wait's return, or a host-delivered terminal message that names the launch and carries its payload; a progress update is not one. If launched work cannot be collected reliably, stop it, and for any persisted peer (the cross-model job) run the cleanup its reference describes before returning the failure result, and never end the turn on progress to await it. Detaching local review into a polled background job is forbidden. The cross-model peer is the only detached work, and it may overlap this batch. - Stages 5 and 6. Once every reviewer result is in, write the finish input
references/finish-input.mddefines and stay in that reference: it owns the validator launch and the run-artifact list. Dispatch in sequence the two leaf subagents it names, each seeded withreferences/finish-review.md, which the leaves read from disk and you do not open: a merge leaf that folds in the peer's findings once and merges from the run dir, then, after you launch and collect the validator it selected, a report leaf that renders the report. Neither leaf launches a subagent; you launch every one. Emit the report leaf's return verbatim as this skill's response. Never synthesize directly from raw reviewer artifacts, and never merge or render in the dispatch context. In the multi-agent path, emit only this skill's report: do not also invoke a harness-native findings or reporting tool, which belongs to the quick-review short-circuit alone.
What ships with it
39 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.
- references/action-class-rubric.md 5.1 KB
- references/cross-model-recovery.md 10 KB
- references/cross-model-review.md 31 KB
- references/depth-paths.md 6.9 KB
- references/diff-scope.md 4.8 KB
- references/dispatch-reviewers.md 25 KB
- references/findings-schema.json 8.0 KB
- references/finish-input.md 18 KB
- references/finish-review.md 43 KB
- references/intent-and-plan.md 7.8 KB
- references/modes-and-output.md 16 KB
- references/persona-catalog.md 7.7 KB
- references/personas/adversarial-reviewer.md 10 KB
- references/personas/agent-native-reviewer.md 9.3 KB
- references/personas/api-contract-reviewer.md 4.2 KB
- references/personas/correctness-reviewer.md 5.7 KB
- references/personas/data-migration-reviewer.md 5.1 KB
- references/personas/deployment-verification-agent.md 4.9 KB
- references/personas/julik-frontend-races-reviewer.md 3.7 KB
- references/personas/learnings-researcher.md 18 KB
- references/personas/maintainability-reviewer.md 8.6 KB
- references/personas/performance-reviewer.md 3.5 KB
- references/personas/previous-comments-reviewer.md 3.5 KB
- references/personas/project-standards-reviewer.md 7.5 KB
- references/personas/reliability-reviewer.md 5.0 KB
- references/personas/security-reviewer.md 5.3 KB
- references/personas/swift-ios-reviewer.md 11 KB
- references/personas/testing-reviewer.md 7.0 KB
- references/review-output-template.md 14 KB
- references/scope.md 17 KB
- references/select-and-route.md 15 KB
- references/subagent-template.md 25 KB
- references/validator-batch-template.md 7.0 KB
- scripts/cross-model-adversarial-review.sh 60 KB runs code
- scripts/findings-mechanics.py 9.8 KB runs code
- scripts/packs-resolve.py 31 KB runs code
- scripts/peer-job-runner.py 102 KB runs code
- scripts/review-scope.py 14 KB runs code
- scripts/run-log.py 10 KB runs code
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 · -4 lines 4dbdc4485af0
- 1mo ago Changed 857e42218445
- 1mo ago Changed · -2 lines · +2 tokens per session ada077c4739f
- 1mo ago Changed 867dfbc55121
- 1mo ago Changed · +5 lines 14a52206237f
- 1mo ago First seen · 42 lines · 60 tokens per session scan A 4fe803c556d4
ce-code-review is a skill published in the GitHub repository EveryInc/compound-engineering-plugin (25,625 stars, last pushed today), licensed MIT. It adds 62 tokens to every session and 1,669 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
code-review
Reviews Spec Kit code changes for consistency with applicable design documents, positive and negative test coverage, regression evidence for bug fixes, and consistent repository terminology. Use when reviewing a diff or pull request. Do not use for implementing changes or posting GitHub review actions.
apex-review
Cross-cutting review of recent work — catches gaps between specialists. Use when asked to "review what we built", "check the work", "pre-launch review", or after completing a significant chunk of work.
spine-review
API and backend code review — REST conventions, auth, validation, error handling, pagination, rate limiting, test coverage. Use when asked to "review this API", "code review", "review backend", or "pre-launch backend check".
engineering-skills
23 skills de engenharia prontas para produção para Claude Code. Arquitetura, frontend, backend, QA, DevOps, segurança, IA/ML, engenharia de dados, Playwright, Stripe, AWS, MS365. ferramentas Node.js, TypeScript e scripts utilitários.
alex-core-invariants
Use when reviewing architecture, refactors, state/data ownership, duplication, code and test structure, failure handling, debug discipline, work-in-progress hygiene, or concurrency policy against Alex's eight fixed invariants.
grill-me
Adversarial code review before a PR — Claude asks hard questions until you pass the test. Also supports elegant rewrites ("scrap this and implement the elegant solution") and spec-driven development. Use before opening any pull request. For mechanically finding bugs in a diff, prefer a platform-native code-review…