ce-code-review

A structured review of code for bugs, regressions, missing tests, and project standards. A regression is a new problem caused by a recent change.

In plain words
What is it for?
Use it to review changes, check related tests and standards, and produce or apply findings using the repository's configured documentation location.
Why use it?
It gives code review a consistent process before a pull request or when you want to apply review findings locally.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/everyinc/compound-engineering-plugin/ce-code-review
Any agent
npx skills add EveryInc/compound-engineering-plugin --skill ce-code-review
Clone the repo
git clone --depth 1 https://github.com/EveryInc/compound-engineering-plugin

Made for: Claude Code, Codex.

Per session 60 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,507 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00060 $0.01507
Opus 5 $0.00030 $0.00754
Sonnet 5 $0.00012 $0.00301
Haiku 4.5 $0.00006 $0.00151

Measured yesterday against content hash 4fe803c556d4, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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 yesterday.

The scan reads SKILL.md. This mod also ships 4 executable files (scripts/cross-model-adversarial-review.sh, scripts/findings-mechanics.py, scripts/peer-job-runner.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

skills/ce-code-review/SKILL.md · 42 lines

How it starts

The opening of the file, as written. The whole thing — 42 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Code Review

Artifact Root

Resolve the CE artifact root <root> before composing any artifact path.

  • Read docs_root from <repo-root>/.compound-engineering/config.yaml only (<repo-root> = git rev-parse --show-toplevel). Do not read it from config.local.yaml. Unset -> <root> is docs, 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 naming docs_root and the value -- never fall back to docs.
  • 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 read docs.

Execution spine

Follow these steps in order; the references supply the detail but never change the order. Each reference named below is a required read for its step: load it before doing that step's work.

  1. Read references/modes-and-output.md first. It settles what the arguments mean, which argument conflicts stop the run before any reviewer is dispatched, whether the quick-review short-circuit applies, and what this invocation returns.
  2. Stage 1. Read references/scope.md and resolve the reviewed diff, the scope mode, and the deterministic scope signals.
  3. Stage 2. Read references/intent-and-plan.md, write the intent summary every reviewer receives, and discover the plan Stage 6 verifies requirements against.
  4. Stage 3. Read references/persona-catalog.md and references/select-and-route.md, then select the risk-driven reviewer roster, discover applicable standards paths, and bind the adversarial route.
  5. Stage 3d. When adversarial is selected for a local reviewed tree, start and persist the sanctioned cross-model job that references/cross-model-review.md defines, 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 sets cross_model_review_mode: off with no live opt-in; both are resolved before you bind a route. This pass's skip and target-selection keys are cross_model_review_mode and cross_model_peer. Missing files or unset keys take the default auto route; they are not a skip. Another skill's engine preference is not this gate. Model and effort overrides stay with the bound target as the reference states. 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.
  6. Stage 4. Read references/dispatch-reviewers.md. Dispatch the materialized local roster as one foreground concurrent batch sized to the host's active-agent cap. Every successful launch is collected when its terminal outcome is in hand: consume valid compact returns, classify a terminal tool error or malformed output as a failed reviewer, and keep launch receipts uncollected. Use the host's blocking collection capability for asynchronous receipts, and do not synthesize until every successful launch is collected. If launched work cannot be collected reliably, stop it, discharge any persisted peer through its owning cleanup before returning the failure result, and never emit progress or wait for a notification. 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.
  7. Stages 5 and 6. Once the reviewer returns are ready, read references/finish-review.md. Fold in the peer once, run the documented findings mechanics, and run every validator the reference selects; only then return the report. Never synthesize directly from raw reviewer artifacts. 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.

Read the full file on GitHub · 42 lines

Files

What ships with it

36 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.

Changes

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.

  1. yesterday First seen · 42 lines · 60 tokens per session scan A 4fe803c556d4

Subscribe to this mod's changes

ce-code-review is a skill published in the GitHub repository EveryInc/compound-engineering-plugin (24,696 stars, last pushed 2d ago), licensed MIT. It adds 60 tokens to every session and 1,507 once invoked, about $0.0003 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 2026-08-30.

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