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Ce Code Review

OrganizationPopular
EveryInc
ce-code-review

Review a named diff or PR for bugs, regressions, tests, and standards. Use when asked to review code or when a shipping skill needs a review receipt. Use when asked to apply this review's findings locally. Use ce-resolve-pr-feedback for feedback already left on a PR.

Overview

PublisherEveryInc
Repositorycompound-engineering-plugin
Skill namece-code-review
Stars
25.1K
Forks
2.1K
Bundled files
40
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 40 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by EveryInc on GitHub. Read the source before you install it.

Installation

Install the Ce Code Review AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/EveryInc/compound-engineering-plugin.git /tmp/compound-engineering-plugin
mkdir -p .claude/skills
cp -r /tmp/compound-engineering-plugin/skills/ce-code-review .claude/skills/ce-code-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ce Code Review in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Ce Code Review on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Ce Code Review is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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_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. 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.

  1. Read references/modes-and-output.md first. It settles arguments, conflicts, the quick-review short-circuit, the Review depth gate, 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. 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 by references/depth-paths.md.
  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 reviewers the change's risks call for, find the applicable standards files, and decide how the adversarial review will run.
  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. 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 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.
  7. Stages 5 and 6. Once every reviewer result is in, write the finish input references/finish-input.md defines 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 with references/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.

Operating principles

  • Report-only by default; never push. A bare ce-code-review invocation produces findings and does not apply them. Entering the apply stage requires apply:local, or an explicit user request in the invoking prompt to apply or fix this review's findings; a deprecated mode:autofix token is neither. mode:agent never mutates the tree, even when nested inside a workflow that later applies findings. Never push, open PRs, or file tickets in any mode.
  • No blocking prompts. Never use AskUserQuestion, request_user_input, ask_user, or other blocking question tools. Infer intent, plan, and scope from explicit tokens, git state, PR metadata, and conversation. Note uncertainty in Coverage or the verdict — do not stop to ask.
  • Explicit mutations only. Never run gh pr checkout, git checkout, git switch, or similar branch-switch commands. Passing a PR number, URL, or branch name selects review scope, not permission to mutate the working tree. Uncommitted work can only be reviewed from the checkout that holds it, so to review it on a feature branch, stay on that branch (or check it out yourself) and pass base: or no target.
  • Report outcomes, not machinery. What you show the user is about the review: what is being examined, which coverage is included and the one-line reason for each conditional lens, the independent cross-model pass, and the findings. Name what the user would recognize, such as a PR number, a reviewer's concern, or a peer model. This skill's internal labels, dispatch bookkeeping, and setup narration stay out of user-facing text. Never claim more about the peer than its receipt attests. This governs what you surface and suppress, not the wording; use your own voice.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Ce Code Review AI skill do?

Review a named diff or PR for bugs, regressions, tests, and standards. Use when asked to review code or when a shipping skill needs a review receipt. Use when asked to apply this review's findings locally. Use ce-resolve-pr-feedback for feedback already left on a PR.

Why use Ce Code Review on TypingMind?

Because you install it once and use it with any model. Ce Code Review is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Ce Code Review in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/EveryInc/compound-engineering-plugin/tree/main/skills/ce-code-review. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Ce Code Review?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Ce Code Review?

As many as you like. As long as a model supports skills, you can use Ce Code Review with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Ce Code Review AI skill free?

Yes. It is published on GitHub by EveryInc under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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