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Comet Review

OrganizationPopular
rpamis
comet-review

Manually review the implementation diff for the current Comet change. Report correctness, security, and edge-case issues without advancing the workflow.

Overview

Publisherrpamis
Repositorycomet
Skill namecomet-review
Stars
3.1K
Forks
295
Bundled files
1
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.

  • 1 bundled files

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

  • Open source

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

Installation

Install the Comet 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/rpamis/comet.git /tmp/comet
mkdir -p .claude/skills
cp -r /tmp/comet/assets/skills/comet-review .claude/skills/comet-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Comet 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 Comet 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 Comet 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.

Comet Manual Code Review

Perform one on-demand, read-only code review of the currently selected Comet change. This entry is not a workflow phase and does not replace Build or Verify checks and reviews.

This entry is independent of review_mode. That field controls automatic reviews within the workflow; /comet-review is a single review explicitly requested by the user. Do not read, modify, or override the current change's review_mode during this invocation.

Perform read-only operations

The entire Skill invocation must remain read-only:

  • Do not modify, create, or delete files.
  • Do not stage, commit, switch branches, create branches, or create worktrees.
  • Do not run comet state select, comet native select, comet state set, comet state transition, phase guards, comet native next, or archive commands.
  • Do not fix findings, advance phase, or update tasks, state, verification reports, or review records.
  • Do not call this review a Verify pass or treat “no findings” as proof that tests passed.

Only run commands needed to read files, query state, and inspect Git diffs. Checks that might execute project code, install dependencies, or produce files are outside this entry's scope.

1. Locate the project and current change

  1. Use read-only Git queries to find the project root. Outside a Git repository, use the current Comet project root.

  2. Run at the project root:

    bash
    comet status . --json
  3. Read .comet/current-change.json and choose the review target in this order:

    • If the file contains a valid comet.selection.v2, use its workflow and change.
    • If selection is absent and status lists exactly one unarchived Comet change, use that change for this review only; do not write selection.
    • If selection is absent and several changes exist, list their names, workflows, and phases, ask the user to choose one, and end this invocation.
    • If selection points to a missing, archived, or invalid change, report the stale/invalid selection and stop without repairing it.

Ignore ordinary OpenSpec changes that Comet does not manage. Do not replace the selected workflow with the default workflow merely because they differ.

2. Gather review context

Read only the context needed for this change. Keep a source path or command for each fact.

Classic

  1. Read and follow comet-classic/reference/classic-layout.md to resolve the project's Classic logical roots.

  2. Read the current change's proposal.md, design.md, tasks.md, and specs/*/spec.md. Also read any linked Design Doc.

  3. Use these read-only queries for the phase, baseline, and existing evidence references:

    bash
    comet state get <change-name> phase
    comet state get <change-name> base_ref
    comet state get <change-name> plan
    comet state get <change-name> verification_report
  4. Read the existing plan, verification report, and build/verify command checks returned by comet status . --json. Mark missing evidence as “not provided”; do not infer failure or success.

Native

Run these read-only commands:

bash
comet native show <change-name> --json
comet native status <change-name> --details --json

Follow the returned references to read the brief, complete proposed Specs, acceptance, Builder handoff, checks, verification, risks, blockers, and verification report. Use evidence for the current candidate/iteration only. Historical iterations may explain remaining risks but must not override current state.

3. Establish the implementation diff

  1. Run git status --short --untracked-files=all to list all staged, unstaged, and untracked files in the worktree.
  2. Use the current change's requirements, workspace binding, Git history, and worktree state to establish the most reliable relevant scope. For Classic, prefer a valid plan base-ref; if it is absent or invalid, fall back to the state base_ref. The two values do not need to match. Only when both values are invalid is the Classic baseline missing. For Native, use the workspace relationships in state and the evidence defining the current candidate's implementation scope.
  3. Inspect the complete diff from the reliable baseline to the current worktree, including committed, staged, and unstaged changes. Read all untracked files belonging to this change, including source, tests, documentation, configuration, and metadata such as SKILL.md and agents/openai.yaml. Clearly identify them as untracked.
  4. Exclude changes clearly belonging to other changes or unrelated user work. Ask the user only when ambiguity would materially affect the review conclusions. Otherwise, continue with the available evidence and state your scope assumptions in the report.

If the available evidence still cannot establish a reliable, verifiable baseline, review the visible worktree diff and prominently label the review scope as incomplete.

4. Perform the review

Review the requirements, tasks, and current diff, focusing only on:

  • Implementation correctness and clear logic errors.
  • Security, permission, and path-boundary risks.
  • Error handling, compatibility, and important edge cases.
  • Missing tasks or implementation that contradicts explicit requirements for this change.
  • Whether tests cover the changed behavior and whether the available test evidence supports the conclusions.

Do not report style preferences, unrelated refactoring, or speculation without a concrete impact. Each finding must identify a file and line number and explain the input or situation that triggers the error or risk. Lower its severity or place it under “Open questions” when evidence is insufficient.

Use only these severity levels:

  • CRITICAL: security compromise, data loss, or an unusable core workflow.
  • IMPORTANT: a clear correctness error, missing core acceptance requirement, or likely regression.
  • WARNING: a real, non-blocking edge-case risk or test gap.
  • SUGGESTION: a concrete improvement that does not affect current correctness.

5. Report the results

List findings first, ordered by severity. Use this format:

text
[IMPORTANT] Short title — path/to/file.ts:123
Impact: The input or situation and the resulting error.
Evidence: The specific relationship to the diff, task, spec, or verification record.

Then include:

  • Review scope: workflow, change, phase, baseline, included diffs, and any scope limitations.
  • Evidence status: the test, build, and verification records inspected and whether they still apply to the current changes. Do not rerun tests.
  • Open questions: only questions that actually prevent a judgment.
  • Conclusion: the finding count, or an explicit “No concrete findings.”

Even with no findings, state remaining risks and checks not performed. End with this reminder:

This was a read-only manual review. It does not advance the Comet phase and cannot replace /comet-verify or Native Verify.

If the user subsequently requests fixes, treat that as a new write task: leave this Skill and resume development under the repository's current workflow rules.

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 Comet Review AI skill do?

Manually review the implementation diff for the current Comet change. Report correctness, security, and edge-case issues without advancing the workflow.

Why use Comet Review on TypingMind?

Because you install it once and use it with any model. Comet 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 Comet Review in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/rpamis/comet/tree/master/assets/skills/comet-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 Comet 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 Comet Review?

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

Is the Comet Review AI skill free?

Yes. It is published on GitHub by rpamis 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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