Review logo

Review

Community
owainlewis
review

Uses a fresh agent to review an implementation change without editing it. Checks behavior, security, regressions, complexity, tests, docs, and missing proof. Use for code, PR, diff, security, second-opinion, or pre-merge reviews.

Overview

Publisherowainlewis
Repositoryblueprint
Skill namereview
Stars
419
Forks
73
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 owainlewis on GitHub. Read the source before you install it.

Installation

Install the 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.
    https://github.com/owainlewis/blueprint/tree/main/skills/review
  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/owainlewis/blueprint.git /tmp/blueprint
mkdir -p .claude/skills
cp -r /tmp/blueprint/skills/review .claude/skills/review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable 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 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 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.

Review

Find defects that can change the result or make the change unsafe. Do not turn personal taste into a finding.

Use a fresh subagent that did not implement the change. Tell it to review directly without delegating. If you are that reviewer, review directly. Stay read-only. Do not edit files or post comments.

Scope

Review the implementation target named by the user. It may be a file, diff, branch, commit, pull request, or other code change on GitHub. Use /architecture-review for a technical proposal that has not been implemented.

If the user does not name a target, review the repository's complete local change set. Include commits on the current branch relative to the default branch, staged changes, unstaged changes, and untracked files.

Use the repository and the immediate surrounding code as context. If there are no local changes, say so. Do not substitute a whole-repository audit.

Standard

Approve only when:

  • The change matches its source and behaves as required.
  • No known defect or unhandled risk could break the source or repository rules for security, data loss, compatibility, or operations.

Do not demand perfection or block on personal taste. Cite technical evidence or repository conventions.

The verdict is independent agent evidence. It is not GitHub approval or a replacement for human review.

Review order

  1. Set the frame. Give the reviewer the task, acceptance criteria and invariant IDs from its task or spec, repository rules, complete diff or pull request, and test evidence. Name the user or developer affected by the change.
  2. Take the broad view. Read the change summary and the relevant spec or ticket. Confirm that the change belongs in the system, matches the intended behavior, and delivers one reviewable outcome. Report a mismatch before reviewing details.
  3. Review the main behavior. Start with the files and flows that deliver the outcome. Check behavior, failures, security boundaries, interfaces, compatibility, migrations, concurrency, and operations.
  4. Review every human-written changed line in context. Read enough surrounding code to judge correctness, regressions, complexity, names, comments, style, and docs. For generated files or large data, inspect the source and spot-check the output. Keep findings within the change's scope.
  5. Review the proof. Check that tests:
    • Cover the changed behavior and affected failure paths.
    • Prove every cited AC-n, REQ-n, INV-n, or task criterion.
    • Assert behavior a user or caller can observe, or a documented internal contract.
    • Would fail under a broken implementation.
    • Do not copy implementation logic or hide the scenario in setup.
  6. Run focused checks that can confirm or disprove a claim that could change a finding or verdict.
  7. Check specialist coverage. Identify security, privacy, concurrency, accessibility, internationalization, or domain-specific work. Mark a risk unverified when the reviewer lacks the evidence or skill to judge it. Use Blocked when that gap could hide a problem that breaks a rule and no qualified reviewer covers it.
  8. Report findings. Return actionable findings in priority order using the format below.

Code review findings

Report only real bugs, customer-impacting problems, and proven simplifications introduced or exposed by the change. Check security, logic, behavior, reliability, compatibility, regressions, and affected failure paths.

Inspect enough surrounding code to prove each finding. A simplification must deliver the same result and proof with less state, indirection, duplication, or operational work. Report it only as Could fix.

Use this format for every finding:

text
Priority: Must fix / Should fix / Could fix
Confidence: 0 to 5
What I found: Describe the technical problem.
Why it matters: Explain the impact on customers, callers, or the service.
Trigger and effect: State the exact condition and what the customer or service experiences.
Where: File and line.
Suggested fix: Give a short, practical direction.

Priority means:

  • Must fix: Unsafe to merge. The change can cause a security failure, data loss, broken required behavior, or a serious service regression.
  • Should fix: A real defect or reliability risk that should be corrected before merge.
  • Could fix: A proven, limited problem or simplification that does not need to block merge. Do not use this for style preferences.

Confidence means:

  • 5: Confirmed by reproduction, test, or direct code evidence.
  • 4: Strong evidence with no credible alternative explanation.
  • 3: Probable, but some runtime evidence is unavailable.
  • 0 to 2: Do not report. Investigate further or mark the area unverified.

Use plain words. State what breaks, when it breaks, and who it affects. Omit style preferences, theoretical concerns, and problems outside the change.

Verdict

If fresh subagents are unavailable, stop and report that independent review is blocked unless the user explicitly accepts a documented self-review.

If there are no findings, say so. End with Approve, Request changes, or Blocked, then state what remains unverified. A Must fix or Should fix finding requires Request changes. Could fix findings do not prevent Approve.

Boundaries

  • Keep findings within the change, but inspect enough surrounding code to judge each changed line and its system effect.
  • Do not turn preferences into findings. Use technical evidence and repository conventions.
  • Do not approve solely because checks pass.

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 Review AI skill do?

Uses a fresh agent to review an implementation change without editing it. Checks behavior, security, regressions, complexity, tests, docs, and missing proof. Use for code, PR, diff, security, second-opinion, or pre-merge reviews.

Why use Review on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/owainlewis/blueprint/tree/main/skills/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 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 Review?

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

Is the Review AI skill free?

Yes. It is published on GitHub by owainlewis 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇