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Dev Review

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codeaholicguy
dev-review

AI DevKit · Final code review phase guidance for holistic pre-push review. Use when the user wants code review, final lifecycle review, design alignment checks, integration risk review, or dev-lifecycle phase 9.

Overview

Publishercodeaholicguy
Repositoryai-devkit
Skill namedev-review
Stars
1.6K
Forks
252
Bundled files
1
LicenseApache-2.0
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 codeaholicguy on GitHub. Read the source before you install it.

Installation

Install the Dev 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/codeaholicguy/ai-devkit.git /tmp/ai-devkit
mkdir -p .claude/skills
cp -r /tmp/ai-devkit/skills/dev-review .claude/skills/dev-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Dev 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 Dev 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 Dev 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.

Dev Review

Run final pre-push review for configured AI docs features. Before changing docs or code, propose the concrete review plan and wait for user approval unless the user already approved the exact phase plan.

Phase Contract

  1. Run npx ai-devkit@latest lint before phase work.
  2. If working on a named feature, run npx ai-devkit@latest lint --feature <name>.
  3. Check git status -sb and git diff --stat.
  4. Read feature docs and relevant changed files before findings.
  5. Apply the verify skill before claiming readiness.
  6. If parent dev-lifecycle established usable task tracing, emit review phase, progress, blocker/finding, next-step, and final evidence/readiness events per task.

Code Review

Use for Phase 9. Take a holistic review stance: findings first, ordered by severity, grounded in file/line references.

  1. Gather context: feature description, modified files, design docs, risky areas, tests already run.
  2. Verify design alignment by summarizing architectural intent and checking implementation matches.
  3. For each modified file, grep exported names to trace callers and dependents. Read relevant signatures, call sites, and type definitions.
  4. Check consistency against 1-2 similar modules.
  5. Search for existing utilities the new code could reuse or now duplicates. Flag near-matches honestly; do not force a wrong abstraction.
  6. Verify contract integrity at API, type, config, and schema boundaries.
  7. Check boundary discipline: external data is validated at edges, internal code is not littered with redundant guards, and transport/storage/framework types do not leak through domain APIs.
  8. Check reader load: needless layers, pass-through methods, broad shallow interfaces, mutable state scope, and unclear value ownership.
  9. Check domain fit: branch growth, synchronized flags, repeated shape assumptions, temporal decomposition, and missing state models.
  10. Check dependency health, including circular dependencies or version conflicts from new imports.
  11. Check breaking changes. For public/external APIs, recommend parallel change and deprecation over in-place mutation. For in-repo-only callers, all callers should be migrated and legacy APIs deleted.
  12. Check rollback safety, especially irreversible migrations or one-way data/state changes.
  13. Review file by file for correctness, logic, edge cases, redundancy, security, performance, error handling, and test coverage.
  14. Check cross-cutting concerns: naming conventions, documentation updates, missing tests, config/migration changes.
  15. Summarize blocking issues, important follow-ups, and nice-to-haves. Per finding include file, issue, impact severity, and recommendation.
  16. If task tracing is available, add blockers and set blocked for blocking findings; if review passes with final evidence, close the task per task.
  17. Complete final checklist: design match, no logic gaps, security addressed, integration points verified, tests cover changes, docs updated.

Done: if the checklist passes, the feature is ready to push and create a PR. If blocking issues remain, return to dev-implementation or dev-testing.

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 Dev Review AI skill do?

AI DevKit · Final code review phase guidance for holistic pre-push review. Use when the user wants code review, final lifecycle review, design alignment checks, integration risk review, or dev-lifecycle phase 9.

Why use Dev Review on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/codeaholicguy/ai-devkit/tree/main/skills/dev-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 Dev 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 Dev Review?

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

Is the Dev Review AI skill free?

Yes. It is published on GitHub by codeaholicguy under the Apache-2.0 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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