Requesting Code Review logo

Requesting Code Review

Organization
ed3dai
requesting-code-review

Use when completing tasks, implementing major features, or before merging to verify work meets requirements - dispatches code-reviewer subagent, handles retries and timeouts, manages review-fix loop until zero issues

Overview

Publishered3dai
Repositoryed3d-plugins
Skill namerequesting-code-review
Stars
249
Forks
33
Bundled files
1
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 ed3dai on GitHub. Read the source before you install it.

Installation

Install the Requesting 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/ed3dai/ed3d-plugins.git /tmp/ed3d-plugins
mkdir -p .claude/skills
cp -r /tmp/ed3d-plugins/plugins/ed3d-plan-and-execute/skills/requesting-code-review .claude/skills/requesting-code-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Requesting 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 Requesting 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 Requesting 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.

Requesting Code Review

Dispatch ed3d-plan-and-execute:code-reviewer subagent to catch issues before they cascade.

Core principle: Review early, review often. Fix ALL issues before proceeding.

Do not use nested subagents. This skill may dispatch code-reviewer and task-bug-fixer as first-level subagents. Those subagents must do their assigned work directly and must not dispatch additional subagents.

Session Isolation

If the calling context provides a SCRATCHPAD_DIR, pass it to code-reviewer.

This prevents collisions when multiple planning/execution sessions run in parallel. The SCRATCHPAD_DIR is a namespaced temp directory (e.g., /tmp/plan-2025-01-24-feature-a7f3b2/) that the code-reviewer uses for any scratch files.

When to Request Review

Mandatory:

  • After each task in plan execution
  • After completing major feature
  • Before merge to main

Optional but valuable:

  • When stuck (fresh perspective)
  • Before refactoring (baseline check)
  • After fixing complex bug

The Review Loop

The review process is a loop: review → fix → re-review → until zero issues.

┌──────────────────────────────────────────────────┐
│                                                  │
│   Dispatch code-reviewer                         │
│         │                                        │
│         ▼                                        │
│   Issues found? ──No──► Done (proceed)           │
│         │                                        │
│        Yes                                       │
│         │                                        │
│         ▼                                        │
│   Dispatch bug-fixer                             │
│         │                                        │
│         ▼                                        │
│   Re-review with prior issues ◄──────────────────┘
└──────────────────────────────────────────────────┘

Exit condition: Zero issues, or issues accepted per your workflow's policy.

Step 1: Initial Review

Get git SHAs:

bash
BASE_SHA=$(git rev-parse HEAD~1)  # or commit before task
HEAD_SHA=$(git rev-parse HEAD)

Dispatch code-reviewer subagent:

<invoke name="Task">
<parameter name="subagent_type">ed3d-plan-and-execute:code-reviewer</parameter>
<parameter name="description">Reviewing [what was implemented]</parameter>
<parameter name="prompt">
  Use template at requesting-code-review/code-reviewer.md

  WHAT_WAS_IMPLEMENTED: [summary of implementation]
  PLAN_OR_REQUIREMENTS: [task/requirements reference]
  BASE_SHA: [commit before work]
  HEAD_SHA: [current commit]
  DESCRIPTION: [brief summary]
  SCRATCHPAD_DIR: [session-isolated temp dir, or omit if not applicable]
  Do not dispatch or invoke any subagents.
</parameter>
</invoke>

Code reviewer returns: Strengths, Issues (Critical/Important/Minor), Assessment

Step 2: Handle Reviewer Response

If Zero Issues

All categories empty → proceed to next task.

If Any Issues Found

Regardless of category (Critical, Important, or Minor), dispatch bug-fixer:

<invoke name="Task">
<parameter name="subagent_type">ed3d-plan-and-execute:task-bug-fixer</parameter>
<parameter name="description">Fixing review issues</parameter>
<parameter name="prompt">
  Fix issues from code review.

  Code reviewer found these issues:
  [list all issues - Critical, Important, and Minor]

  Your job is to:
  1. Understand root cause of each issue
  2. Apply fixes systematically (Critical → Important → Minor)
  3. Verify with tests/build/lint
  4. Commit your fixes
  5. Report back with evidence
  6. Do not dispatch or invoke any subagents.

  Work from: [directory]

  Fix ALL issues — including every Minor issue. The goal is ZERO issues on re-review.
  Minor issues are not optional. Do not skip them.
</parameter>
</invoke>

After fixes, proceed to Step 3.

Step 3: Re-Review After Fixes

CRITICAL: Track prior issues across review cycles.

<invoke name="Task">
<parameter name="subagent_type">ed3d-plan-and-execute:code-reviewer</parameter>
<parameter name="description">Re-reviewing after fixes (cycle N)</parameter>
<parameter name="prompt">
  Use template at requesting-code-review/code-reviewer.md

  WHAT_WAS_IMPLEMENTED: [from bug-fixer's report]
  PLAN_OR_REQUIREMENTS: [original task/requirements]
  BASE_SHA: [commit before this fix cycle]
  HEAD_SHA: [current commit after fixes]
  DESCRIPTION: Re-review after bug fixes (review cycle N)
  SCRATCHPAD_DIR: [session-isolated temp dir, or omit if not applicable]

  PRIOR_ISSUES_TO_VERIFY_FIXED:
  [list all outstanding issues from previous reviews]

  Verify:
  1. Each prior issue listed above is actually resolved
  2. No regressions introduced by the fixes
  3. Any new issues in the changed code

  Do not dispatch or invoke any subagents.

  Report which prior issues are now fixed and which (if any) remain.
</parameter>
</invoke>

Tracking prior issues:

  • When re-reviewer explicitly confirms fixed → remove from list
  • When re-reviewer doesn't mention an issue → keep on list (silence ≠ fixed)
  • When re-reviewer finds new issues → add to list

Loop back to Step 2 if any issues remain.

Handling Failures

Operational Errors

If reviewer reports operational errors (can't run tests, missing scripts):

  1. STOP - do not continue
  2. Report to human
  3. When told to continue, re-execute same review

Timeouts / Empty Response

Usually means context limits. Retry with focused scope:

First retry: Narrow to changed files only:

FOCUSED REVIEW - Context was too large.

Review ONLY the diff between BASE_SHA and HEAD_SHA.
Focus on: [list only files actually modified]

Skip: broad architectural analysis, unchanged files, tangential concerns.

WHAT_WAS_IMPLEMENTED: [summary]
PLAN_OR_REQUIREMENTS: [reference]
BASE_SHA: [sha]
HEAD_SHA: [sha]

Second retry: Split into multiple smaller reviews (one per file or logical group).

Third failure: Stop and ask human for help.

Quick Reference

SituationAction
Zero issuesProceed
Any issuesFix, re-review (or accept per workflow)
Operational errorStop, report, wait
TimeoutRetry with focused scope
3 failed retriesAsk human

Red Flags

Never:

  • Skip review because "it's simple"
  • Proceed with ANY unfixed issues (Critical, Important, OR Minor)
  • Argue with valid technical feedback without evidence
  • Rationalize skipping Minor issues ("they're just style", "we can fix later")

Minor issues are NOT optional. The code reviewer flagged them for a reason. Fix all of them. "Minor" means lower severity, not "ignorable."

If reviewer wrong:

  • Push back with technical reasoning
  • Show code/tests that prove it works
  • Request clarification on unclear feedback

Integration

Called by:

  • executing-an-implementation-plan (after each task)
  • finishing-a-development-branch (final review)
  • Ad-hoc when you need a review

Template location: requesting-code-review/code-reviewer.md

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 Requesting Code Review AI skill do?

Use when completing tasks, implementing major features, or before merging to verify work meets requirements - dispatches code-reviewer subagent, handles retries and timeouts, manages review-fix loop until zero issues

Why use Requesting Code Review on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ed3dai/ed3d-plugins/tree/main/plugins/ed3d-plan-and-execute/skills/requesting-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 Requesting 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 Requesting Code Review?

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

Is the Requesting Code Review AI skill free?

It is published on GitHub by ed3dai. Check the repository for licensing terms. 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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