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Parallel Code Review

Community
spencerpauly
parallel-code-review

Run four parallel read-only subagents that each review the same diff from a different lens — security, performance, correctness, and readability — then merge findings into one report. Use before merging large or risky PRs.

Overview

Publisherspencerpauly
Repositoryawesome-cursor-skills
Skill nameparallel-code-review
Stars
848
Forks
169
Bundled files
Instructions only
LicenseCC0-1.0
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Parallel 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.
    https://github.com/spencerpauly/awesome-cursor-skills/tree/main/resources/parallel-code-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/spencerpauly/awesome-cursor-skills.git /tmp/awesome-cursor-skills
mkdir -p .claude/skills
cp -r /tmp/awesome-cursor-skills/resources/parallel-code-review .claude/skills/parallel-code-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Parallel 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 Parallel 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 Parallel 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.

Parallel Code Review

Use Cursor’s Task tool to run four explore (read-only) subagents at once. Each subagent only reads code and produces findings for one dimension. The main agent merges results into a single prioritized review — something only a multi-agent setup does efficiently.

When to use

  • Large diffs or refactors where a single pass misses categories.
  • Security-sensitive changes (auth, payments, parsing untrusted input).
  • Performance-sensitive paths (hot loops, N+1 queries, bundle entry points).

Workflow

1. Scope the change set

Prefer a concrete list of files for reviewers:

bash
git diff --name-only origin/main...HEAD

Or paste the PR link and let the main agent list changed files from the branch.

2. Launch four parallel subagents

Send one message with four Task invocations, each subagent_type: "explore" and readonly: true, with prompts like:

Security

Read-only review: SECURITY

Changed files:
<list>

Focus: injection (SQL, shell, XSS), authZ/authN gaps, secrets in code, unsafe deserialization, path traversal, SSRF, IDOR, dependency CVEs mentioned in diff.

Output:
- Critical / High / Medium / Low findings
- File:line and short fix recommendation
- "No issues" if nothing material

Performance

Read-only review: PERFORMANCE

Changed files:
<list>

Focus: N+1 queries, missing indexes, accidental O(n²) loops, bundle size impact, unnecessary re-renders, sync I/O on hot paths, unbounded caches.

Output: same severity + location format as above.

Correctness

Read-only review: CORRECTNESS

Changed files:
<list>

Focus: logic bugs, off-by-one, wrong edge cases, race conditions, error handling gaps, breaking API changes, test gaps for new behavior.

Output: same format.

Readability / maintainability

Read-only review: READABILITY

Changed files:
<list>

Focus: naming, duplication, abstraction boundaries, file size, unclear control flow, missing types/docs where they would prevent bugs.

Output: same format; prefer suggestions over nitpicks.

3. Synthesize

The main agent should:

  1. De-duplicate findings that appear in multiple dimensions (count once, worst severity).
  2. Order by severity, then by fix cost.
  3. Produce a short executive summary (5 bullets max) and a table or list of actionable items.

4. Optional — address findings

Fix in the main agent or spawn targeted non-readonly follow-up tasks only for approved items.

Notes

  • Keep prompts read-only so parallel runs never fight over writes.
  • If the diff is huge, split by directory and run four reviewers per directory in a second wave — do not cram unrelated megadiffs into one pass.
  • This complements human review; it does not replace compliance or security sign-off for regulated environments.

Frequently asked questions

What does the Parallel Code Review AI skill do?

Run four parallel read-only subagents that each review the same diff from a different lens — security, performance, correctness, and readability — then merge findings into one report. Use before merging large or risky PRs.

Why use Parallel Code Review on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/spencerpauly/awesome-cursor-skills/tree/main/resources/parallel-code-review. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Parallel 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 Parallel Code Review?

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

Is the Parallel Code Review AI skill free?

Yes. It is published on GitHub by spencerpauly under the CC0-1.0 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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