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Requesting Code Review

Community
The01Geek
requesting-code-review

Use when completing a task or major feature, or before merging, to have PRFlow's final-pass reviewer check the work against its requirements.

Overview

PublisherThe01Geek
Repositoryprflow
Skill namerequesting-code-review
Stars
100
Forks
24
Bundled files
Instructions only
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.

  • Self-contained

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

  • Open source

    Published by The01Geek 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.
    https://github.com/The01Geek/prflow/tree/main/skills/requesting-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/The01Geek/prflow.git /tmp/prflow
mkdir -p .claude/skills
cp -r /tmp/prflow/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

Portable helper anchor (single-statement). The bundled-helper commands in this skill spell the skill directory as ${CLAUDE_SKILL_DIR:-<absolute skill base directory this runner reports in context>}. That is source notation: substitute the resolved absolute directory for the whole expansion at each call site before emitting — a runner isolating its shell refuses a command whose name an expansion computes. Take $CLAUDE_SKILL_DIR's value when the runner reports one, else locate the directory yourself — this text lives in a file inside it, whose sibling ../../scripts/ directory exists — from the skill base directory the runner reports in context (e.g. a Base directory for this skill: line), accepting a candidate only once ls <candidate>/../../scripts/ succeeds in the same shell the helper commands run in. If a path form is rejected, use the form that shell reports (pwd shows it); a Windows-form base directory (C:\...) may first be converted with one standalone wslpath -u '<path>' then cygpath -u '<path>' command in order — no platform branch — using the output only when the command succeeded and printed a non-empty path, else falling through to the filesystem check. Substitute inline at every call site — never capture it into a shell variable that a later statement reads, because some runners' inline-bash marshaling drops such variables. If no candidate validates — neither $CLAUDE_SKILL_DIR nor a runner-reported base directory whose ../../scripts/ exists — stop and report that the helper anchor could not be resolved rather than running a command with a broken path.

This skill originates in the MIT-licensed superpowers plugin (© 2025 Jesse Vincent) and has been substantially modified by PRFlow.

Invoking it is the user's request to dispatch its reviewer. First load the repository's extension for this review, from the repository root:

bash
.prflow/vendor/prflow/scripts/load-prompt-extension.sh requesting-code-review

On command not found / No such file / exit 127, use the anchor form:

bash
"${CLAUDE_SKILL_DIR:-<absolute skill base directory this runner reports in context>}"/../../scripts/load-prompt-extension.sh requesting-code-review

Not-found on both forms is the anchor failure above: fix the anchor; do not report a missing extension. Any other non-zero exit means a consumer extension exists but could not be loaded: surface its stderr. No PROMPT-EXTENSION-STATUS: line on stderr, as after a refusal, is an unestablished extension to report, never an empty one.

Then dispatch the prflow:requesting-code-review agent with a prompt holding:

  • a one-line description of what was built;
  • the requirements or plan it must meet;
  • the base and head commits (e.g. origin/main and HEAD);
  • on content-present, Consumer review policy: and the extension's text: the helper's output minus every diagnostic it prints, even one run onto the text's last line.

For test edits, read ../fix/references/fix-discipline.md from this skill's directory; include its Tests evidence. Stop if unreadable.

Fix Critical findings at once and Important ones before moving on; push back with technical reasoning, and the code or tests that prove it, when the reviewer is wrong.

Frequently asked questions

What does the Requesting Code Review AI skill do?

Use when completing a task or major feature, or before merging, to have PRFlow's final-pass reviewer check the work against its requirements.

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/The01Geek/prflow/tree/main/skills/requesting-code-review. TypingMind reads its SKILL.md 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?

Yes. It is published on GitHub by The01Geek 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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