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Agent skill

Review PR Local

by Terry-Mao in Terry-Mao/AICodingFlow

Run the repository PR review workflow locally from the current branch using temporary-directory snapshots and the same review.json contract as CI.

MITAuto-check passedDevelopment

Install Review PR Local

skills CLI
$ npx skills add Terry-Mao/AICodingFlow --skill review-pr-local -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install Terry-Mao/AICodingFlow review-pr-local --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/Terry-Mao/AICodingFlow.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/review-pr-local .claude/skills/review-pr-local && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
review-pr-local
GitHub stars
167
Token cost
~518 tokens
SKILL.md length
234 words
Files
1
Skills in repo
30
Repo updated
First seen
Licence
MIT

At a glance

Run the repository PR review workflow locally from the current branch using temporary-directory snapshots and the same review.json contract as CI.

  • Works in 8 steps: From the repository root, prepare local… → Read the skill path printed by the… → Follow the selected skill exactly. It… → …
  • Tasks that involve Pull requests
  • SKILL.md covers Workflow and Safety Rules
  • Calls python3 and git

What it does

Review PR Local is an agent skill from Terry-Mao/AICodingFlow. Run the repository PR review workflow locally from the current branch using temporary-directory snapshots and the same review.json contract as CI.

Its SKILL.md is about 520 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Development, covering Pull requests. It works with GitHub. The repository describes itself as: Setup a AI Coding Flow. The licence is MIT.

When your agent uses it

  • Tasks that involve Pull requests

Example prompts

  • “/review-pr-local”

Requirements

  • Python 3

Workflow steps

8 steps, taken from the first numbered list in SKILL.md.

  1. From the repository root, prepare local review inputs. This prefers the
  2. Read the skill path printed by the command.
  3. Follow the selected skill exactly. It will apply any referenced local
  4. Use only the printed snapshot paths as review inputs
  5. Inspect repository files from the current repository root when the review
  6. Write the review output only to the printed review_path.
  7. Validate the review output
  8. Validate that the review phase did not mutate repository files

What it can do on your machine

Read from SKILL.md and the folder at commit 7703e16. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • python3
    • git

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use git, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Review PR Local loads about 518 tokens when it runs. Until then it costs about 41 tokens; SKILL.md has 234 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~41
When it runs · the whole SKILL.md, loaded when a task matches
~518

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from Terry-Mao/AICodingFlow at commit 7703e16, republished under its MIT licence (© Terry-Mao). 234 words, ~518 tokens.

Download SKILL.mdSave it as .claude/skills/review-pr-local/SKILL.md (or your agent's skills folder).
name
review-pr-local
description
Run the repository PR review workflow locally from the current branch using temporary-directory snapshots and the same review.json contract as CI.

review-pr-local

Use this skill after local implementation work and before pushing or creating a PR. It prepares the same review inputs used by the GitHub review workflow, then delegates review logic to review-pr.

Workflow

  1. From the repository root, prepare local review inputs. This prefers the GitHub PR associated with the current branch for pr_description.txt, then falls back to locally built PR metadata when the GitHub PR cannot be fetched. The pr_diff.txt snapshot is built from the local worktree diff. The command writes snapshots to a temporary directory and prints the selected review skill as skill=<path> plus exact file paths:
    bash
    python3 .github/scripts/prepare_local_review_inputs.py
  2. Read the skill path printed by the command.
  3. Follow the selected skill exactly. It will apply any referenced local companion guidance when present.
  4. Use only the printed snapshot paths as review inputs:
    • pr_description_path
    • pr_diff_path
    • spec_context_path when non-empty
  5. Inspect repository files from the current repository root when the review skill needs source context.
  6. Write the review output only to the printed review_path.
  7. Validate the review output:
    bash
    python3 .github/scripts/validate_review_json.py <pr_diff_path> <review_path>
  8. Validate that the review phase did not mutate repository files:
    bash
    python3 .github/scripts/validate_local_review_result.py \
  --baseline-status <baseline_status_path>

Safety Rules

  • After input preparation, do not run git add, git commit, git push, gh, or GitHub API commands.
  • Do not post comments or mutate GitHub state.
  • Do not modify source, workflow, tests, specs, or skill files.
  • If review discovers issues, report them through review.json; do not fix code during this skill.

© Terry-Mao, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .agents/skills/review-pr-local of Terry-Mao/AICodingFlow.

Open the folder on GitHubat commit 7703e16

Compare with similar skills

Review PR Local next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Review PR Local compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Review PR Local this skillTerry-Mao/AICodingFlow167—~518Automated safety check: PassMIT
PR Babysitteropeninterpreter/openinterpreter69k3 repos~4.2kAutomated safety check: PassApache-2.0
Check PRonyx-dot-app/onyx32k2 repos~2.3kAutomated safety check: PassMIT
Contributor-First PR MergeHKUDS/OpenHarness16k1 repos~847Automated safety check: PassMIT
Create Pull Requestcline/cline70k1 repos~1.6kAutomated safety check: PassApache-2.0
Pull Request Title and Body Writeropeninterpreter/openinterpreter69k2 repos~1.1kAutomated safety check: PassApache-2.0

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More from Terry-Mao/AICodingFlow

All 30 skills in this repo

  • PR Walkthrough

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  • Update PR Review

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  • Implement Issue

    Terry-Mao/AICodingFlow

    Implement a GitHub issue in this repository by applying the local shared implement-specs workflow with repository-specific issue, spec-context, and summary-file handling.

    167 GitHub stars~2.3k tokensUpdated 8 days ago
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  • Implement Specs

    Terry-Mao/AICodingFlow

    Implement an approved feature from the repository's product and tech specs, keeping specs and code aligned in the same change as implementation evolves.

    167 GitHub stars~1.7k tokensUpdated 8 days ago
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  • Review PR

    Terry-Mao/AICodingFlow

    Review a GitHub pull request from pinned prdescription.txt, prdiff.txt, and optional speccontext.md snapshots, then write and validate review.json.

    167 GitHub stars~1k tokensUpdated 8 days ago
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  • Update Dedupe

    Terry-Mao/AICodingFlow

    Learn repo-local duplicate issue guidance from recent maintainer duplicate closures and propose updates to the dedupe companion skill.

    167 GitHub stars~1.1k tokensUpdated 8 days ago
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Works with

Categories

Questions about Review PR Local

What does Review PR Local do?

Run the repository PR review workflow locally from the current branch using temporary-directory snapshots and the same review.json contract as CI. Review PR Local is an agent skill from Terry-Mao/AICodingFlow.json contract as CI.

When should I use Review PR Local?

Review PR Local fits situations like: tasks that involve Pull requests.

How do I install Review PR Local in Claude Code?

Run `npx skills add Terry-Mao/AICodingFlow --skill review-pr-local -a claude-code`. Or copy the skill folder (.agents/skills/review-pr-local in Terry-Mao/AICodingFlow) into .claude/skills/review-pr-local in your project. Claude Code loads it when a task matches its description.

How do I install Review PR Local in Codex?

Run `npx skills add Terry-Mao/AICodingFlow --skill review-pr-local -a codex`. Or copy the skill folder (.agents/skills/review-pr-local in Terry-Mao/AICodingFlow) into .agents/skills/review-pr-local in your project. Codex loads it when a task matches its description.

Can I use Review PR Local in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add Terry-Mao/AICodingFlow --skill review-pr-local -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/review-pr-local, .gemini/skills/review-pr-local, .github/skills/review-pr-local and .opencode/skills/review-pr-local in your project.

What does Review PR Local need to run?

Going by SKILL.md and its folder, Review PR Local needs the command-line tools its instructions call (python3 and git). Our summary lists: Python 3.

Does Review PR Local access the network?

SKILL.md contains no URLs. Its commands use git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Review PR Local safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Review PR Local use?

Review PR Local is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Review PR Local use?

About 518 tokens (SKILL.md is roughly 2.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Review PR Local?

Skills that share tags, products or a category with Review PR Local: PR Babysitter (openinterpreter/openinterpreter, 69k stars), Check PR (onyx-dot-app/onyx, 32k stars), Contributor-First PR Merge (HKUDS/OpenHarness, 16k stars) and Create Pull Request (cline/cline, 70k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Review PR Local?

Terry-Mao (a GitHub user) maintains it in Terry-Mao/AICodingFlow, which has 167 GitHub stars. The repository holds 30 skills in this directory. The repository was last updated on October 3, 2026.

Source: Terry-Mao/AICodingFlow on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.