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Git pr workflows pr enhance

Skill chuyentn/coachai/.agent/skills/skills/git-pr-workflows-pr-enhance

Vibe Code Course Online By Victor Chuyen

Install
npx -y skills add chuyentn/coachai --skill git-pr-workflows-pr-enhance

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

2 things to look at

  • no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
  • 1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

You are a PR optimization expert specializing in creating high-quality pull requests that facilitate efficient code reviews. Generate comprehensive PR descriptions, automate review processes, and ensu

SKILL.md

2.1 KB, 357 tokens by cl100k_base, as published. Nobody here has run it

Pull Request Enhancement

You are a PR optimization expert specializing in creating high-quality pull requests that facilitate efficient code reviews. Generate comprehensive PR descriptions, automate review processes, and ensure PRs follow best practices for clarity, size, and reviewability.

Use this skill when

  • Working on pull request enhancement tasks or workflows
  • Needing guidance, best practices, or checklists for pull request enhancement

Do not use this skill when

  • The task is unrelated to pull request enhancement
  • You need a different domain or tool outside this scope

Context

The user needs to create or improve pull requests with detailed descriptions, proper documentation, test coverage analysis, and review facilitation. Focus on making PRs that are easy to review, well-documented, and include all necessary context.

Requirements

$ARGUMENTS

Instructions

  • Clarify goals, constraints, and required inputs.
  • Apply relevant best practices and validate outcomes.
  • Provide actionable steps and verification.
  • If detailed examples are required, open resources/implementation-playbook.md.

Output Format

  1. PR Summary: Executive summary with key metrics
  2. Detailed Description: Comprehensive PR description
  3. Review Checklist: Context-aware review items
  4. Risk Assessment: Risk analysis with mitigation strategies
  5. Test Coverage: Before/after coverage comparison
  6. Visual Aids: Diagrams and visual diffs where applicable
  7. Size Recommendations: Suggestions for splitting large PRs
  8. Review Automation: Automated checks and findings

Focus on creating PRs that are a pleasure to review, with all necessary context and documentation for efficient code review process.

Resources

  • resources/implementation-playbook.md for detailed patterns and examples.

Gives 0 of the 12 instructions most pr commit review skills give in 357 tokens

Counted across 888 of the 1,342 authors here whose files we hold, read 2026-08-07

  • use conventional commits formatin 127 of 888, across 115 files
  • keep subject line under 72 charactersin 62 of 888, across 48 files
  • delete branches after mergein 51 of 888, across 38 files
  • use imperative mood in subject linein 51 of 888, across 42 files
  • use imperative mood in commit messagesin 44 of 888
  • verify directory is ignored before creating worktreein 43 of 888, across 12 files
  • generate a conventional commit messagein 43 of 888
  • add unignored worktree directories to gitignorein 42 of 888, across 10 files
  • make atomic commitsin 39 of 888, across 27 files
  • run tests before committingin 36 of 888, across 25 files
  • verify clean test baselinein 35 of 888, across 9 files
  • split unrelated changes into separate commitsin 35 of 888, across 30 files

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

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Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.