pr-writing-review
Extract and analyze writing improvements from GitHub PR review comments. Use when asked to show review feedback, style changes, or editorial improvements from a GitHub pull request URL. Handles both explicit suggestions and plain text feedback. Produces structured output comparing original phrasing with reviewer suggestions to help refine future writing.
Оригинален текст · няма потвърден превод на българскиПърво прочетете инструкцията.
Файлът се зарежда от посочения източник само по ваше действие. Можете да го прочетете тук, да го копирате или да го запазите като .md.
GitHub хранилище- Звезди на репото
- 3920
- Лиценз
- Apache-2.0
- Последна проверка на записа
- 22.09.2026 г.
Добавете в своя инструмент.
Командата отваря избора на skills от репозитория. Прочетете файла и условията на автора преди изпълнение.
npx skills add evalstate/fast-agentСтраницата копира текст. Не инсталира и не изпълнява нищо на устройството ви.
Откъс от SKILL.md
Началото на файла на автора, без превод.
PR Writing Review
Extract editorial feedback from GitHub PRs to learn from review improvements.
Prerequisites
- GitHub CLI:
ghinstalled - Authenticated
ghsession:gh auth statusshould show you’re logged in- For private repos, your token needs appropriate scopes (typically
repo).
- For private repos, your token needs appropriate scopes (typically
- Python: 3.12+
- uv (recommended): https://github.com/astral-sh/uv
Division of Labor
| Tool | Responsibility |
|---|---|
| Python script | API calls, parsing, file tracking across renames, structured extraction |
| LLM analysis | Pattern recognition, paragraph comparison, style lesson synthesis |
Quick Start
> **All paths are relative to the directory containing this SKILL.md file.**
> Before running any script, first `cd` to that directory or use the full path.
# Get suggestions and feedback
uv run scripts/extract_pr_reviews.py <pr_url>
# Get full first→final comparison for deep analysis
uv run scripts/extract_pr_reviews.py <pr_url> --diff
# Same as above, but cap each FIRST/FINAL dump to 2k chars for LLM prompting
uv run scripts/extract_pr_reviews.py <pr_url> --diff --max-file-chars 2000
Workflow
Step 1: Extract with --diff
uv run scripts/extract_pr_reviews.py https://github.com/org/repo/pull/123 --diff
This outputs:
- Explicit Suggestions — exact before/after text from
suggestionblocks (supports multiple suggestion blocks per comment) - Reviewer Feedback — plain text comments (the "why" behind changes)
- File Evolution — first draft and final version of each text file
Tip: add
--max-file-chars 2000to keep each FIRST/FINAL dump lightweight, or pair--diffwith--no-filesif you only need the suggestion/feedback summaries.
Step 2: Analyze the Output
With the script output, perform this analysis:
A. Catalog the Explicit Suggestions
Create a table of mechanical fixes:
| Pattern | Original | Fixed |
|---|---|---|
| Grammar | "Its easier" | "It's easier" |
| Filler removal | "using this way" | "this way" |
| Capitalization | "Image Generation" | "image generation" |
B. Map Feedback to Changes
For each reviewer feedback comment: