GitHub Agentic Workflows is a GitHub CLI extension that lets developers define AI-assisted repository automation in Markdown and run it through GitHub Actions. It is intended for tasks requiring interpretation or reasoning, such as issue triage, pull-request review, CI investigation, documentation maintenance, and dependency analysis. The catalogue entries provide skills and agents for working with these workflows.
Borrowing it
Nothing to install: this file belongs to github/gh-aw. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/github/gh-aw/main/.github/skills/documentation/SKILL.mdgit clone --depth 1 https://github.com/github/gh-awWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/github/gh-aw/documentation)<a href="https://agentmods.dev/skills/github/gh-aw/documentation"><img src="https://agentmods.dev/badge/skills/github/gh-aw/documentation/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/github/gh-aw/documentation"><img src="https://agentmods.dev/badge/skills/github/gh-aw/documentation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00018 | $0.01747 |
| Opus 5.5 | $0.00007 | $0.00699 |
| Sonnet 5.5 | $0.00004 | $0.00349 |
| Haiku 4.5 | $0.00002 | $0.00175 |
Grade A, and why
documentation scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 242 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Documentation
Documentation lives in docs/, uses GitHub-flavored Markdown, renders with Astro Starlight, and follows Diátaxis.
Diátaxis Framework
Organize documentation into four Diátaxis types:
1. Tutorials (Learning-Oriented)
Purpose: Guide beginners through achieving a specific outcome to build confidence.
- Start with what the user will build or achieve
- Provide a clear, step-by-step path from start to finish
- Include concrete examples and working code
- Assume minimal prior knowledge
- Focus on the happy path (avoid edge cases and alternatives)
- End with a working result the user can see and use
- Use imperative mood: "Create a file", "Run the command"
Avoid: Explaining concepts in depth, multiple options, troubleshooting
2. How-to Guides (Goal-Oriented)
Purpose: Show how to solve a specific real-world problem or accomplish a particular task.
- Title format: "How to [accomplish specific goal]"
- Assume the user knows the basics
- Focus on practical steps to solve one problem
- Include necessary context but stay focused
- Show multiple approaches only when genuinely useful
- End when the goal is achieved
- Use imperative mood: "Configure the setting", "Add the following"
Avoid: Teaching fundamentals, explaining every detail, being exhaustive
3. Reference (Information-Oriented)
Purpose: Provide accurate, complete technical descriptions of the system.
- Organized by structure (CLI commands, configuration options, API endpoints)
- Comprehensive and authoritative
- Consistent format across all entries
- Technical accuracy is paramount
- Include all parameters, options, and return values
- Use descriptive mood: "The command accepts", "Returns a string"
- Minimal narrative or explanation
Avoid: Instructions, tutorials, opinions on usage
4. Explanation (Understanding-Oriented)
Purpose: Clarify and illuminate topics to deepen understanding.
- Discuss why things are the way they are
- Explain design decisions and tradeoffs
- Provide context and background
- Connect concepts to help form mental models
- Discuss alternatives and their implications
- Use indicative mood: "This approach provides", "The engine uses"
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 242 lines · 18 tokens per session scan A a8142445a061
documentation is a skill published in the GitHub repository github/gh-aw (5,406 stars, last pushed today), licensed MIT. It adds 18 tokens to every session and 1,747 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
c-github
Interact with GitHub using the gh CLI and jq. Manage PRs, issues, repositories, and Actions workflows. Make raw API calls with gh api for anything not covered by built-in commands.
watch-pr
Watch a GitHub pull request for CI status, reviews, comments, merge conflicts, and terminal states using the gh-watch extension. Use when the user wants to monitor a PR, wait for CI, or track PR progress.
watch-tag
Watch a GitHub repository for new tags using the gh-watch extension. Use when the user wants to be notified when a tag is created, when a release is cut, or when a tag that includes a specific commit appears (e.g. "tell me when my merge ships in a release").
watch-branch
Watch a GitHub branch for new commits using the gh-watch extension. Use when the user wants to be notified when new commits are pushed to a branch, monitor main for merges, or track branch activity.
watch-commit
Watch a GitHub commit for CI status changes using the gh-watch extension. Use when the user wants to monitor a commit's CI checks, wait for a build to finish, or track CI progress on a specific SHA.
update-architecture-docs
Generate or update the architecture documentation in docs/content/architecture/. Use on "update architecture docs", "generate architecture documentation", "regenerate architecture docs", or after any structural change to the codebase.