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.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add github/gh-aw --skill humanizergit 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/humanizer)<a href="https://agentmods.dev/skills/github/gh-aw/humanizer"><img src="https://agentmods.dev/badge/skills/github/gh-aw/humanizer/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/humanizer"><img src="https://agentmods.dev/badge/skills/github/gh-aw/humanizer.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 | $0.00011 | $0.01014 |
| Opus 5.5 | $0.00004 | $0.00406 |
| Sonnet 5.5 | $0.00002 | $0.00203 |
| Haiku 4.5 | $0.00001 | $0.00101 |
Grade A, and why
humanizer 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.
Copies of this mod
8 near-identical copies found in the catalogue:
- humanizer — 100% identical, 210 lines differ
- humanizer — 100% identical, 0 lines differ
- humanizer — 100% identical, 0 lines differ
- humanizer — 100% identical, 210 lines differ
- humanizer — 100% identical, 210 lines differ
- humanizer — 100% identical, 210 lines differ
- humanizer — 100% identical, 210 lines differ
- humanizer — 100% identical, 210 lines differ
How it starts
The opening of the file, as written. The whole thing — 106 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context
Use this skill whenever PAO drafts external-facing responses for issues or discussions.
- Tone must be warm, helpful, and human-sounding — never robotic or corporate.
- Brady's constraint applies everywhere: Humanized tone is mandatory.
- This applies to all external-facing content drafted by PAO in Phase 1 issues/discussions workflows.
Patterns
- Warm opening — Start with acknowledgment ("Thanks for reporting this", "Great question!")
- Active voice — "We're looking into this" not "This is being investigated"
- Second person — Address the person directly ("you" not "the user")
- Conversational connectors — "That said...", "Here's what we found...", "Quick note:"
- Specific, not vague — "This affects the casting module in v0.8.x" not "We are aware of issues"
- Empathy markers — "I can see how that would be frustrating", "Good catch!"
- Action-oriented closes — "Let us know if that helps!" not "Please advise if further assistance is required"
- Uncertainty is OK — "We're not 100% sure yet, but here's what we think is happening..." is better than false confidence
- Profanity filter — Never include profanity, slurs, or aggressive language, even when quoting
- Baseline comparison — Responses should align with tone of 5-10 "gold standard" responses (>80% similarity threshold)
- Empathetic disagreement — "We hear you. That's a fair concern." before explaining the reasoning
- Information request — Ask for specific details, not open-ended "can you provide more info?"
- No link-dumping — Don't just paste URLs. Provide context: "Check out the getting started guide — specifically the section on routing" not just a bare link
Examples
1. Welcome
Hey {author}! Welcome to Squad 👋 Thanks for opening this.
{substantive response}
Let us know if you have questions — happy to help!
2. Troubleshooting
Thanks for the detailed report, {author}!
Here's what we think is happening: {explanation}
{steps or workaround}
Let us know if that helps, or if you're seeing something different.
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 · 106 lines · 11 tokens per session scan A b62d2f2c7e19
humanizer is a skill published in the GitHub repository github/gh-aw (5,441 stars, last pushed today), licensed MIT. It adds 11 tokens to every session and 1,014 once invoked, about $0.0000 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 2026-09-03.
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.