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 statistics-infographicgit 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/statistics-infographic)<a href="https://agentmods.dev/skills/github/gh-aw/statistics-infographic"><img src="https://agentmods.dev/badge/skills/github/gh-aw/statistics-infographic/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/statistics-infographic"><img src="https://agentmods.dev/badge/skills/github/gh-aw/statistics-infographic.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00027 | $0.01960 |
| Opus 5.5 | $0.00011 | $0.00784 |
| Sonnet 5.5 | $0.00005 | $0.00392 |
| Haiku 4.5 | $0.00003 | $0.00196 |
Grade A, and why
statistics-infographic 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 22d 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 — 179 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Repository statistics infographic
Use when asked to consolidate repository-statistics slides into one image, regenerate the infographic with updated data, or verify its calculations. This is a reusable eight-panel statistical template, not an arbitrary PowerPoint screenshot collage.
The panels cover inferred PR initiation, merge/closed outcomes, start-to-merge durations, community issue outcomes, Go/JavaScript code composition, and weekly first-parent code changes, plus overlapping histograms of lines added and removed for individual PRs, and mean comments/commits per PR for human versus agent/bot comment-writer and primary-commit-author accounts. Repository names, dates, counts, proportions, and weeks come from the input, never from this skill's original dataset.
Generate
Prefer already collected source evidence. Do not fetch repository data, trigger workflows, or invent missing statistics. Keep raw data and generated images out of the repository. Use a temporary or session artifact directory.
Create a Python 3.9+ virtual environment and install the runtime manifest:
python3 -m venv /tmp/statistics-infographic-venv
/tmp/statistics-infographic-venv/bin/python -m pip install \
-r .github/skills/statistics-infographic/requirements.txt
Supply either one JSON object following examples/example.json, or a directory of existing summaries:
/tmp/statistics-infographic-venv/bin/python \
.github/skills/statistics-infographic/scripts/render_infographic.py \
--input .github/skills/statistics-infographic/examples/example.json \
--output /tmp/statistics-infographic.png
For real data, replace --input with --data-dir /path/to/summaries.
See references/data-contract.md for schemas and
statistical definitions. The example is explicitly synthetic, not a gh-aw report.
Optional arguments: --title, --font-regular, and --font-bold. Supply both font
paths together. Automatic discovery supports Arial on macOS/Windows and Liberation
Sans or DejaVu Sans on Linux. Missing fonts fail explicitly; no bitmap fallback is
used.
What ships with it
10 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- examples/example.json 4.3 KB
- references/data-contract.md 13 KB
- requirements-dev.txt 55 B
- requirements.txt 17 B
- scripts/audit_sources.py 17 KB runs code
- scripts/compute_pr_activity.py 2.5 KB runs code
- scripts/compute_pr_changes.py 3.2 KB runs code
- scripts/render_infographic.py 21 KB runs code
- scripts/statistics_data.py 23 KB runs code
- tests/test_statistics.py 31 KB runs code
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.
- 22d ago Changed · +69 lines · -1 tokens per session c2e3f0eff3ac
- 29d ago First seen · 110 lines · 28 tokens per session scan A 49f1df856cce
statistics-infographic is a skill published in the GitHub repository github/gh-aw (5,579 stars, last pushed yesterday), licensed MIT. It adds 27 tokens to every session and 1,960 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 2026-10-09.
statistics-infographic compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| statistics-infographicgithub/gh-aw | Skill | 5.6k | yesterday | MIT | A | 27 | Claude Code, Codex |
| watch-prjustincampbell/gh-watch | Skill | 4 | 7d ago | MIT | A | 48 | Claude Code |
| watch-tagjustincampbell/gh-watch | Skill | 4 | 7d ago | MIT | A | 62 | Claude Code |
| watch-branchjustincampbell/gh-watch | Skill | 4 | 7d ago | MIT | A | 45 | Claude Code |
| watch-commitjustincampbell/gh-watch | Skill | 4 | 7d ago | MIT | A | 48 | Claude Code |
| c-githubdaxaur/openpaw | Skill | 177 | 5mo ago | MIT | A | 48 | Claude Code, Codex |
| update-architecture-docsAlexSkrypnyk/scaffold | Skill | 11 | yesterday | GPL-3.0 | A | 46 | Claude Code |