No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by github · Codex Skill · ★ 5.3k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is gh-aw safe to install? View the security audit →
GitHub Agentic Workflows Write agentic workflows in natural language markdown, and run them in GitHub Actions. Contents Quick Start Overview Guardrails Documentation Contributing Share Feedback Peli's Agent Factory Related Projects Quick Start Ready to get your first agentic workflow running? Follow our step-by-step Quick Start Guide to install the extension, add a sample workflow, and see it in action. Overview Learn about the concepts behind agentic workflows, explore available workflow types, and understand how AI can automate your repository tasks. See How It Works. Guardrails Guardrails, safety and security are foundational to GitHub Agentic Workflows. Workflows run with read-only permissions by default, with write operations only allowed through sanitized saf
| Stars | 5,340 |
| Forks | 572 |
| Language | Go |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 59.2929228032192/100 |
| Open Issues | 465 |
| Last Updated | 2026-10-03 |
| Created | 2025-08-12 |
| Platforms | claude-code, codex, go |
| Est. Tokens | ~16k |
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gh-aw is GitHub Agentic Workflows. It is categorized as a Codex Skill with 5.3k GitHub stars.
gh-aw is primarily written in Go. It covers topics such as actions, cai, ci.
You can find installation instructions and usage details in the gh-aw GitHub repository at github.com/github/gh-aw. The project has 5.3k stars and 572 forks, indicating an active community.
gh-aw is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to gh-aw on Agent Skills Hub include context-mode, skillshare, claude-code-settings. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: