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.
git clone --depth 1 https://github.com/github/gh-awInstalling this writes the configuration, not the program: `gh` has to be on your PATH already, or the server will not start.
Wrote 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/mcp/github/gh-aw/github-agentic-workflows)<a href="https://agentmods.dev/mcp/github/gh-aw/github-agentic-workflows"><img src="https://agentmods.dev/badge/mcp/github/gh-aw/github-agentic-workflows/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/mcp/github/gh-aw/github-agentic-workflows"><img src="https://agentmods.dev/badge/mcp/github/gh-aw/github-agentic-workflows.svg" alt="Reviewed on agentmods" width="80" height="20"></a>Grade A, and why
github-agentic-workflows 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.
This is a copy
100% identical to github-agentic-workflows — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
{
"github-agentic-workflows": {
"command": "gh",
"args": [
"aw",
"mcp-server"
],
"cwd": "${workspaceFolder}"
}
}What else mcp.json configures
This page is one entry in a file that holds 2. Installing the file brings all of them; each is measured and scanned on its own page.
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 · 10 lines scan A 01a660d578c1
github-agentic-workflows is an MCP server published in the GitHub repository github/gh-aw (5,469 stars, last pushed today), licensed MIT. Its token cost is not measured: an MCP server costs its tool schemas, not its config file. A static security scan graded it A with 0 findings. It is 100% identical to github-agentic-workflows, differing in 0 lines, and is treated as a copy.
Other mcp servers, from other repositories
CodSpeed
MCP server "CodSpeed", hosted remotely at mcp.codspeed.io, as configured in CodSpeedHQ/codspeed.
mcp
Local-first visual regression for AI agents: verdicts, diff images, explainsnapshot. No API key. Runs locally from the @testivai/mcp npm package.
gha-doctor
MCP server "gha-doctor" as configured in linnea-bakshi/gha-doctor. Runs in Docker (ghcr.io/linnea-bakshi/gha-doctor:0.66.0). Needs 1 environment variable to run.
gonzalgo
Measure where a formal library spends its axioms: Lean 4 and Metamath. Runs locally from the gonzalgo Python package.
slop-eval
Scores AI-generated UI for genericness ("slop") using an Anthropic LLM-judge rubric, with a --json mode for CI/agent quality gates and a documented programmatic API (scorecomposite, ScoreInput, LLMJudgeSource) for in-process use by agent frameworks. Runs locally from the slop-eval-cli Python package.
constitution-lint
Static linter for CLAUDE.md-style agent constitution files: 10 operational-guardrail checks. Remote server at www.agentopskit.dev.