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 http-mcp-headersgit 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/http-mcp-headers)<a href="https://agentmods.dev/skills/github/gh-aw/http-mcp-headers"><img src="https://agentmods.dev/badge/skills/github/gh-aw/http-mcp-headers/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/http-mcp-headers"><img src="https://agentmods.dev/badge/skills/github/gh-aw/http-mcp-headers.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 | $0.01194 |
| Opus 5.5 | $0.00007 | $0.00478 |
| Sonnet 5.5 | $0.00004 | $0.00239 |
| Haiku 4.5 | $0.00002 | $0.00119 |
Grade C, and why
http-mcp-headers scanned grade C with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Harvests environment variableshighData exfiltration
Enumerating or grepping the environment for keys collects credentials unrelated to what the mod says it does.
1. Extract secrets from headers (e.g., `${{ secrets.DD_API_KEY }}`) How it starts
The opening of the file, as written. The whole thing — 152 lines — stays where its author put it; the contents beside it link to each section on GitHub.
HTTP MCP Header Secret Support - Implementation Summary
Use this reference for HTTP MCP header secret support in the copilot engine.
Problem Statement
When HTTP MCP headers include GitHub Actions secrets, mcp-config.json must:
- Extract secrets from headers (e.g.,
${{ secrets.DD_API_KEY }}) - Declare those env variables in the execution step
- Configure the MCP config's "env" section to passthrough those variables
- Use the passed variables in the headers section
Example Workflow
on:
workflow_dispatch:
permissions:
contents: read
engine: copilot
mcp-servers:
datadog:
type: http
url: "https://mcp.datadoghq.com/api/unstable/mcp-server/mcp"
headers:
DD_API_KEY: "${{ secrets.DD_API_KEY }}"
DD_APPLICATION_KEY: "${{ secrets.DD_APPLICATION_KEY }}"
DD_SITE: "${{ secrets.DD_SITE || 'datadoghq.com' }}"
allowed:
- search_datadog_dashboards
- search_datadog_slos
- search_datadog_metrics
- get_datadog_metric
# Datadog Dashboard Search
Search for Datadog dashboards and provide a summary.
Generated Output
1. MCP Config (mcp-config.json)
{
"mcpServers": {
"datadog": {
"type": "http",
"url": "https://mcp.datadoghq.com/api/unstable/mcp-server/mcp",
"headers": {
"DD_API_KEY": "${DD_API_KEY}",
"DD_APPLICATION_KEY": "${DD_APPLICATION_KEY}",
"DD_SITE": "${DD_SITE}"
},
"tools": [
"search_datadog_dashboards",
"search_datadog_slos",
"search_datadog_metrics",
"get_datadog_metric"
],
"env": {
"DD_API_KEY": "\\${DD_API_KEY}",
"DD_APPLICATION_KEY": "\\${DD_APPLICATION_KEY}",
"DD_SITE": "\\${DD_SITE}"
}
}
}
}
2. Execution Step Environment Variables
env:
DD_API_KEY: ${{ secrets.DD_API_KEY }}
DD_APPLICATION_KEY: ${{ secrets.DD_APPLICATION_KEY }}
DD_SITE: ${{ secrets.DD_SITE || 'datadoghq.com' }}
COPILOT_GITHUB_TOKEN: ${{ secrets.COPILOT_GITHUB_TOKEN }}
# ... other env vars
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.
- 2mo ago First seen · 152 lines · 18 tokens per session scan C 537d2fff1660
http-mcp-headers is a skill published in the GitHub repository github/gh-aw (5,579 stars, last pushed yesterday), licensed MIT. It adds 18 tokens to every session and 1,194 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it C with 1 finding (harvests environment variables). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
http-mcp-headers compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| http-mcp-headersgithub/gh-aw | Skill | 5.6k | yesterday | MIT | C | 18 | Claude Code, Codex |
| argot-suggest-rulesget-tmonier/argot | Skill | 52 | 4d ago | MIT | A | 173 | Claude Code |
| remix-expertpersonamanagmentlayer/pcl | Skill | 44 | 11d ago | Apache-2.0 | A | 70 | Claude Code |
| 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 |