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 tmolavi/mcp-agent-skills-hub --skill github-workflow-automationgit clone --depth 1 https://github.com/tmolavi/mcp-agent-skills-hubWrote 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/tmolavi/mcp-agent-skills-hub/github-workflow-automation)<a href="https://agentmods.dev/skills/tmolavi/mcp-agent-skills-hub/github-workflow-automation"><img src="https://agentmods.dev/badge/skills/tmolavi/mcp-agent-skills-hub/github-workflow-automation/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/tmolavi/mcp-agent-skills-hub/github-workflow-automation"><img src="https://agentmods.dev/badge/skills/tmolavi/mcp-agent-skills-hub/github-workflow-automation.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 | $0.00043 | $0.05245 |
| Opus 5 | $0.00022 | $0.02622 |
| Sonnet 5 | $0.00009 | $0.01049 |
| Haiku 4.5 | $0.00004 | $0.00524 |
Grade A, and why
github-workflow-automation scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
98% identical to github-workflow-automation — 9 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 855 lines — stays where its author put it; the contents beside it link to each section on GitHub.
🔧 GitHub Workflow Automation
Patterns for automating GitHub workflows with AI assistance, inspired by Gemini CLI and modern DevOps practices.
When to Use This Skill
Use this skill when:
- Automating PR reviews with AI
- Setting up issue triage automation
- Creating GitHub Actions workflows
- Integrating AI into CI/CD pipelines
- Automating Git operations (rebases, cherry-picks)
1. Automated PR Review
1.1 PR Review Action
# .github/workflows/ai-review.yml
name: AI Code Review
on:
pull_request:
types: [opened, synchronize]
jobs:
review:
runs-on: ubuntu-latest
permissions:
contents: read
pull-requests: write
steps:
- uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Get changed files
id: changed
run: |
files=$(git diff --name-only origin/${{ github.base_ref }}...HEAD)
echo "files<<EOF" >> $GITHUB_OUTPUT
echo "$files" >> $GITHUB_OUTPUT
echo "EOF" >> $GITHUB_OUTPUT
- name: Get diff
id: diff
run: |
diff=$(git diff origin/${{ github.base_ref }}...HEAD)
echo "diff<<EOF" >> $GITHUB_OUTPUT
echo "$diff" >> $GITHUB_OUTPUT
echo "EOF" >> $GITHUB_OUTPUT
- name: AI Review
uses: actions/github-script@v7
with:
script: |
const { Anthropic } = require('@anthropic-ai/sdk');
const client = new Anthropic({ apiKey: process.env.ANTHROPIC_API_KEY });
const response = await client.messages.create({
model: "claude-3-sonnet-20240229",
max_tokens: 4096,
messages: [{
role: "user",
content: `Review this PR diff and provide feedback:
Changed files: ${{ steps.changed.outputs.files }}
Diff:
${{ steps.diff.outputs.diff }}
Provide:
1. Summary of changes
2. Potential issues or bugs
3. Suggestions for improvement
4. Security concerns if any
Format as GitHub markdown.`
}]
});
await github.rest.pulls.createReview({
owner: context.repo.owner,
repo: context.repo.repo,
pull_number: context.issue.number,
body: response.content[0].text,
event: 'COMMENT'
});
env:
ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
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.
- 5d ago First seen · 855 lines · 43 tokens per session scan A 214aec5caf50
github-workflow-automation is a skill published in the GitHub repository tmolavi/mcp-agent-skills-hub (8 stars, last pushed 13d ago), licensed MIT. It adds 43 tokens to every session and 5,245 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 98% identical to github-workflow-automation, differing in 9 lines, and is treated as a copy.
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