github-agentic-workflows（GitHub agentic workflows 搜索）

🤖 GitHub Agentic Workflows Skill

Purpose

This skill provides comprehensive guidance on GitHub Agentic Workflows (gh-aw), a Go-based GitHub CLI extension that enables writing agentic workflows in natural language using Markdown files and running them as GitHub Actions workflows. Developed by GitHub Next and Microsoft Research, gh-aw delivers repository automation with strong guardrails, safe outputs, and sandboxed execution.

gh-aw augments existing deterministic CI/CD with Continuous AI capabilities — systematic, automated application of AI to software collaboration tasks like triaging issues, maintaining documentation, improving code quality, and automating reviews.

When to Use

Apply this skill when:

• Writing AI-powered repository automation as Markdown workflows
• Implementing Continuous AI patterns (issue triage, documentation sync, code review, quality improvement)
• Leveraging safe-outputs for write operations without granting direct write permissions
• Using multiple AI engines (Copilot, Claude, Codex) for event-triggered and scheduled jobs
• Building orchestrator-worker patterns for complex multi-agent coordination
• Automating tasks that traditionally require human judgment

5-Layer Security Architecture

gh-aw enforces defense-in-depth with five security layers:

1. Read-only tokens — Agent receives only read-scoped GitHub token
2. Zero secrets in agent — Write tokens/API keys exist only in separate isolated jobs
3. Containerized with network firewall — Agent Workflow Firewall (AWF) routes all outbound traffic through Squid proxy with domain allowlist
4. Safe outputs with guardrails — Agent produces structured artifacts; a separate gated job applies only permitted actions
5. Agentic threat detection — AI-powered scan of proposed changes before any write; blocks prompt injection, leaked credentials, malicious code

Rules

Workflow Structure

MUST:

• Create workflow files as Markdown (.md) in .github/workflows/
• Include YAML frontmatter between --- markers at the top
• Write natural language instructions below the frontmatter
• Compile to .lock.yml files using gh aw compile
• Commit both .md (source) and .lock.yml (compiled) files
• Use clear, imperative task descriptions in natural language
• Separate configuration (frontmatter) from instructions (body)

MUST NOT:

• Manually edit .lock.yml files (regenerate via compile)
• Push .md changes without recompiling
• Write complex YAML conditionals in workflow files
• Skip compilation step before deployment

Frontmatter Configuration

MUST:

• Define on: trigger(s) with appropriate event types and activity filters
• Set permissions: with specific resource scopes (e.g., issues: read, contents: read)
• Configure tools: with specific toolsets (e.g., github: with toolsets: [issues, labels])
• Include safe-outputs: for all write operations, using a hard limit where the output type supports it (e.g., max, max-size) or an allowlist where it does not (e.g., allowed)
• Set timeout-minutes: to prevent runaway workflows

MUST NOT:

• Use permissions: write-all without explicit security review
• Omit safe-outputs: for workflows that create/modify resources
• Hard-code secrets in frontmatter
• Grant unrestricted tool access

Natural Language Instructions

MUST:

• Write as if explaining a task to a colleague
• Use imperative mood ("Analyze this issue", "Create a summary")
• Include context, success criteria, and constraints
• Define expected outputs (comments, PRs, issues)
• Break complex tasks into clear numbered phases
• Specify what NOT to do when relevant
• Include examples of desired output format

MUST NOT:

• Write vague instructions ("Do something helpful")
• Assume the AI knows implicit repository context
• Mix code/YAML in instruction text

Engines (AI Models)

MUST:

• Use GitHub Copilot as default (no explicit engine: needed)
• For Claude: set engine: claude and configure ANTHROPIC_API_KEY secret
• For Codex: set engine: codex and configure OPENAI_API_KEY secret
• Test with chosen engine before production deployment

MUST NOT:

• Mix multiple engines in same workflow
• Assume identical capabilities across engines

Triggers

MUST:

• Choose appropriate trigger(s):

- issues: with types: [opened, reopened] for issue automation - pull_request: for PR-related automation - schedule: with human-friendly syntax (daily, weekly on monday) or cron - workflow_dispatch: for manual execution - slash_command: with command: for comment-triggered actions (e.g., /plan, /analyze)

• Consider rate limits and costs for scheduled workflows
• Test with workflow_dispatch before enabling automatic triggers

MUST NOT:

• Use overly frequent schedules that waste resources
• Trigger on every event type without necessity

Tools and MCP Integration

MUST:

• Configure tools in frontmatter with specific toolsets: tools: github: toolsets: [issues, labels, pull-requests]
• Use safe-inputs: for custom lightweight inline functions
• Use edit: tool for file modifications, bash: for shell commands
• Use web-search: or web-fetch: for external information with network restrictions
• Set network: allowlists when external access is needed
• Use min-integrity: for public repos to control event visibility

MUST NOT:

• Grant unrestricted tool access without review
• Bypass tool allowlists or network access controls

Compilation and Setup

MUST:

• Install: gh extension install github/gh-aw
• Initialize: gh aw init for new repositories
• Compile: gh aw compile (generates .lock.yml)
• Watch mode: gh aw compile --watch for development
• Test: gh aw run <workflow-name> for manual testing
• Logs: gh aw logs <workflow-name> for debugging
• Add community workflows: gh aw add-wizard <url>
• Use fine-grained PATs with minimal scopes

MUST NOT:

• Use classic PATs instead of fine-grained tokens
• Skip gh aw init for new repositories
• Commit secrets to version control

Examples

Example 1: Issue Triage (Real-World Pattern from Agent Factory)

---
timeout-minutes: 5
on:
  issues:
    types: [opened, reopened]
permissions:
  issues: read
tools:
  github:
    toolsets: [issues, labels]
safe-outputs:
  add-labels:
    allowed: [bug, feature, enhancement, documentation, question, "help wanted", "good first issue"]
  create-comment:
    max: 1
---

# Issue Triage Agent

Analyze the triggering issue (${{ github.event.issue.number }}) title and body,
then add one of the allowed labels: `bug`, `feature`, `enhancement`,
`documentation`, `question`, `help wanted`, or `good first issue`.

Skip the issue if it:
- Already has any of these labels
- Has been assigned to any user (especially non-bot users)

Do research on the issue in the context of the codebase and, after adding
the label, mention the issue author in a comment explaining why the label
was added and give a brief summary of how the issue may be addressed.

Example 2: Daily Status Report

---
on:
  schedule: daily
permissions:
  contents: read
  issues: read
  pull-requests: read
safe-outputs:
  create-issue:
    max: 1
    title-prefix: "[team-status] "
    labels: [report, daily-status]
    close-older-issues: true
---

## Daily Issues Report

Create an upbeat daily status report for the team as a GitHub issue.

## What to include

- Recent repository activity (issues, PRs, discussions, releases, code changes)
- Progress tracking, goal reminders and highlights
- Project status and recommendations
- Actionable next steps for maintainers

Example 3: Plan Command (Slash Command)

---
on:
  slash_command:
    command: /plan
permissions:
  issues: read
tools:
  github:
    toolsets: [issues]
safe-outputs:
  create-issue:
    max: 10
  create-comment:
    max: 1
---

# Plan Command

Break down the current issue into actionable sub-tasks.
Create child issues for each sub-task and link them.
Post a comment summarizing the plan with links to all created sub-issues.

Example 4: Network-Restricted Security Review

---
on: pull_request
timeout-minutes: 10
permissions:
  contents: read
  pull-requests: read
  security-events: read
tools:
  github:
    toolsets: [pull-requests, code-scanning]
network: defaults
safe-outputs:
  create-comment:
    max: 3
  threat-detection:
    enabled: true
    action: block
---

# Security-Focused PR Review

Review pull request for security issues. No external network access allowed.

Focus on:
- Hard-coded secrets or credentials
- Unsafe input handling
- Missing authentication checks
- Injection vulnerabilities

Example 5: Safe Inputs with Custom Tool

---
on: issues
permissions:
  issues: read
tools:
  github:
safe-inputs:
  calculate_priority:
    type: function
    description: Calculate issue priority based on labels and content
    code: |
      function calculate_priority(labels, body) {
        let score = 0;
        if (labels.includes('critical')) score += 10;
        if (labels.includes('security')) score += 8;
        if (labels.includes('bug')) score += 5;
        if (body.toLowerCase().includes('production')) score += 3;
        return Math.min(score, 10);
      }
safe-outputs:
  create-comment:
    max: 1
---

# Priority Calculator

Use the calculate_priority tool to assess issue priority.
Post a comment with the priority score and recommended action timeline.

Example 6: Multi-Engine Configuration

---
on: workflow_dispatch
engine: claude
permissions:
  contents: read
tools:
  github:
safe-outputs:
  create-issue:
    max: 1
---

# Advanced Analysis with Claude

Perform deep technical analysis of repository architecture.
Create an issue with findings and recommendations.

Note: Requires ANTHROPIC_API_KEY secret to be configured.

Best Practices

Start Simple, Iterate

• Begin with read-only workflows using workflow_dispatch
• Add safe-outputs incrementally; start with create-comment
• Use gh aw compile --watch for rapid iteration
• Graduate to scheduled triggers after manual testing

Clear Instructions Win

• Write as if explaining to a human colleague
• Include examples of desired output format
• Define constraints and guardrails explicitly
• Specify what NOT to do when relevant

Security First

• Use specific permissions (e.g., issues: read) not read-all
• Use safe-outputs constraints: title-prefix, labels, allowed, max
• Use network: {} for zero external access, network: defaults for GitHub-only
• Enable threat-detection for all safe-outputs workflows
• Use min-integrity: in public repos for event visibility control

Agent Factory Patterns (Proven at Scale)

The GitHub Next team operates 100+ workflows. Key learnings:

• Customized agents beat generic ones — tailor to your repo's context
• Incremental improvement beats heroic efforts — small daily PRs
• Observability is essential — track success rates and merge rates
• Meta-analysis reveals hidden patterns — use AI to analyze AI behavior
• Task decomposition enables coordination — /plan command + sub-issues

Monitor and Improve

• Track workflow success/merge rates
• Review AI output quality regularly
• Refine instructions based on actual behavior
• Use gh aw logs and GitHub Actions logs

Compilation Flow

1. Author:   .github/workflows/my-workflow.md
2. Compile:  gh aw compile → generates .lock.yml
3. Commit:   git add *.md *.lock.yml && git commit
4. Push:     git push
5. Secrets:  Configure API keys in repository settings
6. Test:     gh aw run my-workflow
7. Monitor:  gh aw logs my-workflow

Safe Outputs Reference

Troubleshooting

Related ISMS Policies

This skill aligns with:

• Secure Development Policy - Secure automation practices
• Access Control Policy - Least privilege for workflows
• Cryptographic Controls Policy - Secure secret management
• Information Security Policy - Overall security framework

Related Skills

• Agentic Workflow Security - Security best practices and threat detection
• Agentic Workflow Orchestration - Multi-agent patterns and coordination
• Agentic Workflow Development - CLI usage, testing, debugging
• Continuous AI Patterns - Automation patterns and best practices
• MCP Server Integration - Model Context Protocol servers
• Copilot Agent Patterns - Custom agent design patterns

Related Documentation

• GitHub Agentic Workflows Official Site
• Abridged LLM Documentation
• Full LLM Documentation
• Agent Factory Blog Series
• GitHub Blog: Automate Repository Tasks
• GitHub Actions Documentation

Compliance Mapping

ISO 27001:2022

• A.8.25 Secure development life cycle
• A.8.32 Change management
• A.5.23 Information security for use of cloud services

NIST Cybersecurity Framework 2.0

• GV.OV-03: Cybersecurity supply chain risk management
• PR.DS-02: Data-in-transit is protected
• DE.CM-07: Monitoring for unauthorized changes

CIS Controls v8.1

• Control 16: Application Software Security

- 16.1 Establish and Maintain a Secure Application Development Process - 16.11 Leverage Vetted Modules or Services for Application Security Components

Enforcement

Version History

• 2026-04-02: Major update with latest gh-aw v0.45+ features, 5-layer security architecture, real-world Agent Factory patterns, safe-outputs reference table
• 2026-02-11: Initial skill creation