A hands-on workshop that takes you from zero to a fully automated, AI-powered workflow — running on a schedule or on events in GitHub Actions.

• Agentic Workflows for GitHub Actions Power Users — one-page cheat sheet for what changes vs what stays the same in agentic workflows; branches from Step 5.
• Agentic Workflows Deep Dive — classification exercises, example agent output, the two-file structure, and concept checks; branches from Step 5.
• Terminal Basics — optional primer that branches from Step 1.
• Set Up Your Local Terminal — optional alternative for experienced terminal users who want to work on their own machine instead of in the recommended Codespace.
• Enterprise Setup Considerations — required reading for GHES users and useful for any managed enterprise environment; common return points include Step 1, Step 2, the Local Terminal side quest, and Step 5.
• Environment Reference — glossary of workshop environments and tool terms with official docs links; branches from Step 1.
• Permission Errors — troubleshooting guide for permission denied errors encountered during terminal-based steps; branches from Step 1.
• Install gh-aw Troubleshooting — optional install troubleshooting reference that branches from Step 6.
• Use gh-aw with the GitHub Copilot Cloud Agent — how to open a terminal inside the Copilot Cloud Agent Codespace so gh-aw CLI commands work; branches from Step 6.
• Install gh-aw in a Local Terminal — optional alternative for learners who completed the local terminal setup side quest and want to continue on their own machine.
• Using gh aw compile to Catch Errors Early — quick reference for gh aw compile, --validate, --watch, and common compile errors; branches from Step 7 or Step 9.
• Fix Codespaces actions:write Errors When Running gh aw run — troubleshooting guide for Codespaces workflow-trigger permission errors with an Actions-tab option and advanced recovery steps; branches from Step 8.
• Diagnosing Common Agent Output Patterns — expanded troubleshooting guide for the five most common log patterns; branches from Step 9.
• Pattern: Long [plan] Chains — how to spot a planning loop and rewrite your workflow brief so the agent starts with an explicit first tool call; branches from Diagnosing Common Agent Output Patterns.
• Pattern: Empty [result] Data — how to diagnose empty tool responses and decide whether the root cause is missing read scope, over-filtering, or truly empty repository data; branches from Diagnosing Common Agent Output Patterns.
• Pattern: Safe-output limit reached — how to interpret blocked writes and choose between increasing allowed outputs or constraining agent behavior; branches from Diagnosing Common Agent Output Patterns.
• Pattern: permission denied — how to map permission failures to the correct control: read access in permissions: and write allowlisting in safe-outputs:; branches from Diagnosing Common Agent Output Patterns.
• Pattern: "Done" but Nothing Written — how to diagnose successful runs that produce no write output and tighten instructions so expected writes happen reliably; branches from Diagnosing Common Agent Output Patterns.
• Debugging Checklist — a repeatable seven-step triage flow to apply whenever a run produces unexpected output; branches from Diagnosing Common Agent Output Patterns.
• Writing a Clear Agent Brief — five-step framework for designing any agentic workflow brief; branches from Step 10.
• Jailbreaking the Agent Brief — explains how adversarial instructions embedded in repository content attempt to override the agent's task brief, and how the compiled brief, minimal permissions:, safe-outputs, and network.allowed-domains contain any partial success; branches from Step 10.
• Frontmatter Deep Dive — Part A — walkthrough of the opening, trigger, and permissions sections of gh-aw frontmatter, with predict-and-try activities; branches from Step 7.
• Workflow File Structure at a Glance — visual map of the two-file structure (.md + .lock.yml), the frontmatter sections, and the Markdown body; branches from Step 7.
• Frontmatter Deep Dive — Part B — walkthrough of the tools, safe-outputs, closing fence, and agent body sections, with predict-and-try activities; continues from Part A.
• Pattern: Auto-Label PRs by Content — apply labels automatically based on which files changed in a pull request; branches from Step 14b.
• Pattern: Generate a PR Summary Comment — post a structured, changelog-ready summary comment when a pull request opens; branches from Step 14b.
• Pattern: PR Review Checklist — evaluate pull requests against a quality checklist and post a pass/fail table; branches from Step 14b.
• Observe and Reduce Token Costs — hands-on optimization activity for building an AIC baseline, auditing expensive runs, identifying cost drivers, and testing one token-reduction change at a time; branches from Step 14b.
• Fuzzy Schedule Expressions — quick reference for choosing between daily, hourly, weekly, and other fuzzy schedule expressions; branches from Step 9.
• Evaluating and Iterating on Agent Output — structured rubric for judging output quality, a five-row problem-to-fix reference table, and a one-change-at-a-time iteration loop; branches from Step 9.
• GitHub Actions Expressions and Contexts — deep dive into ${{ }} syntax, available context objects, output references, and if: conditions; branches from Step 15.
• Chaining Conditions — Run an Agent Only When Security Findings Exist — hands-on exercise for adding a Dependabot alert-count step and chaining it with a branch check so the agent only runs when there are real security findings; branches from Step 15.
• YAML Frontmatter Pitfalls — reference guide for the five most common YAML mistakes; branches from Step 7.
• Write Better AI Task Briefs — five prompt-engineering techniques for getting clearer, more consistent AI output; branches from Step 7.
• Explore and Adapt an Annotated Workflow — annotated daily-status.md walkthrough with hands-on edits to confirm each design decision; branches from Step 7.
• Event-Driven Triggers in Agentic Workflows — primer on choosing between pull_request, push, issues, and schedule, and on matching safe-outputs to the trigger; branches from Step 11c.
• Passing Data Between Steps with $GITHUB_OUTPUT — deep-dive into how $GITHUB_OUTPUT works; branches from Step 16.
• How MCP Tool Servers Work — conceptual primer explaining what MCP is, how the agentic loop changes, and how to read tool calls in the Actions log; branches from Step 17.
• Storing Credentials with GitHub Secrets — guide to creating repository secrets, referencing them in workflow steps, using the built-in GITHUB_TOKEN, and scoping permissions; branches from Step 16 or Step 17.
• Token and Secret Exfiltration in Agentic Workflows — explains how crafted repository content can attempt to leak tokens or API keys, and how log masking, safe-outputs, network.allowed-domains, and minimal permissions: stop it; branches from Step 16.
• Deterministic vs Agentic Data Ops — decision guide for splitting fixed data operations from agentic interpretation in hybrid workflows; branches from Step 16.
• Long-Lived Credential Risks in Agentic Workflows — explains why personal access tokens create a larger attack surface than the ephemeral GITHUB_TOKEN and how permissions: minimization and network.allowed-domains contain the blast radius; branches from Step 16.
• Agentic Workflow Security Architecture (Explain Like You're 5) — visual, beginner-friendly explanation of sandbox boundaries, where the agent runs, and what safe outputs look like; branches from Step 17.
• Prompt Injection Attacks in Agentic Workflows — explains what prompt injection is, how gh-aw's task brief, permissions:, and safe-outputs limit the impact, and what you can do as a workflow author; branches from Step 17.
• Permission Escalation in Agentic Workflows — explains how over-scoped workflow authority lets a misdirected agent attempt changes the task never needed, and how minimal permissions:, safe-outputs, and protected-files enforce least privilege; branches from Step 17.
• Supply Chain Attacks via MCP Tool Servers — explains how a compromised or malicious MCP server can feed poisoned tool results to your agent, and how network.allowed-domains, the explicit tools: block, minimal permissions, and safe-outputs reduce that risk; branches from Step 17.
• Output Injection via Safe Outputs — explains how crafted repository content can embed misleading markdown into agent output to fool human reviewers, and how safe-outputs surface declarations and label scoping prevent it; branches from Step 17.
• Repository Poisoning via Agentic Write Access — explains how a misdirected agent granted contents: write could be tricked into committing backdoors or overwriting sensitive files, and how contents: read, protected-files, and safe-outputs: create-pull-request close that path entirely; branches from Step 17.
• Configure GitHub Copilot for Agentic Workflows — explains organization centralized billing and personal COPILOT_GITHUB_TOKEN billing; branches from Step 7d.
• Method 1 — Copilot Requests Permission — use this when your organization has centralized Copilot billing enabled for GitHub Actions; branches from Configure GitHub Copilot for Agentic Workflows.
• Method 2 — COPILOT_GITHUB_TOKEN Secret — use this for personal billing or when centralized Copilot billing is not enabled; branches from Configure GitHub Copilot for Agentic Workflows.
• Method 2 (UI-only) — COPILOT_GITHUB_TOKEN Secret — complete personal-billing setup using only the GitHub web UI, without terminal commands; branches from Configure GitHub Copilot for Agentic Workflows.
• Configure an Anthropic API Key — step-by-step guide to generating an Anthropic key, storing it as a repository secret, and switching your workflow to engine: claude; branches from Step 7.
• Configure an OpenAI API Key — step-by-step guide to generating an OpenAI key, storing it as a repository secret, and switching your workflow to engine: codex; branches from Step 7.
• Choosing Between Cache Memory and Repo Memory — decision guide, full field references, and example task briefs for both cache-memory and repo-memory; branches from Step 20.
• Sub-Agent Syntax Reference — name rules, block boundary rules, supported frontmatter fields, and model alias table for inline sub-agents; branches from Step 21.
• Agent Session Phases Explained — full phase reference table, activity feed tips, steering prompts, and advanced agent merge / --watch paths; branches from Step 9.
• Audit Reference — Artifacts, Firewall Logs, and Report Contents — detailed breakdown of gh aw audit report fields, agent artifact files, ⌖ AIC billing, firewall.md, and network.allow; branches from Step 25.
• Self-Hosted Runner Infrastructure Deep Dive — enterprise infrastructure primer covering ephemeral and JIT runners, proxy configuration, and network isolation for air-gapped environments; branches from Step 24.
• Project Future AI Credit Costs with gh aw forecast — full walkthrough of gh aw forecast: reading P10/P50/P90 output, using --period week and --days 7, forecasting all workflows, and deriving a max-daily-ai-credits value from the P90 figure; branches from Step 26.

Start at Welcome — it shows you what you'll build and sets you up for success.