GitHub Agentic Workflows Gallery
The GitHub Agentic Workflows gallery shows how Markdown workflows can run AI agents through GitHub Actions for repository tasks that require reasoning, interpretation, investigation, or generation. Use this catalog to choose a starting point; each entry explains when the pattern is useful and links to maintained guidance or workflow source.
Gallery by Task
Section titled “Gallery by Task”| Task | When to use it |
|---|---|
| Issue Triage | Automatically classify new issues, identify duplicates, apply bounded labels, and ask for missing information. |
| Pull Request Review | Automatically inspect diffs for concrete defects and post review feedback through controlled safe outputs. |
| Documentation Maintenance | Automatically detect drift between code and documentation and propose reviewable updates. |
| CI Failure Investigation | Automatically analyze failed GitHub Actions runs, correlate logs, and open diagnostic issues with likely causes. |
| Code Improvement | Automatically find unnecessary complexity or duplicated logic and propose focused changes for human review. |
| Dependency Analysis | Automatically research dependency usage and upstream changes before creating prioritized follow-up work. |
| Metrics and Analytics | Automatically collect workflow activity and store structured snapshots for health and performance analysis. |
| Repository Reporting | Automatically summarize repository or release activity on an event or schedule. |
| Repository Maintenance | Automated repository assistance by reviewing a backlog, performing bounded maintenance tasks, and proposing controlled changes on a schedule. |
| Security Review | Automatically combine repository evidence with AI interpretation to report suspicious changes through code scanning. |
| Triage from Side Repo | Automatically triage a main repository from an isolated side repository through a slash-command bridge. |
| Code Quality Monitoring | Automatically analyze code quality across repositories and create focused, actionable issues. |
| Feature Synchronization | Automatically synchronize code and configuration across repositories through reviewable pull requests. |
| Cross-Repository Issue Tracking | Automatically aggregate and synchronize issue status in a central repository. |
| Dependabot Rollout | Automatically roll out tailored Dependabot configuration across multiple repositories. |
Gallery Workflows and AI Engines
Section titled “Gallery Workflows and AI Engines”Most gallery workflows specify the default Copilot engine or omit engine: entirely, so the published set is not evenly distributed across engines. Workflows are engine-portable: to run one on Claude, Codex, Gemini, or Pi, change engine: in the workflow frontmatter and configure that engine’s authentication secret. Engine-specific options such as engine.agent or engine.harness are not portable — see the engine feature comparison before switching.
Use a Gallery Workflow Safely
Section titled “Use a Gallery Workflow Safely”Before enabling a workflow, review its trigger, AI engine authentication, tools, network access, permissions, and safe outputs. Compile the Markdown source with gh aw compile, inspect both the .md and generated .lock.yml files, and begin with the narrowest permissions and outputs that satisfy the task.
Follow the quickstart to install gh-aw, read Create a New Workflow to adapt an example, compare AI engines, and review the security architecture and FAQ before deployment.