GitHub Agentic Workflows (`gh-aw`) is a GitHub CLI extension for writing Agentic Workflows in markdown and compiling them to GitHub Actions.
github/gh-aw
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Showing 40 of 100 collected skills.
Add and test declarative behavior-defined agentic engines in gh-aw, extending Go infrastructure only when necessary.
Design and verify a deterministic operational-value grader for any GitHub Agentic Workflow. Use when reasoning from workflow goals to measurable downstream outcomes, defining repository evidence, choosing outcome metrics, or creating an operational-value…
Teach Copilot how to plan, address, and respond to pull request review feedback.
Prepare an open pull request for merge from a GitHub Copilot cloud agent. Drives Reviews, local validation, and Mergeable to a ready state. Does not merge, and cannot trigger CI.
Merge a base ref and safely regenerate compiled workflow lock-file conflicts.
Internal gh-aw architecture: validation system design, safe output message patterns, schema validation, YAML compatibility notes, and MCP logs guardrail.
Apply safe error-pattern matching rules for agentic engines.
Split large JavaScript files into maintainable modules safely.
Add new safe-output message types and wire validation/rendering.
Add temporary ID support to safe-output jobs end to end.
Query GitHub discussions with jq filtering and reusable selectors.
Review a GitHub security advisory and safely update .github/aw/compat.json with evidence-backed version enforcement.
Upgrade gh-aw to latest gh-aw-firewall release and identify follow-up spec tasks.
Core developer rules and coding conventions for gh-aw changes.
Publish validated gh-aw changes as a draft pull request from a cloud agent.
Define and validate GitHub custom agent files, prompts, and examples.
Review agentic workflow changes for correctness, security posture, and optimization opportunities with compile, validation, and audit evidence.
Analyze gh-aw OpenTelemetry traces from JSONL mirrors or OTLP backends.
{what this skill teaches agents}
Standard collaboration patterns for all squad agents — worktree awareness, decisions, cross-agent communication
Shared hard rules enforced across all squad agents
How to write comprehensive architectural proposals that drive alignment before code is written
Defensive CI/CD patterns: semver validation, token checks, retry logic, draft detection — earned from v0.8.22
Platform detection and adaptive spawning for CLI vs VS Code vs other surfaces
The complete two-phase Init Mode protocol the Squad coordinator runs when no team exists yet in the current repo. Phase 1 = propose the team (no files created, wait for user confirm). Phase 2 = create .squad/ scaffolding, casting state, .gitattributes for…
Selecting WHO handles work is the Routing table; selecting HOW they handle it (Direct, Lightweight, Standard, Full) is Response Mode. This skill contains the complete decision table, exemplar prompts for each mode, the Lightweight spawn template, and the…
The complete file-by-file source-of-truth hierarchy for Squad: which files are authoritative, which are derived/append-only, who may write each one, who may read each one, and the precedence rules when they conflict. Squad coordinator loads this on demand…
Enables squad agents on different machines to share work via git-based task queuing
Protocol for sending queries, delegating tasks, and sharing context between independent Squad instances across different repositories
Coordinating work across multiple Squad instances — discovery, delegation, and disambiguation when the user says 'squad' (the product) vs casual English 'group of agents'.
How to coordinate with squads on different machines using git as transport
Microsoft Style Guide + Squad-specific documentation patterns
End-to-end validation of coordinator and agent template changes
Shifts Layer 3 model selection to cost-optimized alternatives when economy mode is active.
Standard recovery patterns for all squad agents. When something fails, adapt — don't just report the failure.
PAO workflow for scanning, drafting, and presenting community responses with human review gate
Review and validate claims using counter-hypothesis testing. Use when verifying technical content, checking references, validating API endpoints, or performing quality assurance on deliverables.
Safely manage multiple GitHub identities (EMU + personal) in agent workflows
Squad branching model: dev-first workflow with insiders preview channel