Create GitHub Copilot coding tasks from plain-language instructions using the GitHub CLI extension. Tasks become issues that trigger code changes and pull requests for review, with commands to list tasks, inspect details, and update instructions.
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Define and check GitHub Copilot instructions and specialized agents using Markdown files. It explains file locations, configuration fields, tool access, path-specific rules, and workflow integration, with examples for documentation, testing, reviews, and other development tasks.
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Format gh-aw terminal output using Go struct tags, annotations attached to data fields. Set labels and section titles, format numbers and dollar amounts, omit empty fields, and render lists of records as tables.
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Update the gh-aw-firewall version used by gh-aw, rebuild generated files, and review release notes and configuration schemas, which define valid settings. It guides related code and test updates while identifying follow-up work that remains.
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Prepare an open pull request for human merging from a GitHub Copilot cloud agent. Address review threads, validate changes locally, resolve branch conflicts, and report remaining blockers, including the need for a human to rerun CI.
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Review git and gh operations in gh-aw workflows to check credential availability for each repository. Identify missing token handoffs, risky expansions of limited checkouts, and error handling needed where credentials are intentionally unavailable.
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Guide changes to gh-aw internals, including workflow validation, session scoring, and GitHub output handling. Find implementation locations and design rules for configuration checks, YAML compatibility, and limits on log responses returned to agents.
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Write and edit gh-aw documentation using Diátaxis, a framework that separates learning, task instructions, reference, and explanations. Apply Astro Starlight Markdown conventions while keeping examples focused and removing repeated or unnecessary content.
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Use this reference to configure GitHub access for AI agents through MCP, a protocol for connecting agents to tools. Covers tool selection, authentication, read-only access, and migration of existing workflow settings.
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List GitHub Actions workflows with a script that lets you choose the page size and page number. Get JSON containing the total count, workflow names, file paths, states, timestamps, and links for inspection or further processing.
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Generate conventional commit messages - use when creating commits, writing commit messages, or asking for git commit help
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Generate comprehensive pytest tests - use when generating tests, creating test suites, or testing Python code
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A minimal skill example - use when learning the skill format
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Team code quality checklist - use for checking Python code quality, bugs, security issues, and best practices
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Manages stacked PRs and splits multi-part work into reviewable branches with gh-stack. Use for stack creation, viewing, edits, push, submit, sync, rebase, merge, or checkout; when asked to split or isolate work for review; whenever a user mentions a stack, branch layers, dependent PRs, or gh stack; or when a stack is checked out.
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Analyze C++ build performance using vcperf.exe and ETL traces. Use this skill when the user wants to profile a build, understand compile bottlenecks, identify expensive headers or templates, generate JSON analysis reports, or interpret vcperf output. Covers tracing workflows, the /jsonAnalysis flag, reading the resulting JSON schema, and actionable remediation strategies for each bottleneck category.
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Comprehensive guidance for building web apps with opinionated defaults for tech stack, design system, and code standards. Use when user wants to create a new web application, dashboard, or interactive interface. Provides tech choices, styling guidance, project structure, and design philosophy to get users up and running quickly with a fully functional, beautiful web app.
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Route gh-aw design, creation, diagnosis, patching, active debugging, and upgrade requests to the right strategies.
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Configure or diagnose file and directory access, mounts, and host ACLs for AWF sandboxes.
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Route gh-aw design, creation, diagnosis, patching, active debugging, and upgrade requests to the right strategies.
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Decide and implement how to support a new LLM provider or agent engine in AWF - either as a proxied provider (api-proxy adapter) or a direct-API engine (domain allowlist only), e.g. Cursor, Aider, or any tool that calls its own endpoint.
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Regenerate and post-process all agentic workflows. Use when gh-aw is updated, workflow .md files change, or when asked to recompile/regenerate workflows.
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Diagnose an AWF (Agentic Workflow Firewall) failure from an error, workflow run URL, or symptom. Covers auth (api-proxy, enterprise/BYOK Copilot, OIDC, mcpg), ARC/DinD and self-hosted runners, alternative runtimes (gVisor/Kata/chroot), Squid/DNS/egress denials, CI and gh-aw safe-output failures, and suspected security regressions. Routes to the canonical diagnosis registry in docs/diagnostics.
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Practical Python scripts for debugging awf - parse logs, diagnose issues, inspect containers, test domains
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