Set up separate commands for personal and work GitHub accounts on Windows. Detects accounts already signed in through GitHub CLI, asks which serves each role, creates PowerShell shortcuts and CMD wrappers, and verifies the selected usernames.
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Find interrupted Copilot CLI sessions in stored history, review conversations, checkpoints, files, and linked issues or pull requests, then resume unfinished work with the context needed to understand where it stopped.
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Clean up stored agent state in `.squad/` by compressing histories, pruning old logs, archiving stale decisions, and removing orphaned inbox files. Use it after long sessions or before starting work with many agents to reclaim context space.
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Guide agents in handling credentials safely by avoiding live environment files, using placeholders in shared records, checking staged changes before commits, and escalating secrets already exposed in Git history.
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Review technical claims and deliverables by checking evidence and testing alternative explanations. Confirm that references and API endpoints exist, then report verified, unverified, or contradicted claims alongside evidence and an overall review verdict.
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Set up a Squad agent team in a repository that has no team yet. Propose roles and persistent names, obtain user confirmation, then create team files, shared context, built-in member policies, and merge settings for accumulated records.
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Find and select Squad operations through a categorized menu, from managing an AI team and GitHub issues to choosing models and reviewing activity. The skill collects required options and routes requests to CLI commands or the coordinator agent.
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Validate changes to Squad coordinator and agent templates through real sessions using a locally built CLI. Check generated files, agent behavior, and saved state, then record evidence, scenario verdicts, and progress for pull request reviews.
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Add a new safe-output message type to gh-aw, connecting configuration validation, Go parsing, and JavaScript rendering. Follow the required updates through tests, exports, documentation, and generated action bundles so the message reaches workflow output.
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Create and manage coding tasks for GitHub Copilot from the command line. The skill covers writing clear implementation requests, listing and updating sessions, and connecting task creation to automated GitHub workflows that produce pull requests for review.
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Analyze token usage and AI costs in GitHub Agentic Workflows, identify expensive steps and budget or timeout failures, and develop prioritized improvements checked against the original workflow’s output quality.
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Query GitHub Discussions and extract titles, authors, categories, or answer status. First inspect the data structure and size, then use jq, a JSON filtering tool, to select relevant discussions, sort updates, or count entries by category.
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Guide gh-aw release preparation and assess whether command-line changes break existing usage. The reference covers changesets, which record release changes, version previews, release steps, firewall log classification, and summaries of Go module usage.
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Query GitHub pull requests by author, status, or review needs, and extract selected fields as JSON. Inspect the data structure and size first, then use jq, a JSON filtering tool, to refine results.
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Implement HTTP headers that pass GitHub Actions secrets through environment variables in gh-aw’s copilot engine. This reference explains secret extraction, generated MCP configuration, execution-step settings, and tests for keeping secret values out of configuration files.
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Use concrete JSON and Markdown examples to structure Sergo tool caches, strategy histories, improvement tasks, and reports. The templates show which fields to record and how to organize findings for review.
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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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Rewrite long prompts into shorter, clearer instructions for language models. Preserve essential requirements while removing repetition, defining scope, and making output formats and success criteria explicit to reduce ambiguity and unnecessary tokens.
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Resolve merge conflicts in generated workflow lock files by rebuilding them from merged Markdown sources. The skill checks for leftover conflict markers, stages regenerated files, and verifies that unresolved paths and whitespace errors are cleared.
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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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Review and implement gh-aw workflows and Go code with security checks for untrusted input, shell commands, dependency versions, and permissions. The skill also covers event-specific conditions and tools that inspect workflows for errors and security risks without running them.
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Guides the refactoring of large JavaScript files into smaller CommonJS modules within gh-aw. It covers source placement, function exports, tests, and action builds so extracted code remains testable and is included in generated actions.
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Follow gh-aw development conventions with checks matched to Go, JavaScript, and workflow changes. Provides staged validation commands, capitalization rules, and pointers to more detailed guidance for specific parts of the codebase.
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