Analyze a codebase to document its technologies, architecture, dependencies, and implementation patterns. Produce a Markdown reference with optional diagrams, code examples, and guidance for adding features consistently with the existing design.
Edit patch release notes for GitHub Enterprise Server, correcting typos and style while preserving approved technical facts. Keep repeated notes consistent across versions and update known issue source comments so future releases inherit the same edits.
Edit and validate Bazel build settings using conventions specific to CodeQL development. Follow established helpers for packaging and operating system differences, add dependencies with checksums, and check changed targets while respecting the standalone build setup.
Maintain reference test data and expected results for model-routing audits, which analyze how AI models are selected. Investigate mismatches, update outputs after intentional behavior changes, and add captured workflow runs as new test cases.
Guide visual changes to an existing documentation site and landing page. Apply defined limits for text width, spacing, navigation, and color so layout updates preserve readable content and a consistent visual style.
Turns collected repository statistics into an eight-panel PNG infographic. It shows pull request outcomes, timing, code changes, and human versus agent activity, with scripts to recalculate figures and check them against available source records.
Review cross-platform code and Git commands for filename, path, and shell compatibility issues. Apply shared timestamp utilities, platform-aware path handling, and file-based commit messages when working across Windows, macOS, and Linux.
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.
Set shared development rules for an agent team: keep specific agent names out of product code, tests, and CI configuration, verify affected tests before finishing, and record lessons learned from mistakes.
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.
Choose how an AI team handles each task, from a direct coordinator answer to parallel work by several agents. Includes decision criteria, example requests, and a short assignment template for small tasks.
Consult Squad’s file ownership and authority rules to decide where state belongs, who may read or write it, and which source takes precedence. Distinguish authoritative configuration from append-only histories and reference material.
Prepare architecture proposals before implementation so the team can agree on a direction. Organize evidence, technical choices, effects on existing work, risks, and the first release’s scope into a document with clear recommendations and decision owners.
Convert SKILL.md files into SSL JSON, a structured format describing invocation conditions, workflow stages, and individual actions. Receive a validation report covering format errors, invalid references, and rejected outputs.
Set up personal AI agents that join Squad sessions across projects. Manage their roles in a user configuration directory, inspect the combined team, and use agents that read project state and offer advice without writing to it.
Manage rejected work in an AI team by assigning revisions to a different agent. Defines author lockouts, independent revision requirements, specialist escalation, and when the coordinator must ask the user to resolve a deadlock.
Document project-specific development rules for an AI agent using a starter template. It provides sections for error handling, testing, code style, file organization, examples, and practices to avoid, ready to fill with actual conventions.
Guide changes to the Squad CLI using its established coding conventions. Covers Node.js built-ins, standard tests, readable errors, cross-platform paths, and initialization and upgrade behavior that preserves user-owned files.
Learn how to start an AI team with Squad in Copilot CLI. This reference explains how to invoke the coordinator agent, find the command catalog, and initialize a new project while avoiding common invocation mistakes.
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.
Follow Squad's Git workflow from issue branches through pull requests and cleanup. It covers development from dev, isolated directories for parallel issues, coordinated changes across repositories, and promotion to preview, insiders, and stable releases.
Coordinate work across independent Squad installations by discovering their capabilities and delegating tasks across repositories. It guides context sharing, acceptance criteria, and progress tracking, while distinguishing persistent Squad teams from temporary groups of agents.
Adapt Squad agent coordination to the available Copilot interface. Detect supported tools, choose how to launch helper agents and collect results, and use file-based state for workflows that must work across interfaces.
Coordinate work across AI agents with shared rules for locating team files, recording decisions and their reasons, and requesting help. It also defines who owns revisions after a reviewer rejects an artifact.
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