
Standard collaboration patterns for all squad agents — worktree awareness, decisions, cross-agent communication
Standard collaboration patterns for all squad agents — worktree awareness, decisions, cross-agent communication
Shared hard rules enforced across all squad agents
Defensive CI/CD patterns: semver validation, token checks, retry logic, draft detection — earned from v0.8.22
# Skill: CLI Command Wiring **Bug class:** Commands implemented in `packages/squad-cli/src/cli/commands/` but never routed in `cli-entry.ts`. ## Checklist — Adding a New CLI Command 1. **Create command file** in `packages/squad-cli/src/cli/commands/<name>.ts` - Export a `run<Name>(cwd, options)` async function (or class with static methods for utility modules) 2. **Add routing block** in `packages/squad-cli/src/cli-entry.ts` inside `main()`: ```ts if (cmd === '<name>') {
How to contribute, build, test
Coordinating work across multiple Squad instances
Microsoft Style Guide + Squad-specific documentation patterns
Microsoft Style Guide + Squad-specific documentation patterns
Shifts Layer 3 model selection to cost-optimized alternatives when economy mode is active.
PAO workflow for scanning, drafting, and presenting community responses with human review gate
PAO workflow for scanning, drafting, and presenting community responses with human review gate
Safely manage multiple GitHub identities (EMU + personal) in agent workflows
Detect and set up account-locked gh aliases for multi-account GitHub. The AI reads this skill, detects accounts, asks the user which is personal/work, and runs the setup automatically.
Squad branching model: dev-first workflow with insiders preview channel
Idiomatic Go patterns, best practices, and conventions for building robust, efficient, and maintainable Go applications.
Pluggable grader architecture (6 types, gate semantics)
Tone enforcement patterns for external-facing community responses
Tone enforcement patterns for external-facing community responses
# Java SDK Validation Skill You are a **Java Azure SDK validation reviewer** for generated code samples. Your job is to check whether generated Java code follows modern Azure SDK for Java conventions and flag violations of common anti-patterns that LLMs frequently produce. ## Rules 1. **NEVER modify generated code.** You are evaluating, not fixing. 2. Report all findings honestly — pass or fail with specific evidence. 3. Check every rule below. A single violation in a category means that cate
slog usage, no third-party logging
Determines which LLM model to use for each agent spawn based on 4-layer hierarchy
Context hygiene — compress, prune, archive .squad/ state
User-level personal agents that travel across projects via Ghost Protocol
Session creation, workspace isolation, cleanup
How properties map works, snake_case convention
Team-wide charter and history optimization through skill extraction
Sets up build environments for generated Azure SDK code samples and attempts to compile/build without modifying generated files. Use during review to verify code compiles correctly.
Identifies Azure SDK packages in generated code and checks whether they are the latest available versions. Use during code review to catch outdated dependencies.
Never read .env files or write secrets to .squad/ committed files
Never read .env files or write secrets to .squad/ committed files
Never read .env files or write secrets to .squad/ committed files
Core conventions and patterns used in the Squad codebase
Core conventions and patterns used in the Squad codebase
{what this skill teaches agents}
Table-driven tests, -race flag, stub patterns
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
Cobra commands in cmd/ package, flag conventions
Platform detection and adaptive spawning for CLI vs VS Code vs other surfaces
Generator/Reviewer sub-structs, property-based tool filters
How hyoka uses the Copilot SDK
Coordinating work across multiple Squad instances
How to coordinate with squads on different machines using git as transport
Shifts Layer 3 model selection to cost-optimized alternatives when economy mode is active.
Return errors up, no log-and-return, %w wrapping
How the eval engine works: generate → grade → report
Helps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express interest in extending capabilities. This skill should be used when the user is looking for functionality that might exist as an installable skill.
Detect and set up account-locked gh aliases for multi-account GitHub. The AI reads this skill, detects accounts, asks the user which is personal/work, and runs the setup automatically.
Record final outcomes to history.md, not intermediate requests or reversed decisions
Team initialization flow (Phase 1 proposal + Phase 2 creation)
Determines which LLM model to use for each agent spawn based on 4-layer hierarchy
User-level personal agents that travel across projects via Ghost Protocol
Core conventions and patterns for this codebase
Expert guidance for authoring evaluation prompts for hyoka, the Azure SDK Prompt Evaluation Tool. Use when creating, reviewing, or improving .prompt.md files that test AI agent code generation quality.
Prompt frontmatter format, properties, migration
JSON/HTML/Markdown reports, template system
Team-wide charter and history optimization through skill extraction
Reviewer rejection workflow and strict lockout semantics
API endpoints, SPA dashboard, path traversal protection
Find and resume interrupted Copilot CLI sessions using session_store queries
Update tests when changing APIs — no exceptions
Cross-platform path handling and command patterns
How to write comprehensive architectural proposals that drive alignment before code is written
Platform detection and adaptive spawning for CLI vs VS Code vs other surfaces
How to coordinate with squads on different machines using git as transport
Microsoft Style Guide + Squad-specific documentation patterns
Safely manage multiple GitHub identities (EMU + personal) in agent workflows
Record final outcomes to history.md, not intermediate requests or reversed decisions
Team initialization flow (Phase 1 proposal + Phase 2 creation)
Context hygiene — compress, prune, archive .squad/ state
Core conventions and patterns for this codebase
Reviewer rejection workflow and strict lockout semantics
Find and resume interrupted Copilot CLI sessions using session_store queries
Update tests when changing APIs — no exceptions
Cross-platform path handling and command patterns
How to coordinate with squads on different machines using git as transport
Step-by-step release checklist for Squad — prevents v0.8.22-style disasters
Find and resume interrupted Copilot CLI sessions using session_store queries
Core conventions and patterns used in the Squad codebase
Update tests when changing APIs — no exceptions
Cross-platform path handling and command patterns
How to write comprehensive architectural proposals that drive alignment before code is written
Master effective code review practices to provide constructive feedback, catch bugs early, and foster knowledge sharing while maintaining team morale. Use when reviewing pull requests, establishing review standards, or mentoring developers.
Platform detection and adaptive spawning for CLI vs VS Code vs other surfaces
PAO workflow for scanning, drafting, and presenting community responses with human review gate
Standard collaboration patterns for all squad agents — worktree awareness, decisions, cross-agent communication
Coordinating work across multiple Squad instances
Safely manage multiple GitHub identities (EMU + personal) in agent workflows
Shifts Layer 3 model selection to cost-optimized alternatives when economy mode is active.
Team initialization flow (Phase 1 proposal + Phase 2 creation)
Detect and set up account-locked gh aliases for multi-account GitHub. The AI reads this skill, detects accounts, asks the user which is personal/work, and runs the setup automatically.
Determines which LLM model to use for each agent spawn based on 4-layer hierarchy
User-level personal agents that travel across projects via Ghost Protocol
Core conventions and patterns for this codebase
Context hygiene — compress, prune, archive .squad/ state
Reads generated Azure SDK code files and adds inline review comments without changing any actual code. Use during code review to annotate quality issues, best practices, and suggestions.
Shared hard rules enforced across all squad agents
Record final outcomes to history.md, not intermediate requests or reversed decisions
Team-wide charter and history optimization through skill extraction
Reviewer rejection workflow and strict lockout semantics
Defensive CI/CD patterns: semver validation, token checks, retry logic, draft detection — earned from v0.8.22
Tone enforcement patterns for external-facing community responses