Verify skills work under pressure and resist rationalization using the RED-GREEN-REFACTOR cycle. Critical for discipline-enforcing skills.

Evaluate and improve Claude Code commands, skills, and agents. Use when testing prompt effectiveness, validating context engineering choices, or measuring improvement quality.

Comprehensive guide for skill development based on Anthropic's official best practices - use for complex skills requiring detailed structure

Interactive assistant for creating new Claude commands with proper structure, patterns, and MCP tool integration

Create and configure git hooks with intelligent project analysis, suggestions, and automated testing

Guide for creating effective `.claude/rules` files with contrastive examples that improve agent accuracy.

Guide for creating effective skills. This command should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations. Use when creating new skills, editing existing skills, or verifying skills work before deployment - applies TDD to process documentation by testing with subagents before writing, iterating until bulletproof against rationalization

Create a workflow command that orchestrates multi-step execution through sub-agents with file-based task prompts

Use this skill when you writing commands, hooks, skills for Agent, or prompts for sub agents or any other LLM interaction, including optimizing prompts, improving LLM outputs, or designing production prompt templates.

Use when creating or editing any prompt (commands, hooks, skills, subagent instructions) to verify it produces desired behavior - applies RED-GREEN-REFACTOR cycle to prompt engineering using subagents for isolated testing

Use when creating or editing skills, before deployment, to verify they work under pressure and resist rationalization - applies RED-GREEN-REFACTOR cycle to process documentation by running baseline without skill, writing to address failures, iterating to close loopholes

Use when tackling complex reasoning tasks requiring step-by-step logic, multi-step arithmetic, commonsense reasoning, symbolic manipulation, or problems where simple prompting fails - provides comprehensive guide to Chain-of-Thought and related prompting techniques (Zero-shot CoT, Self-Consistency, Tree of Thoughts, Least-to-Most, ReAct, PAL, Reflexion) with templates, decision matrices, and research-backed patterns

Update and maintain project documentation for local code changes using multi-agent workflow with tech-writer agents. Covers docs/, READMEs, JSDoc, and API documentation.

Execute complete FPF cycle from hypothesis generation to decision

Search the FPF knowledge base and display hypothesis details with assurance information

Analyze a GitHub issue and create a detailed technical specification

Add line-specific review comments to pull requests using GitHub CLI API

Create well-formatted commits with conventional commit messages and emoji

Create pull requests using GitHub CLI with proper templates and formatting

Use when adding metadata to commits without changing history, tracking review status, test results, code quality annotations, or supplementing commit messages post-hoc - provides git notes commands and patterns for attaching non-invasive metadata to Git objects.

Use when working on multiple branches simultaneously, context switching without stashing, reviewing PRs while developing, testing in isolation, or comparing implementations across branches - provides git worktree commands and workflow patterns for parallel development with multiple working directories.

Load all open issues from GitHub and save them as markdown files

Comprehensive A3 one-page problem analysis with root cause and action plan

Iterative PDCA cycle for systematic experimentation and continuous improvement

Guide for setup Context7 MCP server to load documentation for specific technologies.

Comprehensive multi-perspective review using specialized judges with debate and consensus building

Curates insights from reflections and critiques into CLAUDE.md using Agentic Context Engineering

Reflect on previus response and output, based on Self-refinement framework for iterative improvement with complexity triage and verification

Comprehensive review of local uncommitted changes using specialized agents with code improvement suggestions

Comprehensive pull request review using specialized agents

Execute tasks through competitive multi-agent generation, meta-judge evaluation specification, multi-judge evaluation, and evidence-based synthesis

Launch multiple sub-agents in parallel to execute tasks across files or targets with intelligent model selection, quality-focused prompting, and meta-judge → LLM-as-a-judge verification

Launch a meta-judge then a judge sub-agent to evaluate results produced in the current conversation

Launch an intelligent sub-agent with automatic model selection based on task complexity, specialized agent matching, Zero-shot CoT reasoning, and mandatory self-critique verification

Design multi-agent architectures for complex tasks. Use when single-agent context limits are exceeded, when tasks decompose naturally into subtasks, or when specializing agents improves quality.

Use when executing implementation plans with independent tasks in the current session or facing 3+ independent issues that can be investigated without shared state or dependencies - dispatches fresh subagent for each task with code review between tasks, enabling fast iteration with quality gates

Execute tasks through systematic exploration, pruning, and expansion using Tree of Thoughts methodology with meta-judge evaluation specifications and multi-agent evaluation

creates draft task file in .specs/tasks/draft/ with original user intent

Generate ideas in one shot using creative sampling

Implement a task with automated LLM-as-Judge verification for critical steps

Refine, parallelize, and verify a draft task specification into a fully planned implementation-ready task

Systematically fix all failing tests after business logic changes or refactoring

Use when implementing any feature or bugfix, before writing implementation code - write the test first, watch it fail, write minimal code to pass; ensures tests actually verify behavior by requiring failure first

Systematically add test coverage for all local code changes using specialized review and development agents. Add tests for uncommitted changes (including untracked files), or if everything is commited, then will cover latest commit.

Pause for review every N tasks - selective autonomy pattern

Applies prompt repetition to improve accuracy for non-reasoning LLMs

Integrate digital health data sources (Apple Health, Fitbit, Oura Ring) and connect to WellAlly.tech knowledge base. Import external health device data, standardize to local format, and recommend relevant WellAlly.tech knowledge base articles based on health data. Support generic CSV/JSON import, provide intelligent article recommendations, and help users better manage personal health data.

Styling and structure conventions for stream-chat-react. Use when adding or editing components, SCSS, or icons in this repo—file layout, styling folder structure, SCSS imports, and icon placement.

Analyze your Claude Code session logs to improve prompt quality, optimize tool usage, and become a better AI-native engineer.

22 production-ready scripts for iOS app testing, building, and automation. Provides semantic UI navigation, build automation, accessibility testing, and simulator lifecycle management. Optimized for AI agents with minimal token output.