Skill with injected eval patterns for security testing

Manages work transitions between team members or agents by creating structured handoff documents, summarizing project status, documenting key decisions, blockers, and open questions, and generating onboarding briefs. Use when someone needs to hand off, hand over, or transition a project; pass work to another person or agent; brief a colleague taking over; prepare a shift change summary; or onboard someone mid-task. Produces ready-to-use handoff documents covering current status, next steps, known issues, technical context, and communication templates for both planned and unplanned transfers.

Coordinates parallel investigation threads to simultaneously explore multiple hypotheses or root causes across different system areas. Use when debugging production incidents, slow API performance, multi-system integration failures, or complex bugs where the root cause is unclear and multiple plausible theories exist; when serial troubleshooting is too slow; or when multiple investigators can divide root-cause analysis work. Provides structured phases for problem decomposition, thread assignment, sync points with Continue/Pivot/Converge decisions, and final report synthesis.

Performs systematic root cause analysis to identify the true source of bugs, errors, and unexpected behavior through structured investigation phases — not just treating symptoms. Use when a user reports a bug, crash, error, or broken behavior and needs to debug, troubleshoot, or investigate why something is not working; especially for complex or intermittent issues across multiple components. Applies the Five Whys method, hypothesis-driven testing, stack trace analysis, git blame/log evidence gathering, and causal chain documentation to isolate and confirm root causes before applying any fix.

Creates and structures SKILL.md files for AI coding agents, including YAML frontmatter, trigger phrases, directive instructions, decision trees, code examples, and verification checklists. Use when the user asks to write a new skill, create a skill file, author agent capabilities, generate skill documentation, or define a skill template for Claude Code agents.

Guides the creation of technical design documents before writing code, producing architecture diagrams, data models, API interface definitions, implementation plans, and multi-option trade-off analyses. Use when the user asks to plan a feature, architect a system, design an API, explore implementation approaches, or requests a technical design or spec before coding — especially for complex features involving multiple components, ambiguous requirements, or significant architectural changes.

Creates explicit validation checkpoints (verification gates) between project phases to catch errors early and ensure quality before proceeding. Use when the user asks about quality gates, milestone checks, phase transitions, approval steps, go/no-go decision points, or preventing cascading errors across a multi-step workflow. Produces acceptance criteria checklists, automated CI gate configurations, manual sign-off requirements, and conditional review rules for scenarios such as security changes, API changes, or database migrations.

Discovers, searches, and installs skills from multiple AI agent skill marketplaces (400K+ skills) using the SkillKit CLI. Supports browsing official partner collections (Anthropic, Vercel, Supabase, Stripe, and more) and community repositories, searching by domain or technology, and installing specific skills from GitHub. Use when the user wants to find, browse, or install new agent skills, plugins, extensions, or add-ons; asks 'is there a skill for X' or 'find a skill for X'; wants to explore a skill store or marketplace; needs to extend agent capabilities in areas like React, testing, DevOps, security, or APIs; or says 'browse skills', 'search skill marketplace', 'install a skill', or 'what skills are available'.

Choose the right serialization format for .NET applications. Prefer schema-based formats (Protobuf, MessagePack) over reflection-based (Newtonsoft.Json). Use System.Text.Json with AOT source generators for JSON scenarios.

Guide for creating effective skills using a TDD-based approach. This command treats skill creation as Test-Driven Development applied to process documentation.

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

Understand the components, mechanics, and constraints of context in agent systems. Use when writing, editing, or optimizing commands, skills, or sub-agents prompts.

Comprehensive guide for creating Claude Code agents with proper structure, triggering conditions, system prompts, and validation - combines official Anthropic best practices with proven patterns

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

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

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

Apply writing rules to any documentation that humans will read. Makes your writing clearer, stronger, and more professional.

Reconcile the project's FPF state with recent repository changes

Manage evidence freshness by identifying stale decisions and providing governance actions

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

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.

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

Auto-selects best Kaizen method (Gemba Walk, Value Stream, or Muda) for target

Systematic Fishbone analysis exploring problem causes across six categories

Iterative PDCA cycle for systematic experimentation and continuous improvement

Iterative Five Whys root cause analysis drilling from symptoms to fundamentals

Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).

Guide for setup arXiv paper search MCP server using Docker MCP

Guide for setup Codemap CLI for intelligent codebase visualization and navigation

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

Guide for setup Serena MCP server for semantic code retrieval and editing capabilities

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 a task with sub-agent implementation and LLM-as-a-judge verification with automatic retry loop

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