context-engineering-kit

Execute complex tasks through sequential sub-agent orchestration with intelligent model selection, meta-judge → LLM-as-a-judge verification

Use when errors occur deep in execution and you need to trace back to find the original trigger - systematically traces bugs backward through call stack, adding instrumentation when needed, to identify source of invalid data or incorrect behavior

Iterative Five Whys root cause analysis drilling from symptoms to fundamentals

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

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

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

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

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

Execute complete FPF cycle from hypothesis generation to decision

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

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

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

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

Use when Code implementation and refactoring, architecturing or designing systems, process and workflow improvements, error handling and validation. Provide tehniquest to avoid over-engineering and apply iterative improvements.

Iterative PDCA cycle for systematic experimentation and continuous improvement

Use when creating or developing, before writing code or implementation plans - refines rough ideas into fully-formed designs through collaborative questioning, alternative exploration, and incremental validation. Don't use during clear 'mechanical' processes

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

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.

Create well-formatted commits with conventional commit messages and emoji

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

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 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

Systematically fix all failing tests after business logic changes or refactoring

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

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.