Claude Skills by NeoLabHQ

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

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

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

Review your local uncommitted working-tree changes (git diff plus untracked files) and return actionable improvement suggestions. Use before committing, when nothing has been pushed yet.

Review an existing GitHub pull request and post inline review comments on its diff. Use when the changes are on an opened PR rather than your local working tree.

This skill should be used when need prioritize what changed code in repository human must review.

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

Run independent tasks concurrently across multiple files or targets using parallel sub-agents, with per-task model selection and LLM-as-a-judge verification. Use when tasks do not depend on each other and can run side by side.

Execute one complex task as ordered, dependent steps run sequentially, passing context from each step to the next, with per-step LLM-as-a-judge verification. Use when later steps depend on the results of earlier ones.

Evaluate solutions through multi-round debate between independent judges until consensus

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

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

Generate ideas in one shot using creative sampling

Implement a task step by step with automated LLM-as-Judge verification at the end of each phase

Refine a draft task specification into a fully planned, implementation-ready task with acceptance criteria, architecture, per-step sub-task files and verifiable phases

Use before writing any type of tests. Distills 14 industry sources into deterministic decision gates, schemas, and worked test examples.

Systematically fix all failing tests after business logic changes or refactoring

Use after writing tests to assess coverage quality across structural, mutation, requirements, and API/integration dimensions; organized knowledge for choosing and interpreting coverage analyses.

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

Add missing test coverage for your local code changes by generating new test files (covers uncommitted and untracked changes, or the latest commit if everything is committed). Use when you want write tests for new logic or increase test coverage.

Reconcile the project's FPF state with recent repository changes

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

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

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

Analyze a GitHub issue and create a detailed technical specification

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

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

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

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).

Systematic Fishbone analysis exploring problem causes across six categories

Create well-formatted commits with conventional commit messages and emoji

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