NeoLabHQ@Context Engineering Kit

Iterative Five Whys root cause analysis drilling from symptoms to fundamentals

Manage evidence freshness by identifying stale decisions and providing governance actions

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

Reset the FPF reasoning cycle to start fresh

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

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

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

Reconcile the project's FPF state with recent repository changes

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).

Systematically fix all failing tests after business logic changes or refactoring

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.

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 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 found gap or repetative issue, that produced by you or implemenataion agent. Esentially use it each time when you say "You absolutly right, I should have done it differently." -> need create rule for this issue so it not appears again.

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.

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

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

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.

Generate ideas in one shot using creative sampling

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

Analyze a GitHub issue and create a detailed technical specification

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.

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

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

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

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

Guide for setup arXiv paper search MCP server using Docker MCP

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

Systematic Fishbone analysis exploring problem causes across six categories

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

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

Use to load open/unresolved PR review comments then aggregate them as tasks in .specs/comments/*.md for parallel agents to fix.

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

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

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

Iterative PDCA cycle for systematic experimentation and continuous improvement

Guide for setup Codemap CLI for intelligent codebase visualization and navigation

Execute complete FPF cycle from hypothesis generation to decision

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

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

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.

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.

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

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

Verify what PR review comments have been addressed (committed/pushed OR uncommitted local changes) and resolve the threads that are genuinely fixed or no longer relevant.

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