NeoLab

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.

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

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

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.

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

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

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

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

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

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

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

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

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

Iterative PDCA cycle for systematic experimentation and continuous improvement

Analyze a GitHub issue and create a detailed technical specification

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

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

Create pull requests using GitHub CLI with proper templates and formatting

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

Systematic Fishbone analysis exploring problem causes across six categories

Comprehensive pull request review using specialized agents

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

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

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.

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

Guide for setup Codemap CLI for intelligent codebase visualization and navigation

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

Display the current state of the FPF knowledge base

Execute complete FPF cycle from hypothesis generation to decision

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.

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

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).

Manage evidence freshness by identifying stale decisions and providing governance actions

Reset the FPF reasoning cycle to start fresh

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

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 setup arXiv paper search MCP server using Docker MCP

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

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

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

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.

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

Generate ideas in one shot using creative sampling

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

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

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

Comprehensive pull request review using specialized agents

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