NeoLab
Official@neolabhq
Offers architectural guidance and iterative development methodologies focused on Clean Architecture, Domain-Driven Design, and test-first implementation strategies.
Agent Skills by NeoLab
Showing 71 vetted skills indexed across 1 GitHub repositories.
NeoLabHQcause-and-effect
Analyzes problem causes across six Fishbone diagram categories to identify root causes.
NeoLabHQwhy
Applies Five Whys root cause analysis to drill from symptoms to systemic causes.
NeoLabHQplan-do-check-act
Guides iterative PDCA cycles for systematic experimentation and continuous process improvement.
NeoLabHQroot-cause-tracing
Traces bugs backward through call stacks to find original triggers.
NeoLabHQanalyse
Selects and applies Gemba Walk, Value Stream Mapping, or Muda analysis to codebases and workflows.
NeoLabHQanalyse-problem
Generates A3 one-page problem analyses with root cause analysis and action plans.
NeoLabHQmemorize
Curates insights from reflections and critiques into CLAUDE.md using Agentic Context Engineering.
NeoLabHQreflect
Evaluates prior AI output against weighted rubrics and triggers iterative refinement until confidence thresholds are met.
NeoLabHQcritique
Reviews completed work using three parallel judge agents with debate and consensus reporting.
NeoLabHQsetup-context7-mcp
Configures the Context7 MCP server and registers technology documentation IDs in CLAUDE.md.
NeoLabHQsetup-codemap-cli
Installs and configures Codemap CLI for codebase visualization and navigation.
NeoLabHQsetup-serena-mcp
Guides installation and configuration of the Serena MCP server for semantic code retrieval.
NeoLabHQbuild-mcp
Guides creation of MCP servers in Python or TypeScript with evaluation workflows.
NeoLabHQsetup-arxiv-mcp
Configures the paper-search MCP server via Docker MCP for academic paper search.
NeoLabHQwrite-concisely
Applies Strunk's Elements of Style rules to improve documentation clarity and conciseness.
NeoLabHQupdate-docs
Updates project documentation for local code changes using multi-agent tech-writer workflows.
NeoLabHQtraiage-review
Prioritizes changed repository files that require human code review attention.
NeoLabHQreview-local-changes
Reviews uncommitted git changes with parallel agents and returns scored, actionable issues.
NeoLabHQreview-pr
Review GitHub pull requests and post inline comments on the diff.
NeoLabHQplan-task
Refines draft task specifications into implementation-ready plans via multi-agent analysis and judge validation.
NeoLabHQimplement-task
Orchestrates multi-phase task implementation with sub-agents and automated code review verification.
NeoLabHQbrainstorm
Refines rough feature ideas into validated designs through structured collaborative questioning.
NeoLabHQcreate-ideas
Generates six diverse ideas for a topic using probability-based creative sampling.
NeoLabHQadd-task
Creates draft task files in .specs/tasks/draft/ with typed naming and preserved user intent.
Frequently Asked Questions About NeoLab
FAQPage SchemaWhat specific development tasks does NeoLab support?▼
NeoLab supports system design using Clean Architecture and Domain-Driven Design, iterative code refactoring via Kaizen principles, and test-first development. It also provides templates for RAG-based information retrieval and isolated task execution for code reviews.
Which engineering personas benefit from these methodologies?▼
These methodologies are designed for software architects, lead engineers, and technical leads who prioritize maintainable codebases. The practices are ideal for teams transitioning to domain-centric design or those seeking to enforce strict test-first development standards.
What are the prerequisites for implementing these development practices?▼
Implementation requires a foundational understanding of Clean Architecture and Domain-Driven Design principles. Teams should be prepared to adopt a red-green-refactor cycle and integrate isolated execution environments for task-specific code reviews.