NeoLabHQ/context-engineering-kit agent skills, page 2

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

NeoLabHQ/context-engineering-kit

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

NeoLabHQ/context-engineering-kit

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

NeoLabHQ/context-engineering-kit

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

NeoLabHQ/context-engineering-kit

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

NeoLabHQ/context-engineering-kit

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

NeoLabHQ/context-engineering-kit

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

NeoLabHQ/context-engineering-kit

Update and maintain project documentation for local code changes using multi-agent workflow with tech-writer agents.

NeoLabHQ/context-engineering-kit

Analyze a GitHub issue and create a detailed technical specification

NeoLabHQ/context-engineering-kit