context-engineering-kit

Systematically add test coverage for all local code changes using specialized review and development agents. Add tests for uncommitted changes (including untracked files), or if everything is commited, then will cover latest commit.

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

Systematically fix all failing tests after business logic changes or refactoring

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

Implement a task with automated LLM-as-Judge verification for critical steps

Generate ideas in one shot using creative sampling

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

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

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

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

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.

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

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

Execute complex tasks through sequential sub-agent orchestration with intelligent model selection, meta-judge → LLM-as-a-judge verification

Launch multiple sub-agents in parallel to execute tasks across files or targets with intelligent model selection, quality-focused prompting, and meta-judge → LLM-as-a-judge verification

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

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

Comprehensive pull request review using specialized agents

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

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

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

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

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

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