context-engineering-kit

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.

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 per step

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 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.

Run independent tasks concurrently across multiple files or targets using parallel sub-agents, with per-task model selection and LLM-as-a-judge verification. Use when tasks do not depend on each other and can run side by side.

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

Review an existing GitHub pull request and post inline review comments on its diff. Use when the changes are on an opened PR rather than your local working tree.

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.

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.