tdd-workflow
A rigorous TDD workflow agent that enforces test-first development, ensuring 80%+ code coverage across unit, integration, and E2E tests for features, bug fixes, and refactoring.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
270 skills found
A rigorous TDD workflow agent that enforces test-first development, ensuring 80%+ code coverage across unit, integration, and E2E tests for features, bug fixes, and refactoring.
Development guide for creating and publishing TPMJS tools using the blocks CLI, AI SDK v6, and npm registry patterns.
Create, register, and manage custom agent tools and MCP servers to extend AI agent capabilities with external APIs and custom logic.
Streamline technical documentation by generating, updating, and refining README files. Tailors content for specific audiences including OSS contributors, internal teams, and personal projects.
A template skill for creating project-specific AI agent guidelines, defining architecture, file structures, and code patterns for deterministic development.
Autonomous pattern detection and skill recommendation engine that monitors project memory, logs, and task lists to evolve your AI agent's capabilities automatically.
Capture and formalize software development ideas into structured design documents within the Hashbrown repository, including research and conceptual sketches.
Create high-performance AI skills by reverse-engineering successful GitHub projects and proven open-source methodologies.
Adapt existing skills to your unique workflow, or create new ones for repetitive, time-consuming tasks.
The final execution agent for the vibe-coding workflow. Builds your MVP incrementally by following the AGENTS.md master plan, managing session continuity, and verifying each feature via testing.
Orchestrate multi-agent AI swarms using the ClawTeam CLI to automate parallel task execution, dependency management, and team collaboration with git worktree isolation and tmux support.
Architect production-grade LLM applications using LangChain 1.x and LangGraph. Implement stateful AI agents, multi-step workflows, and custom memory systems for complex conversational and automation tasks.