A development skill for adding an Agentic RAG layer to a Modular RAG MCP Server and measuring the results. RAG means finding relevant documents before generating an answer; an agentic layer lets the system take multiple reasoning steps.
Autonomous spec-driven development agent. Syncs DEVSPEC.md into chapter-based reference files, identifies the next pending task from the schedule, implements code following spec architecture and patterns, runs tests with up to 3 auto-fix rounds, and persists progress with atomic commits. Use when user says "auto…
Fully autonomous QA testing agent for Modular RAG MCP Server. Reads test cases from QATESTPLAN.md, executes ALL test types automatically without human intervention — CLI commands, Dashboard UI via Streamlit AppTest headless rendering, MCP protocol via subprocess JSON-RPC, provider switches, and data lifecycle checks.…
A resume-writing aid for describing project experience from the Modular RAG MCP Server project. It produces Chinese and English descriptions based on the project's technical points and the user's business context.
Guide for creating effective agent skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends the agent's capabilities with specialized knowledge, workflows, or tool integrations. Use when user says "create skill", "new skill", "build a skill", "update skill", or…
A mock technical-interview agent for the Modular RAG MCP Server project. RAG means retrieving relevant information before generating an answer; MCP is a protocol for connecting AI tools to applications.
Clean and package the project for distribution. Removes pycache, .venv, build artifacts, data caches, logs, IDE files, coverage reports, and sanitizes API keys in config. Produces a minimal, ready-to-share codebase. Use when user says 'package', 'clean project', 'clean up', '打包', '清理项目', '清理缓存', 'prepare for…
A guided study skill for reviewing the Modular RAG MCP Server project chapter by chapter. It asks questions, gives reference answers, and tracks learning progress between sessions.
Interactive project setup wizard. From a clean codebase, guides user through provider selection (OpenAI/Azure/DeepSeek/Ollama/Qwen/Gemini/etc.), API key configuration, dependency installation, config generation, and launches the dashboard. If user selects an unimplemented provider, auto-scaffolds the provider code…