发现 tools llm ai 相关 Skills，比较来源仓库和用途，查看适合你工作流的技能说明。
Automate Perplexity Deep Research API calls using sonar-deep-research model. Use for Phase 1 academic research in podcast episodes. Handles API key verification, script execution (30-120s), and result formattin...
Run GPT-Researcher multi-agent deep research framework locally using OpenAI GPT-5.2. Replaces ChatGPT Deep Research with local control. Researches 100+ sources in parallel, provides comprehensive citations. Use...
Manages the Notion LLM Config Table database tracking model configurations and parameters across multiple model providers. Use when working with the LLM config table, adding models, updating model parameters, o...
Conversation phases, topic drift, and convo-exit protocols
Personal assistant for daily routines, task management, and productivity
Complete conversation lifecycle management - start (understanding & alignment), capture (mid-conversation context), and exit (debrief & improvement). Use automatically at conversation boundaries and checkpoints...
Open Commerce MCP and AI operations context
Create new Claude Code skills. Use when the user wants to create a skill, make a skill, add a skill, write a skill, or build a skill for Claude Code.
Guide for creating services in the backend. Use when asked to create a service layer component. Directs to the appropriate service skill based on the type of service needed.
Master AI, machine learning, LLMs, prompt engineering, and blockchain development. Use when building AI applications, working with LLMs, or developing smart contracts.
Master backend development with Node.js, Python, Java, Go, Rust, API design, databases, and microservices. Use when building APIs, designing systems, or learning backend frameworks.
Master machine learning, data engineering, AI engineering, MLOps, and prompt engineering. Build intelligent systems from data pipelines to production AI applications with LLMs, agents, and modern frameworks.
Master machine learning, data engineering, AI engineering, LLMs, prompt engineering, and MLOps. Build intelligent systems with Python.
Process and analyze text using modern NLP techniques - preprocessing, embeddings, and transformers
Deploy ML models to production - APIs, containerization, monitoring, and MLOps
Comprehensive data science, machine learning, and AI guide covering Python, deep learning, NLP, LLMs, prompt engineering, and MLOps. Use when building AI models, data pipelines, or machine learning systems.
Comprehensive guide for skill assessment, career evaluation, learning paths, knowledge testing, and professional development. Use when assessing technical skills, planning learning journeys, or evaluating caree...
Master plugin performance optimization, best practices, and marketplace readiness. Learn to optimize for speed, efficiency, and user satisfaction.
Master writing plugins including agent implementation, skill creation, command development, and hook scripting. Learn best practices for plugin coding.
Build RAG systems - embeddings, vector stores, chunking, and retrieval optimization
Implement agent memory - short-term, long-term, semantic storage, and retrieval
Master AI agent fundamentals - architectures, ReAct patterns, cognitive loops, and autonomous system design
Integrate LLMs into applications - APIs, prompting, fine-tuning, and context management
"Domain vocabulary and entity relationships for {project}. Ensures consistent terminology and model understanding."
默认按已收录数据中的 GitHub 仓库 Stars 排序。Stars 属于来源仓库，同一仓库的多个技能可能共享相同数值；它反映仓库关注度，不代表单个技能的安装量或质量评分。