Découvrez les skills tools llm ai, comparez leurs dépôts et consultez leurs instructions.
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."
Le classement utilise les étoiles du dépôt GitHub indexé. Plusieurs skills peuvent partager ce nombre : ce n’est ni un nombre d’installations ni une note de qualité.