neuro-symbolic
Neuro-symbolic architecture patterns. Use when building the bridge between LLM (Neural) and Python rules (Symbolic).
发现 research 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。
Neuro-symbolic architecture patterns. Use when building the bridge between LLM (Neural) and Python rules (Symbolic).
Esta skill debe usarse cuando el usuario pide "pensar esto a fondo", "analizar paso a paso", "evaluar opciones", "razonar sobre esto", "pensar detenidamente", "meditar", o ante problemas con descripciones exten...
Esta skill debe usarse cuando el usuario pide "orquestar agentes", "coordinar subagentes", "agentes en paralelo", "flujo multi-agente", "delegar a agentes", "lanzar agentes en paralelo", o necesita ejecutar múl...
Web-augmented agent for creating manufacturer skills and comprehensive tests. Use when adding or improving support for a manufacturer handler.
This skill should be used when the user asks about "Workers AI", "AI models", "text generation", "embeddings", "semantic search", "RAG", "Retrieval Augmented Generation", "AI inference", "LLaMA", "Llama", "bge...
Use when creating, editing, evaluating, testing, or verifying ANY skill or skill-related file (SKILL.md, skill resources, skill scripts, or skill assets). If you're asked to evaluate or test a skill's effective...
Use when analyzing Claude Code conversation logs to find patterns in repeated user instructions that could become skills. Ask for date range first.
Use when user requests research requiring multiple sources, comprehensive analysis, or synthesis across topics - technical research, domain knowledge gathering, market analysis, or learning about complex subjec...
Use when building AI agents with Microsoft Agent Framework (Semantic Kernel + AutoGen unified); when implementing memory or context providers; when threads won't deserialize; when workflow checkpointing fails;...
Create and configure custom subagents in Claude Code. Use when user asks to create a new subagent, needs specialized AI assistance with specific tool restrictions or permissions, implements task-specific workfl...
This skill should be used when users want to create custom slash commands for Claude Code. It guides the creation of project-specific or personal slash commands with proper frontmatter, argument handling, and C...
Use when writing new skills, creating skill directories, confused about skill creation, unsure about naming conventions, need help with activation descriptions, want to avoid common mistakes, skill not triggeri...
Provides the single source of truth for brand guidelines, design tokens, technology choices, and voice/tone. Use this skill whenever generating UI components, styling applications, writing copy, or creating use...
Generates high-quality, predictable, and efficient .agent/skills/ directories based on user requirements. Use when the user asks to create a new skill.
Turns product or tech specs into concrete Notion tasks that Claude code can implement. Breaks down spec pages into detailed implementation plans with clear tasks, acceptance criteria, and progress tracking to g...
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or...
Intelligent request analysis and routing system. Analyzes incoming requests, calculates complexity, and routes to appropriate resources (27 agents, 27 skills, 14 MCPs). Acts as the gateway for the entire Claude...
Assists in writing high-quality content by conducting research, adding citations, improving hooks, iterating on outlines, and providing real-time feedback on each section. Transforms your writing process from s...
Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or to...
Universal guide for creating well-structured Agent Skills for any technology or domain. Use when creating, writing, authoring, or designing a new Skill. Triggers on requests involving SKILL.md files, skill stru...
Fine-tune LLMs 2x faster with 80% less memory using Unsloth. Use when the user wants to fine-tune models like Llama, Mistral, Phi, or Gemma. Handles model loading, LoRA configuration, training, and model export...
Train and use SuperBPE tokenizers for 20-33% token reduction across any project. Covers training, optimization, validation, and integration with any LLM framework. Use when you need efficient tokenization, want...
Analyze, compare, and work with tokenizers using Unsloth tools. Compare different tokenizers, analyze token efficiency, and integrate with Unsloth models. For SuperBPE training, see the 'superbpe' skill.
Export and deploy fine-tuned models to production. Covers GGUF/Ollama, vLLM, HuggingFace Hub, Docker, quantization, and platform selection. Use after fine-tuning when you need to deploy models efficiently.
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。