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。
Interactive planning system for epic fantasy novels using the Crucible Structure—a 36-beat narrative framework with three interwoven story strands (Quest, Fire, Constellation), five Forge Points, and a Mercy En...
Expert guidance for Dagster data orchestration including assets, resources, schedules, sensors, partitions, testing, and ETL patterns. Use when building or extending Dagster projects, writing assets, configurin...
Use multiple Claude agents to investigate and fix independent problems concurrently
Skills wiki intro - mandatory workflows, search tool, brainstorming triggers
Brief description of what this Skill does and when to use it. Include specific trigger phrases. Use when user asks "trigger phrase 1", "trigger phrase 2", or wants to [outcome]. Max 1024 characters.
Brief description of what this Skill does and when to use it. Include specific trigger phrases users would say. Use when user asks "trigger phrase 1", "trigger phrase 2", or wants to [outcome]. Max 1024 charact...
Search previous Claude Code conversations for facts, patterns, decisions, and context using semantic or text search
Automate Chrome/Chromium via CDP: navigate, click, fill forms, evaluate JS, screenshot, scrape.
Implement the Ralph Wiggum iterative AI loop for Codex/Claude Code: create /ralph-loop and /cancel-ralph commands, wire a stop hook or wrapper loop, persist a fixed prompt, track iterations, and detect a comple...
Lateral-thinking playbook for options and trade-offs; always deliver a five-tier strategy portfolio.
Mine Codex sessions to synthesize your top software design heuristics (no quotes, no evidence).
Create and manage Claude Code skills following Anthropic best practices. Use when creating new skills, modifying skill-rules.json, understanding trigger patterns, working with hooks, debugging skill activation,...
Jobs to Be Done analysis to understand what customers really want. Use for product discovery, competitive analysis, or understanding why customers hire/fire solutions.
ARGUS NANO v2.1 - Compact research skill for context-limited sessions (~3KB). FOCO: Triangulacao (Academico + Pratico + Empirico), validacao de claims, busca multi-fonte com niveis de confianca. DROID: argus-...
Persistent memory management for Claude Code via AutoMem. Use this skill when: - Starting a session (recall project context, decisions, patterns) - Making architectural decisions or library choices - Fixing bug...
专门用于开发 FastGPT 系统工具和工具集的技能，包含 Zod 类型安全验证、共享配置管理和完整测试工作流。适用于创建新的 FastGPT 工具、开发包含子工具的工具集、或实现带有正确配置和测试的 API 集成。
Design LLM applications using the LangChain framework with agents, memory, and tool integration patterns. Use when building LangChain applications, implementing AI agents, or creating complex LLM workflows.
Transform clarified user requests into structured delegation prompts optimized for specialist agents (cto-architect, strategic-cto-mentor, cv-ml-architect). Use after clarification is complete, before routing t...
Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking. Use when testing LLM performance, measuring AI application quality, or establishing...
Evaluates agent skills against Anthropic's best practices. Use when asked to review, evaluate, assess, or audit a skill for quality. Analyzes SKILL.md structure, naming conventions, description quality, content...
Migrate prompts and code from Claude Sonnet 4.0, Sonnet 4.5, or Opus 4.1 to Opus 4.5. Use when the user wants to update their codebase, prompts, or API calls to use Opus 4.5. Handles model string updates and pr...
Implement Retrieval-Augmented Generation (RAG) systems with LangChain4j. Build document ingestion pipelines, embedding stores, vector search strategies, and knowledge-enhanced AI applications. Use when creating...
Apply optimization techniques to extend effective context capacity. Use when context limits constrain agent performance, when optimizing for cost or latency, or when implementing long-running agent systems.
Engineer effective LLM prompts using zero-shot, few-shot, chain-of-thought, and structured output techniques. Use when building LLM applications requiring reliable outputs, implementing RAG systems, creating AI...
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。