数据分析、机器学习、LLM 应用、提示词设计（9022 个）
Use when designing, optimizing, testing, or deploying robust prompt systems for AI agents. This skill provides frameworks for structured prompt engineering, meta-prompting, and automated optimization workflows.
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Use when needing 100% type-safe, schema-valid data extraction from LLMs. Keywords: structured output, JSON schema, Pydantic, Zod, type-safe, constrained decoding, validation, tool use.
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Use when the user asks how to build with OpenAI products or APIs and needs up-to-date official documentation with citations, help choosing the latest model for a use case, or explicit GPT-5.4 upgrade and prompt-upgrade guidance; prioritize OpenAI docs MCP tools, use bundled references only as helper context, and restrict any fallback browsing to official OpenAI domains.
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Use when validating, benchmarking, or monitoring LLM application performance. Keywords: RAG evaluation, LLM-as-a-judge, CI/CD gating, trajectory scoring, test suites, prompt quality.
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Master context engineering for AI agent systems. Use when designing agent architectures, debugging context failures, optimizing token usage, implementing memory systems, building multi-agent coordination, evaluating agent performance, or developing LLM-powered pipelines. Covers context fundamentals, degradation patterns, optimization techniques (compaction, masking, caching), compression strategies, memory architectures, multi-agent patterns, LLM-as-Judge evaluation, tool design, and project development.
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Use when building self-correcting retrieval systems for AI agents. Keywords: RAG, retrieval, Corrective RAG, Self-RAG, query decomposition, reranking, hallucination, grounding.
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Use when optimizing token usage, KV cache efficiency, or context window management for LLM agents. Keywords: context optimization, KV cache, prompt caching, token budget, semantic pruning, lost-in-the-middle.
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Use when needing global or relational understanding of codebases or knowledge corpora. Keywords: GraphRAG, knowledge graph, entity extraction, community summarization, graph traversal, Microsoft GraphRAG.
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Process and generate multimedia content using Google Gemini API. Capabilities include analyze audio files (transcription with timestamps, summarization, speech understanding, music/sound analysis up to 9.5 hours), understand images (captioning, object detection, OCR, visual Q&A, segmentation), process videos (scene detection, Q&A, temporal analysis, YouTube URLs, up to 6 hours), extract from documents (PDF tables, forms, charts, diagrams, multi-page), generate images (text-to-image, editing, composition, refinement). Use when working with audio/video files, analyzing images or screenshots, processing PDF documents, extracting structured data from media, creating images from text prompts, or implementing multimodal AI features. Supports multiple models (Gemini 2.5/2.0) with context windows up to 2M tokens.
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Reference documentation for embedding Prismatic's integration marketplace and workflow builder in a web application. Covers JWT authentication, the embedded SDK, marketplace and workflow embedding, theming, i18n, additional screens, and custom marketplace UI. Use when the user asks about embedding Prismatic, JWT tokens for embedded apps, marketplace iframes, workflow builder integration, custom marketplace UI, or frontend SDK setup.
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Design effective reasoning processes for AI agents and optimize decision-making strategies. Use when designing agent prompts, improving decision quality, implementing specialized reasoning modes, creating reasoning traces, or debugging agent mistakes. Covers prompting, chain-of-thought, reasoning depth, and decision evaluation.
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This skill should be used when the user asks to "generate tasks", "create implementation plan", "break down feature", "write agent prompts", "decompose into tasks", "create work items", or when creating agent-ready task descriptions from PRD and SDD documents.
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Design agent system prompts with explicit behavior rules and cognitive control. This skill should be used when users need to define agent instructions, create intent-to-tool mappings, establish confirmation policies, or prevent hallucinated actions in AI agents.
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Expert quantitative analyst specializing in financial modeling, algorithmic trading, and risk analytics. Masters statistical methods, derivatives pricing, and high-frequency trading with focus on mathematical rigor, performance optimization, and profitable strategy development.
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Expert prompt engineer specializing in designing, optimizing, and managing prompts for large language models. Masters prompt architecture, evaluation frameworks, and production prompt systems with focus on reliability, efficiency, and measurable outcomes.
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Create well-structured prompts for AI agents using proven architecture patterns. Use when users ask to write agent prompts, system prompts, or agent instructions, or want to improve existing prompts that aren't working.
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This skill should be used when the model's ROLE_TYPE is orchestrator and needs to delegate tasks to specialist sub-agents. Provides scientific delegation framework ensuring world-building context (WHERE, WHAT, WHY) while preserving agent autonomy in implementation decisions (HOW). Use when planning task delegation, structuring sub-agent prompts, or coordinating multi-agent workflows.
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This skill should be used when the user asks to "create an agent", "add an agent", "write a subagent", "agent frontmatter", "when to use description", "agent examples", "agent tools", "agent colors", "autonomous agent", or needs guidance on agent structure, system prompts, triggering conditions, or agent development best practices for Gemini CLI plugins.
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Expert IoT engineer specializing in connected device architectures, edge computing, and IoT platform development. Masters IoT protocols, device management, and data pipelines with focus on building scalable, secure, and reliable IoT solutions.
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Agentic workflow patterns for autonomous LLM reasoning. Use when building ReAct agents, implementing reasoning loops, or creating LLMs that plan and execute multi-step tasks.
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Generates Claude Code plugin agent .md files: writes YAML frontmatter (name, model, color, tools, hooks, disallowedTools), crafts system prompts, and creates triggering example blocks with context and commentary. Validates agent structure against naming, length, and format constraints. Use when the user asks to create an agent, add an agent, write a subagent, configure agent frontmatter, design agent system prompts, set agent tools or colors, build an autonomous agent, or add agent hooks.
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This skill should be used when the user asks to 'create an agent', 'add an agent', 'write a subagent', 'agent frontmatter', 'when to use description', 'agent examples', 'agent tools', 'agent colors', 'autonomous agent', or needs guidance on agent structure, system prompts, triggering conditions, or agent development best practices for Claude Code plugins.
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Check and process messages from autonomous AILANG agents. Use when starting a session, after agent handoffs, or when checking for completion notifications.
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Generate AGENT.md files with proper YAML frontmatter, role definitions, expertise areas, and prompt templates following Babysitter SDK conventions.
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