データ分析、機械学習、LLMアプリ、プロンプト設計（8,902 件）
概要と使いどころ
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.
日本語の概要は準備中です。原文の説明を表示しています。
End-to-end resume parsing (detect format → extract fields). Uses a combination of format detection, text extraction, and LLM parsing to normalize resume data.
日本語の概要は準備中です。原文の説明を表示しています。
Track and optimize agent specialization during methodology development. Use when agent specialization emerges (generic agents show >5x performance gap), multi-experiment comparison needed, or methodology transferability analysis required. Captures agent set evolution (Aₙ tracking), meta-agent evolution (Mₙ tracking), specialization decisions (when/why to create specialized agents), and reusability assessment (universal vs domain-specific vs task-specific). Enables systematic cross-experiment learning and optimized M₀ evolution. 2-3 hours overhead per experiment.
日本語の概要は準備中です。原文の説明を表示しています。
Run prompt with OpenAI or Gemini provider via agent CLI
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
Ready-to-use prompt templates for specialized agents. Use when building n8n workflows, AI integrations, or sales materials. Contains structured prompts for automation-architect, llm-engineer, and sales-automator agents.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
Write effective system prompts for TD AI agents. Covers role definition, constraint specification, output formatting, tool usage instructions, and prompt structure patterns.
日本語の概要は準備中です。原文の説明を表示しています。
Agent parameter passing, memory files, and data handoffs between agents
日本語の概要は準備中です。原文の説明を表示しています。
Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them. Key insight: Memory isn't just storage - it's retrieval. A million stored facts mean nothing if you can't find the right one. Chunking, embedding, and retrieval strategies determine whether your agent remembers or forgets. The field is fragm
日本語の概要は準備中です。原文の説明を表示しています。
Expert LLM architect specializing in large language model architecture, deployment, and optimization. Masters LLM system design, fine-tuning strategies, and production serving with focus on building scalable, efficient, and safe LLM applications.
日本語の概要は準備中です。原文の説明を表示しています。
Create specialized agent experts with pre-loaded domain knowledge using the Act-Learn-Reuse pattern. Use when building domain-specific agents that maintain mental models via expertise files and self-improve prompts.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
Expert context manager specializing in information storage, retrieval, and synchronization across multi-agent systems. Masters state management, version control, and data lifecycle with focus on ensuring consistency, accessibility, and performance at scale.
日本語の概要は準備中です。原文の説明を表示しています。
Create and manage Databricks Agent Bricks: Knowledge Assistants (KA) for document Q&A, Genie Spaces for SQL exploration, and Multi-Agent Supervisors (MAS) for multi-agent orchestration. Use when building conversational AI applications on Databricks.
日本語の概要は準備中です。原文の説明を表示しています。
Add a professional footer to README files with engagement prompts, community channels, and call-to-action for subscriptions. Use when you need to enhance your README with a footer that encourages interaction and builds community.
日本語の概要は準備中です。原文の説明を表示しています。
Create custom Claude Code sub-agents with specialized expertise and tool access. Use when you need to build reusable agents for specific tasks like code review, debugging, data analysis, or domain-specific workflows.
日本語の概要は準備中です。原文の説明を表示しています。
Run and manage ad-hoc AI prompts in sgai workspaces without starting a full agentic session. Use when you need to run a one-off AI prompt against a workspace, check the status of a running ad-hoc prompt, or stop a running ad-hoc prompt.
日本語の概要は準備中です。原文の説明を表示しています。
Add Qdrant embedding support to v3 WordPress components for RAG chatbot. Implements component-level content chunking for searchable, structured embeddings. Use when adding embedding to new or existing v3 components.
日本語の概要は準備中です。原文の説明を表示しています。
Appends relevant hashtags to journal entries based on content analysis. Intended for LLM execution.
日本語の概要は準備中です。原文の説明を表示しています。
Especialista en configuración de agentes de IA: templates, tools, models, prompts y seed data.
日本語の概要は準備中です。原文の説明を表示しています。
Build RAG (Retrieval-Augmented Generation) agents with Google ADK and Vertex AI RAG Engine. Use when implementing document Q&A, knowledge base search, or citation-backed responses. Covers VertexAiRagRetrieval tool, corpus setup, and citation formatting.
日本語の概要は準備中です。原文の説明を表示しています。
Document management, LLM pipeline, anonymization, Q&A generation, versioning
日本語の概要は準備中です。原文の説明を表示しています。
Read your YouTube audience from the comments: pull commenters and top comments on any video, surface recurring questions and themes (content ideas), repeat commenters (superfans), sentiment, and which comments to reply to or pin. Also channel stats and niche trending, via the free official YouTube Data API. Triggers on "who commented on my video", "analyze my comments", "audience insights", "trending on YouTube". Not for writing a community post (use yt-community-post-writer).
日本語の概要は準備中です。原文の説明を表示しています。