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Create and structure Claude Code agent skills. Use when the user wants to create a new skill, write an agent skill, make a claude skill, or asks about SKILL.md files.

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。

Guide creation of focused single-purpose agents following the One Agent One Prompt One Purpose principle. Use when designing new agents, refactoring general agents into specialists, or optimizing agent context for a single task.

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。

Central authority for Claude Agent SDK (TypeScript and Python SDKs). Covers SDK installation, authentication (Anthropic key, Bedrock, Vertex), sessions and resumption, forking sessions, streaming vs single mode, custom tools, permissions (allowedTools, disallowedTools, permissionMode), MCP integration, system prompts (CLAUDE.md, appendSystemPrompt, outputStyle), cost tracking, todo tracking, structured outputs, hosting patterns, plugins, and SDK branding guidelines. Assists with building custom agents, configuring SDK options, and troubleshooting SDK issues. Delegates 100% to docs-management skill for official documentation.

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。

Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.

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。

AgentDB Reinforcement Learning Training operates on 3 fundamental principles:

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。

Use this skill in the scenario of intelligent agent application development.

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Asistente especializado en investigación académica, redacción científica, ACD, metodología cualitativa y análisis de datos con prevención de plagio

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。

Skill for discovering and researching autonomous AI agents, tools, and ecosystems using the AgentFolio directory.

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Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.

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。

Optimize ElevenLabs conversational AI agents for real estate applications. Use when creating new agents, improving conversation quality, selecting voices, engineering system prompts, configuring agent parameters, or analyzing agent performance metrics. Includes voice selection, model tuning, prompt engineering, and A/B testing strategies.

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。

Reference for configuring tool permissions when launching Claude Code agents. Use when setting up --allowedTools flags, restricting file access, or configuring agent permissions.

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。

Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.

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, use, and manage Agent Skills for Claude. Use when working with Skills, creating custom capabilities, or understanding how Skills extend Claude's functionality. Covers Skill architecture, file structure, and best practices.

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。

Economic identity and reputation system for autonomous agents on Base.

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 task distributor specializing in intelligent work allocation, load balancing, and queue management. Masters priority scheduling, capacity tracking, and fair distribution with focus on maximizing throughput while maintaining quality and meeting deadlines.

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。

Creates new agent skills following modern best practices with proper structure and documentation. Use when asked to build a new skill, organize skill resources, design skill descriptions, or validate skill structure for portability across Copilot platforms.

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。

Conduct multi-agent task orchestration and workflow coordination.

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。

AgentDB Persistent Memory Patterns operates on 3 fundamental principles:

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。

Batteries-included agent component for React/Next.js from ui.inference.sh. One component with runtime, tools, streaming, approvals, and widgets built in. Capabilities: drop-in agent, human-in-the-loop, client-side tools, form filling. Use for: building AI chat interfaces, agentic UIs, SaaS copilots, assistants. Triggers: agent component, agent ui, chat agent, shadcn agent, react agent," agentic ui, ai assistant ui, copilot ui, inference ui, human in the loop

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AI agent self-correction mechanisms: error detection, validation loops, recovery strategies, confidence scoring, and iterative refinement

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Create tailored resume from job + source resume. Orchestrates the rewriting of bullet points to emphasize relevant experience without fabrication.

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Expert Scrum Master specializing in agile transformation, team facilitation, and continuous improvement. Masters Scrum framework implementation, impediment removal, and fostering high-performing, self-organizing teams that deliver value consistently.

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Intelligent financial management skill for Claude Code that provides comprehensive PocketSmith API integration with AI-powered analysis, transaction categorization, rule management, tax intelligence, and scenario planning. Use when working with PocketSmith data for (1) Transaction categorization and rule management, (2) Financial analysis and reporting, (3) Australian tax compliance (ATO) and deduction tracking, (4) Scenario analysis and forecasting, (5) PocketSmith setup health checks, (6) Budget optimization and spending insights.

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Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search 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。