数据与 AI技能

Tools are how AI agents interact with the world. A well-designed tool is the difference between an agent that works and one that hallucinates, fails silently, or costs 10x more tokens than necessary. This skill covers tool design from schema to error handling. JSON Schema best practices, description writing that actually helps the LLM, validation, and the emerging MCP standard that's becoming the lingua franca for AI tools. Key insight: Tool descriptions are more important than tool implementa

中文简介正在准备中。当前显示原文说明。

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.

中文简介正在准备中。当前显示原文说明。

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.

中文简介正在准备中。当前显示原文说明。

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.

中文简介正在准备中。当前显示原文说明。

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.

中文简介正在准备中。当前显示原文说明。

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.

中文简介正在准备中。当前显示原文说明。

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.

中文简介正在准备中。当前显示原文说明。

Write effective system prompts for TD AI agents. Covers role definition, constraint specification, output formatting, tool usage instructions, and prompt structure patterns.

中文简介正在准备中。当前显示原文说明。

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.

中文简介正在准备中。当前显示原文说明。

Run prompt with OpenAI or Gemini provider via agent CLI

中文简介正在准备中。当前显示原文说明。

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.

中文简介正在准备中。当前显示原文说明。

End-to-end resume parsing (detect format → extract fields). Uses a combination of format detection, text extraction, and LLM parsing to normalize resume data.

中文简介正在准备中。当前显示原文说明。

Analyze the codebase to create a concise, LLM-optimized structured overview in .agent/map.md.

中文简介正在准备中。当前显示原文说明。

Agent parameter passing, memory files, and data handoffs between agents

中文简介正在准备中。当前显示原文说明。

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

中文简介正在准备中。当前显示原文说明。

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.

中文简介正在准备中。当前显示原文说明。

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.

中文简介正在准备中。当前显示原文说明。

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.

中文简介正在准备中。当前显示原文说明。

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.

中文简介正在准备中。当前显示原文说明。

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.

中文简介正在准备中。当前显示原文说明。

Document management, LLM pipeline, anonymization, Q&A generation, versioning

中文简介正在准备中。当前显示原文说明。

Appends relevant hashtags to journal entries based on content analysis. Intended for LLM execution.

中文简介正在准备中。当前显示原文说明。

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.

中文简介正在准备中。当前显示原文说明。

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.

中文简介正在准备中。当前显示原文说明。