Claude Skills by majiayu000

Create your OpenAI Apps SDK skill in one prompt, then learn to improve it throughout the chapter

Create your RAG skill in one prompt, then learn to improve it throughout the chapter

Create a reusable skill for evaluating fine-tuned models, benchmarking performance, and detecting quality regressions

Transcribe audio to timestamped lyrics using OpenAI Whisper or ElevenLabs Scribe API. Outputs LRC, SRT, or JSON with word-level timestamps. Use when users want to transcribe songs, generate LRC files, or extract lyrics with timestamps from audio.

Add image vision to NanoClaw agents. Resizes and processes WhatsApp image attachments, then sends them to Claude as multimodal content blocks.

Route a NanoClaw agent group to a local Ollama model instead of the Anthropic API. Ollama speaks the Anthropic API natively (v1/messages), so no provider code changes are needed — just env var overrides and a model setting. Use when the user wants to run their agent locally, cut API costs, or experiment with open-weight models. See docs/ollama.md for background.

Add Ollama MCP server so the container agent can call local models for cheaper/faster tasks like summarization, translation, or general queries.

Add Ollama MCP server so the container agent can call local models and optionally manage the Ollama model library.

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.

This skill should be used when the user asks to "implement LLM-as-judge", "compare model outputs", "create evaluation rubrics", "mitigate evaluation bias", or mentions direct scoring, pairwise comparison, position bias, evaluation pipelines, or automated quality assessment.

This skill should be used when the user asks to "implement LLM-as-judge", "compare model outputs", "create evaluation rubrics", "mitigate evaluation bias", or mentions direct scoring, pairwise comparison, position bias, evaluation pipelines, or automated quality assessment.

This skill should be used when the user asks to "implement LLM-as-judge", "compare model outputs", "create evaluation rubrics", "mitigate evaluation bias", or mentions direct scoring, pairwise comparison, position bias, evaluation pipelines, or automated quality assessment.

This skill should be used when the user asks to "implement LLM-as-judge", "compare model outputs", "create evaluation rubrics", "mitigate evaluation bias", or mentions direct scoring, pairwise comparison, position bias, evaluation pipelines, or automated quality assessment.

Master LLM-as-a-Judge evaluation techniques including direct scoring, pairwise comparison, rubric generation, and bias mitigation. Use when building evaluation systems, comparing model outputs, or establishing quality standards for AI-generated content.

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.

Real-time cost tracking, budget enforcement, and ROI measurement for AI agent operations. Track token usage, predict costs, enforce budget caps ($50-70/month typical), optimize model selection, cache results, measure cost-to-value. Use when tracking AI costs, preventing budget overruns, optimizing spend, measuring ROI, or ensuring cost-effective AI operations.

Real-time cost tracking, budget enforcement, and ROI measurement for AI agent operations. Track token usage, predict costs, enforce budget caps ($50-70/month typical), optimize model selection, cache results, measure cost-to-value. Use when tracking AI costs, preventing budget overruns, optimizing spend, measuring ROI, or ensuring cost-effective AI operations.

Real-time cost tracking, budget enforcement, and ROI measurement for AI agent operations. Track token usage, predict costs, enforce budget caps ($50-70/month typical), optimize model selection, cache results, measure cost-to-value. Use when tracking AI costs, preventing budget overruns, optimizing spend, measuring ROI, or ensuring cost-effective AI operations.

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.

Layer 4: Learning and Pattern Extraction for Cognitive Surrogate Systems

Layer 4 Learning and Pattern Extraction for Cognitive Surrogate Systems

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.

Expert in Claude Agent SDK development. Use when users ask about SDK API, agent configuration, MCP servers, hooks, permissions, file checkpointing, or when they mention @AGENT_SDK_DOCS.md. Provides accurate API reference, code examples with TypeScript types, and best practices.

Expert in Claude Agent SDK development. Use when users ask about SDK API, agent configuration, MCP servers, hooks, permissions, file checkpointing, or when they mention @AGENT_SDK_DOCS.md. Provides accurate API reference, code examples with TypeScript types, and best practices.

Guidance for selecting appropriate AI model (sonnet vs haiku) based on task complexity, reasoning requirements, and performance needs. Use when implementing agents or justifying model selection.

Patterns and techniques for evaluating and improving AI agent outputs. Use this skill when implementing self-critique and reflection loops, building evaluator-optimizer pipelines for quality-critical generation, creating test-driven code refinement workflows, designing rubric-based or LLM-as-judge evaluation systems, adding iterative improvement to agent outputs (code, reports, analysis), and measuring agent response quality.

Patterns and techniques for evaluating and improving AI agent outputs. Use this skill when implementing self-critique and reflection loops, building evaluator-optimizer pipelines for quality-critical generation, creating test-driven code refinement workflows, designing rubric-based or LLM-as-judge evaluation systems, adding iterative improvement to agent outputs (code, reports, analysis), and measuring agent response quality.

Patterns and techniques for evaluating and improving AI agent outputs. Use this skill when: - Implementing self-critique and reflection loops - Building evaluator-optimizer pipelines for quality-critical generation - Creating test-driven code refinement workflows - Designing rubric-based or LLM-as-judge evaluation systems - Adding iterative improvement to agent outputs (code, reports, analysis) - Measuring and improving agent response quality

Patterns and techniques for evaluating and improving AI agent outputs. Use this skill when: - Implementing self-critique and reflection loops - Building evaluator-optimizer pipelines for quality-critical generation - Creating test-driven code refinement workflows - Designing rubric-based or LLM-as-judge evaluation systems - Adding iterative improvement to agent outputs (code, reports, analysis) - Measuring and improving agent response quality

Patterns and techniques for evaluating and improving AI agent outputs. Use this skill when: - Implementing self-critique and reflection loops - Building evaluator-optimizer pipelines for quality-critical generation - Creating test-driven code refinement workflows - Designing rubric-based or LLM-as-judge evaluation systems - Adding iterative improvement to agent outputs (code, reports, analysis) - Measuring and improving agent response quality

Patterns and techniques for evaluating and improving AI agent outputs. Use this skill when implementing self-critique and reflection loops, building evaluator-optimizer pipelines for quality-critical generation, creating test-driven code refinement workflows, designing rubric-based or LLM-as-judge evaluation systems, adding iterative improvement to agent outputs (code, reports, analysis), or measuring and improving agent response quality.

Patterns and techniques for evaluating and improving AI agent outputs. Use this skill when: - Implementing self-critique and reflection loops - Building evaluator-optimizer pipelines for quality-critical generation - Creating test-driven code refinement workflows - Designing rubric-based or LLM-as-judge evaluation systems - Adding iterative improvement to agent outputs (code, reports, analysis) - Measuring and improving agent response quality

Advanced RAG with Self-RAG, Corrective-RAG, and knowledge graphs. Use when building agentic RAG pipelines, adaptive retrieval, or query rewriting.

Data framework for building LLM applications with RAG. Specializes in document ingestion (300+ connectors), indexing, and querying. Features vector indices, query engines, agents, and multi-modal support. Use for document Q&A, chatbots, knowledge retrieval, or building RAG pipelines. Best for data-centric LLM applications.

<skill> ────────────────────────────────────────────── <name>ai-ad-code-searcher</name> <version>1.0</version> <domain>AI_AD_SYSTEM / 代码工厂 / 代码搜索</domain> <profile>Code-Search / RAG / Multi-Source</profile> ────────────────────────────────────────────── <!-- ====================================================== 0. 代码来源说明 (Code Sources) ====================================================== --> <code_sources> 本 Skill 的设计和实现借鉴了以下开源项目： 1. **code-graph-rag** (MIT License) - GitHub: https://githu...

<skill> ══════════════════════════════════════════════════════════════════════ AI 提示词结构化器 v4.0 - Prompt Structurer 核心理念：约束优于指令 (Constraints Over Instructions) MCP 工具：sequential-thinking + context7 ══════════════════════════════════════════════════════════════════════ <name>ai-ad-prompt-structurer</name> <version>4.0</version> <domain>AI_AD_SYSTEM / 代码工厂 / 提示词工程</domain> <!-- ====================================================== 1. v4.0 核心创新 ===================================================...

AI-powered development tools configuration and usage

AI-powered development tools configuration and usage

Access 50+ LLM models through a unified OpenAI-compatible API via AceDataCloud. Use when you need chat completions from GPT, Claude, Gemini, DeepSeek, Grok, or other models through a single endpoint. Supports streaming, function calling, and vision.

Multi-turn conversational AI for intent extraction, clarification, and generation readiness detection. Guides users through articulating creative intent with structured parameter extraction.

Build LLM applications, RAG systems, and prompt pipelines. Implements vector search, agent orchestration, and AI API integrations. Use when building LLM features, chatbots, AI-powered applications, or need guidance on AI/ML engineering patterns.

Production-grade AI engineering for LLM applications, RAG systems, and agent architectures. Use when building AI features, designing model integrations, or implementing AI safety and monitoring.

Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations. Use PROACTIVELY for LLM features, chatbots, AI agents, or AI-powered applications.

Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations. Use PROACTIVELY for LLM features, chatbots, AI agents, or AI-powered applications.

Expert-level AI implementation, deployment, LLM integration, and production AI systems

MASTER AI: LLM Apps, Advanced RAG, Agents (ReAct/Plan), Prompting (CoT/Few-shot), LangGraph, VectorDBs, RAGAS Eval. Use for ANY AI/LLM task.

Expert in building comprehensive AI systems, integrating LLMs, RAG architectures, and autonomous agents into production applications. Use when building AI-powered features, implementing LLM integrations, designing RAG pipelines, or deploying AI systems.

Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations.

Caching strategies for LLM prompts including Anthropic prompt caching, response caching, and CAG (Cache Augmented Generation) Use when: prompt caching, cache prompt, response cache, cag, cache augmented.

Ultra-compressed communication mode. Cuts token usage ~75% by speaking like caveman while keeping full technical accuracy. Supports intensity levels: lite, full (default), ultra, wenyan-lite, wenyan-full, wenyan-ultra. Use when user says "caveman mode", "talk like caveman", "use caveman", "less tokens", "be brief", or invokes /caveman. Also auto-triggers when token efficiency is requested.