GitHub
core
Personal AI Infrastructure core. AUTO-LOADS at session start. The authoritative reference for how the PAI system works, how to use it, and all system-level configuration. USE WHEN any session begins, user asks...
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content media llm ai AI Skills
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GitHub
redteam
Adversarial analysis with 32 agents. USE WHEN red team, attack idea, counterarguments, critique, stress test. SkillSearch('redteam') for docs.
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GitHub
on-policy-distillation
Train models using token-level KL distillation with teacher models. Use when you want true on-policy distillation (Thinking Machines approach), token-level KL penalty, or training with Qwen/Llama as teacher. Tr...
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GitHub
chunking-strategy
Implement optimal chunking strategies in RAG systems and document processing pipelines. Use when building retrieval-augmented generation systems, vector databases, or processing large documents that require bre...
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GitHub
prompt-engineering
This skill should be used when creating, optimizing, or implementing advanced prompt patterns including few-shot learning, chain-of-thought reasoning, prompt optimization workflows, template systems, and system...
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GitHub
qdrant-vector-database-integration
Qdrant vector database integration patterns with LangChain4j. Store embeddings, similarity search, and vector management for Java applications. Use when implementing vector-based retrieval for RAG systems, sema...
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GitHub
aws-sdk-java-v2-bedrock
Amazon Bedrock patterns using AWS SDK for Java 2.x. Use when working with foundation models (listing, invoking), text generation, image generation, embeddings, streaming responses, or integrating generative AI...
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GitHub
langchain4j-rag-implementation-patterns
Implement Retrieval-Augmented Generation (RAG) systems with LangChain4j. Build document ingestion pipelines, embedding stores, vector search strategies, and knowledge-enhanced AI applications. Use when creating...
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GitHub
langchain4j-vector-stores-configuration
Configure LangChain4J vector stores for RAG applications. Use when building semantic search, integrating vector databases (PostgreSQL/pgvector, Pinecone, MongoDB, Milvus, Neo4j), implementing embedding storage/...
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GitHub
spring-ai-mcp-server-patterns
Model Context Protocol (MCP) server implementation patterns with Spring AI. Use when building MCP servers to extend AI capabilities with custom tools, resources, and prompt templates using Spring's official AI...
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GitHub
rag-implementation
Build Retrieval-Augmented Generation (RAG) systems for AI applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs...
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GitHub
langchain4j-spring-boot-integration
Integration patterns for LangChain4j with Spring Boot. Auto-configuration, dependency injection, and Spring ecosystem integration. Use when embedding LangChain4j into Spring Boot applications.
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GitHub
langchain4j-testing-strategies
Testing strategies for LangChain4j-powered applications. Mock LLM responses, test retrieval chains, and validate AI workflows. Use when testing AI-powered features reliably.
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GitHub
langchain4j-mcp-server-patterns
Model Context Protocol (MCP) server implementation patterns with LangChain4j. Use when building MCP servers to extend AI capabilities with custom tools, resources, and prompt templates.
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GitHub
langchain4j-tool-function-calling-patterns
Tool and function calling patterns with LangChain4j. Define tools, handle function calls, and integrate with LLM agents. Use when building agentic applications that interact with tools.
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GitHub
langchain4j-ai-services-patterns
Build declarative AI Services with LangChain4j using interface-based patterns, annotations, memory management, tools integration, and advanced application patterns. Use when implementing type-safe AI-powered fe...
☆ 98View skill
GitHub
audio-language-models
Gemini Live API, Grok Voice Agent, GPT-4o-Transcribe, AssemblyAI patterns for real-time voice, speech-to-text, and TTS. Use when implementing voice agents, audio transcription, or conversational AI.
☆ 98View skill
GitHub
embeddings
Text embeddings for semantic search and similarity. Use when converting text to vectors, choosing embedding models, implementing chunking strategies, or building document similarity features.
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GitHub
vision-language-models
GPT-5/4o, Claude 4.5, Gemini 2.5/3, Grok 4 vision patterns for image analysis, document understanding, and visual QA. Use when implementing image captioning, document/chart analysis, or multi-image comparison.
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GitHub
recall
Search and retrieve decisions and patterns from knowledge graph. Use when recalling patterns, retrieving memories, finding past decisions.
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GitHub
advanced-guardrails
LLM guardrails with NeMo, Guardrails AI, and OpenAI. Input/output rails, hallucination prevention, fact-checking, toxicity detection, red-teaming patterns. Use when building LLM guardrails, safety checks, or re...
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GitHub
rag-retrieval
Retrieval-Augmented Generation patterns for grounded LLM responses. Use when building RAG pipelines, constructing context from retrieved documents, adding citations, or implementing hybrid search.
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GitHub
llm-evaluation
LLM output evaluation and quality assessment. Use when implementing LLM-as-judge patterns, quality gates for AI outputs, or automated evaluation pipelines.
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GitHub
reranking-patterns
Reranking patterns for improving search precision. Use when implementing cross-encoder reranking, LLM-based relevance scoring, or improving retrieval quality in RAG pipelines.
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