movement-notation-systems
Designs systems for encoding, scoring, and generating choreographic movement using Laban notation, computational geometry, and procedural animation principles.
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Designs systems for encoding, scoring, and generating choreographic movement using Laban notation, computational geometry, and procedural animation principles.
Cognitive pressure release valve for agent execution. Use when internal reasoning state cannot be carried forward productively - conflicting assumptions, high reasoning pressure, or risk of compoundin…
This skill should be used when working with DSPy.rb, a Ruby framework for building type-safe, composable LLM applications. Use this when implementing predictable AI features, creating LLM signatures a…
Complexity-based task routing with Q-Learning optimization, Agent Booster WASM fast-path, and Mixture-of-Experts model selection.
CrewAI multi-agent orchestration setup for collaborative AI systems
LangChain memory integration including ConversationBufferMemory, ConversationSummaryMemory, and vector-based memory
Human-in-the-loop integration for LangGraph workflows with approval and intervention points
Token-efficient prompt compression techniques for cost optimization
Analyze narrative structures, character arcs, and genre conventions in reality television for academic research and content analysis. Use when studying reality TV editing patterns, mapping contestant …
Responsible AI development and ethical considerations. Use when evaluating
Parallel research agent orchestration dispatching 5-10 concurrent agents for comprehensive multi-source research with synthesis and validation.
Microsoft AutoGen multi-agent configuration for conversational AI systems
Few-shot example generation and optimization for improved LLM performance
LangChain ReAct agent implementation with tool binding for reasoning and action loops
LLM-based zero-shot and few-shot classification for flexible intent detection
spaCy NER model training and entity extraction for conversational AI
Guides users through the process of preparing datasets and fine-tuning local Large Language Models (LLMs) using techniques like LoRA and QLoRA.
Expert prompt optimization for LLMs and AI systems. Use when building
Resolve model profile (quality/balanced/budget) at orchestration start and map agents to specific models. Enables cost/quality tradeoffs by selecting appropriate AI models for each agent role.
Entity and fact extraction for user profiling and personalization
Haystack NLP pipeline configuration for document processing and QA
LlamaIndex agent and query engine setup for RAG-powered agents
PII detection and redaction utilities for privacy-compliant conversational AI
Cross-encoder reranking and MMR diversity filtering for improved retrieval quality