Skill UI

Provides production-ready Groq SDK patterns for TypeScript and Python, covering singleton clients, safe error handling, retries, and validation to guide Groq integrations or SDK refactors.

Guides teams through complete LangChain migrations, covering assessment, SDK refactors, RAG/agent transitions, parallel validation, and feature-flag rollouts for legacy LLM systems.

Framework for building stateful, multi-actor AI applications with LangGraph, covering graph construction, reducers, persistence, human-in-the-loop patterns, and ReAct agents suitable for Python production systems.

Provides expert guidance for using llama-factory to fine-tune LLMs through its WebUI, covering QLoRA/LoRA quantized setups, multimodal models, and automation tips for developers exploring models like Llama, Qwen, or Gemma.

This technique is designed for entity recognition and hypothesis generation when identifying Indian or Bollywood actors based on indirect clues. Instead of running broad searches, users should hypothesize a small set of candidate names (1-3) using the provided clues (e.g., awards, family history, controversies). Subsequently, targeted verification searches must be executed for each constraint against the candidate names to confirm the correct identity.

This technique is designed to identify South Asian (Indian/Bollywood) films when the question provides insufficient information to name the movie directly. Instead, it requires chaining together the biographical details of the actor or director—such as birth year, debut era, family relations, or awards—to first uniquely identify the person, and then use that identity to locate the target film and verify all constraints.