Rag Evaluation
Comprehensive guide to evaluating Retrieval-Augmented Generation systems,
Browse curated skills with source links, package snapshots, README assets and install signals in one calm, searchable catalog.
Comprehensive guide to evaluating Retrieval-Augmented Generation systems,
Checklist for detecting and removing bloat in context, prompts, and responses to reduce token cost and improve signal-to-noise ratio
High Performance Inference focuses on optimizing ML model serving infrastructure
Thai language presents unique challenges: no word boundaries, context-dependent
This skill covers the implementation of explainable AI techniques to
Build a complete AI chat application with database persistence, chat list management, and automatic title generation.
Apply cognitive bias detection whenever the user (or Claude itself) is making an evaluation, recommendation, or decision that could be silently distorted by systematic thinking errors. Triggers on phr…
AI Security Engineer focused on preventing prompt injection, data exfiltration, and model abuse in LLM-enabled products.
Prompt engineering is the art of crafting effective prompts to get desired
Evidence-first summarization rules - start with evidence/data before conclusions, reducing tokens and increasing credibility
On-Device Model Training enables training and updating machine learning
This skill covers the implementation of privacy-preserving machine learning
This skill covers the implementation of automated generative media pipelines
Build a new AI agent with Olakai monitoring from scratch — project setup, SDK integration, KPI configuration, and end-to-end validation
Generate kinetic typography animations for expressive agent-to-human communication. Use when you want to communicate with visual flair - animated text for announcements, alerts, greetings, dramatic re…
AI operational modes (brainstorm, implement, debug, review, teach, ship, orchestrate). Use to adapt behavior based on task type.
Retrieval quality is critical for RAG systems. Bad retrieval leads to
Drift Detection and Retraining is practice of continuously monitoring
Agentic AI Frameworks provide foundation for building autonomous AI agents
This skill covers the implementation of neuromorphic computing systems
Build resumable multi-agent workflows with durable execution, tool loops, and automatic stream recovery on client reconnection.
This skill should be used when the user asks about "Effect AI", "@effect/ai", "LLM integration", "AI tool use", "AI execution planning", "building AI agents", "AI providers", "structured AI output", "…
Research-powered creative strategy with visual previews. Use when you need to figure out WHAT to create before creating it. Researches competitors, captures references, generates visual direction prev…
Create metagene plots and browser tracks for RNA modification data. Use when visualizing m6A distribution patterns around genomic features like stop codons.