3. Data, Science & AI

Expert guidance for Polars dataframe manipulation, including data processing, ETL pipelines, and performance optimization patterns.

Designs and evaluates AI agent skills using archetype-driven methodology, enabling creation from scratch and knowledge extraction from documentation.

Provides tools for training DQN agents in CTF-AI, including hyperparameter adjustment, debugging, and reward analysis to enhance AI performance.

Analyzes project configuration for Claude Code AI tool and provides actionable optimization suggestions.

Generates interactive data visualizations using D3.js, including charts and graphs for data representation.

A 3D volumetric Lenia engine for simulating and visualizing 3D organisms with GPU-accelerated rendering and preset management.

Automates validation of AI outputs through fact-checking, logical consistency, hallucination detection, bias analysis, and safety assessments.

Processes Excel files for data manipulation, formula generation, and chart creation to support data analysis and reporting.

Generates interactive data visualizations using popular charting libraries for effective data representation and analysis.

Optimizes and tests LLM prompts via performance evaluation, A/B testing, and reproducibility verification for enhanced model outputs.

Provides automated metrics, human feedback, and benchmarking for evaluating LLM performance and AI application quality.

Enables implementation of RAG systems for LLMs using vector databases and semantic search to ground AI responses in external knowledge.

Provides core API for Lenia simulation engine, including stable parameters, multi-species presets, and growth functions for configuring artificial life simulations.

Converts data between formats including JSON, XML, CSV, YAML, and TOML for seamless data migration and structure transformation.

Optimizes embedding models and chunking strategies for semantic search and RAG applications in AI systems.

Architects LLM applications with LangChain, enabling agent-based workflows, memory management, and tool integration for complex AI systems.

Provides reusable patterns for AI agent workflows including ReAct, multi-agent systems, and autonomous agent architectures for complex reasoning tasks.

Provides best practices for designing AI memory architectures, covering user/agent/session patterns, vector vs graph tradeoffs, and retention optimization.

Provides configuration templates and strategies for AI model routing in OpenRouter, optimizing cost, speed, quality, and implementing fallback chains.

Designs database schemas with multi-tenancy, entity relationships, and best practices for data modeling and structure planning.

Manages voice cloning workflows, library organization, audio conversion, and voice settings for AI-generated voice creation and customization.

Configures pgvector extension in Supabase for vector search, embedding storage, and AI-optimized queries to enable RAG and semantic search implementations.

Provides reusable templates and patterns for ML training tasks including text classification, generation, fine-tuning, and PEFT/LoRA with ready-to-use configurations.

Provides web scraping templates and scripts using Playwright, BeautifulSoup, and Scrapy for structured data extraction, documentation collection, and RAG knowledge base building.