3. Data, Science & AI

Analyzes Claude Code usage patterns to identify opportunities for enhancing skill performance and effectiveness.

Optimizes Fullstory data structuring by teaching when to use page, element, user, or event properties for searchability and consistency.

Specialized guide for implementing Fullstory analytics in e-commerce, covering conversion funnels, cart abandonment, and PCI-compliant checkout tracking with practical examples.

Automates scraping, validation, and database loading of UFC fighter data from UFCStats.com with error recovery and progress tracking.

Enables comprehensive spreadsheet creation, editing, and data analysis with formula support, formatting, and visualization for .xlsx, .csv, and other formats.

Calculates accurate training costs for Tinker LLM fine-tuning by tokenizing datasets with the correct model tokenizer and providing cost estimates.

Manages Jupyter notebook workflows including engineering, token handling, presentation modes, and reproducible environments for data science and AI projects.

Comprehensive guide for Tinker Cookbook supervised fine-tuning, covering abstractions, API usage, data handling, and vision-language models.

Local reference for LangChain documentation covering agents, chains, RAG, vector stores, and building LLM applications.

Edits images using OpenAI's gpt-image-1 API, supporting selective editing with masks, compositing, and facial feature preservation via text prompts.

Generates images using OpenAI's gpt-image-1.5 API with batch processing, custom prompts, and multiple output formats including transparent backgrounds.

Coordinates prompt updates with corresponding skill modifications to maintain consistency in AI model interactions and functionality.

Executes Google's Gemini CLI for AI-driven code analysis, generation, and complex reasoning tasks in software development.

Dispatches AI agents to independently solve parallel tasks without shared state, optimizing execution across independent problem domains.

Provides bond spread calculations including Z-Spread, I-Spread, G-Spread, ASW, and OAS for fixed income analysis in USD/EUR/GBP markets.

Comprehensive methodology for building financial curves (discount, yield, credit, inflation) using bootstrapping, interpolation, and optimization for bond pricing analytics.

Orchestrates multiple AI agents to explore diverse problem-solving approaches through a diverge-and-converge strategy, enhancing solution quality and innovation.

Simplifies complex data solutions by integrating with map-reduce to distill essential components and core functionality.

Enables semantic and exact-match search across local document knowledge bases for Q&A and information retrieval tasks.

Optimizes user prompts for AI models using 57 proven frameworks to enhance response quality and task effectiveness.

Enables production deployment of agentic AI systems, integrating RAG for knowledge-enhanced implementations.

Guides implementation of Anthropic/Claude agents with workflow patterns and RAG integration guidance.

Customizes AI agent behavior including state management, RAG grounding, and lifecycle hooks for persistent memory and knowledge integration.

Guides on workflow patterns and pitfalls for Google Agent Development Kit, including RAG implementation strategies.