3. Data, Science & AI

Guides database schema design, SQL/NoSQL selection, relationship modeling, and query optimization.

Extracts logistics store names from Hefei Excel files in the unresolved directory for data processing.

Reviews quantitative trading system plans to detect data leakage, look-ahead bias, scalability risks, and production issues before deployment.

Generates line and bar charts from data and saves them as image files for data visualization purposes.

Performs mathematical calculations using Python scripts for data science and scientific applications.

Extracts plain text content from a specified URL for data processing and analysis workflows.

Expert assistance for SQL Server database administration including schema design, query optimization, stored procedures, and performance tuning following Microsoft best practices.

Applies best practices for building scalable LLM applications using LangChain and LangGraph patterns.

Scans code repositories to discover, evaluate, and catalog AI agents within the AI ecosystem.

Generates high-quality images using Google's advanced AI image generation model.

Provides standardized prompt engineering and context engineering principles for AI agents, optimized for clarity, structure, and signal-to-noise ratio per Anthropic best practices.

Complements Claude Code with Gemini AI for multi-modal analysis, large-context code review, and real-time web research.

Designs PostgreSQL schemas with best practices, optimal data types, indexing, constraints, and performance patterns for efficient data management.

Guides creation, auditing, and validation of Best Practice Analyzer rules for Power BI semantic models to ensure optimal data modeling practices.

Generates CANN Ascend C operator code from Python reference implementations for AI model deployment.

Adds new bat listings to BatDigest by creating YAML entries, researching specs, generating affiliate links, and optionally adding ratings/images.

Provides configuration guidance for Tabular Editor 3, covering application preferences, model-level settings, and related files such as Preferences.json and Layouts.json.

Enables production AI application development with Vercel AI Gateway and AI SDK v6, supporting multi-model routing, agent architectures, and observability.

Extracts scenario data, units, and names from GCACW game PDFs for roster generator integration.

Manages AI agent prompts via versioning, A/B testing, and rollback strategies for reliable iterative development.

Analyzes coding agent sessions to enhance or develop skills for AI models including Claude Code, Pi, and Codex.

Provides data inspection utilities for debugging and understanding GCACW parser pipeline data structures, including raw tables, parsed units, and web JSON configurations.

Debugs PDF text extraction issues, resolving incorrect data, missing units, and garbled text from parsed documents.

Provides techniques for enhancing RAG system performance through optimized chunking, embeddings, and search methods.