3. Data, Science & AI

Validates spots.json for missing fields, invalid URLs, duplicates, and data consistency to ensure data integrity.

Enhances WSP protocols through AI-driven strategic analysis, gap identification, and recommendation generation for protocol improvements.

Prototype for detecting noise in text data using Google's Gemma AI model.

Detects spam and toxic content in YouTube live chat using AI models, with a prototype for testing pattern fidelity before deployment.

Queries Claude Code session analytics from cclog database to analyze token usage, costs, and session history for users.

Enables natural language queries on SQL databases for data analysis without requiring SQL knowledge.

Generates agent-ready prompts from task specification files for efficient regeneration after edits or template updates for cpo execution.

Enables data transformation with column-level lineage, virtual environments, and automatic DAG inference for efficient incremental data pipelines.

Enables data transformation, modeling, and quality testing for analytics engineering using dbt patterns and pipelines.

Provides optimal document chunking strategies for RAG systems, tailored to document type and retrieval needs in AI pipelines.

Configures Sentry for monitoring AI agent interactions and LLM calls across SDKs including OpenAI and LangChain.

Enables AI-powered web content retrieval using Jina AI's Reader and Search APIs for efficient data extraction.

Drafts and refines text prompts for AI video generation models like Sora and Veo, enabling text-to-video and image-to-video creation.

Converts tabular data between formats including CSV, JSON, Markdown, HTML, and SQL for seamless data interoperability.

Designs data structures including entities, relationships, and schemas for efficient data storage and retrieval.

Designs data models including entities, relationships, and schemas for efficient data storage and retrieval in data systems.

Assists in designing data models and relationships for effective data structure planning and implementation.

Analyzes and optimizes SQL/NoSQL queries for performance, including index suggestions and N+1 problem resolution.

Provides a recursive language model for efficient large context processing (>50KB) in complex analysis, achieving 40% token savings via Query() and FINAL() patterns.

Transforms vague requirements into production-ready AI prompts using evidence-based principles, including issue diagnosis and boundary definition.

Toolkit for extracting, converting, and structuring web content into markdown/plain text using CSS selectors and batch operations.

Visualizes timestamped log data through graphs, enabling analysis of numeric values over time, event counts, time deltas, and statistics across multiple log files.

Guides AI integration into Ash Framework using vectorization, LLM tool exposure, and LangChain for semantic search and structured outputs.

Automates web content retrieval from URLs with bot detection bypass using a multi-tier fallback strategy.