3. Data, Science & AI

Script-based verification tool providing confidence scores to assess data or process reliability.

Provides token-efficient tracking for AI orchestration workflows, supporting CLI-based status updates and agent-assisted complex operations.

Provides Python modules for interacting with Dataverse data repository, facilitating data management, access, and integration in research and data science workflows.

Saves AI session context to Pieces LTM for seamless continuity during low-context scenarios or session handoffs.

Provides PostgreSQL best practices including RLS for multi-tenancy, schema design, Alembic migrations, and query optimization.

Autonomous AI agent framework for biomedical research, executing genomics, drug discovery, and clinical analysis via LLM reasoning and integrated databases.

Analyzes Axiom query patterns to identify unused data and builds dashboards for cost optimization, reducing data waste and ingest spend.

Manages database schema changes idempotently via sync scripts, avoiding direct Alembic migrations in production. Ensures safe schema modifications across environments.

Generates deterministic, idempotent, and observable Standard Operating Procedures (SOPs) for autonomous agents to execute without ambiguity.

Builds type-safe conversational AI agents with tool calling, validation, and streaming using Pydantic AI and OpenRouter.

Deploys parallel Claude agents to independently investigate and resolve multiple issues simultaneously.

Analyzes images, audio, and video via Gemini API for vision tasks, transcription, and OCR, while generating images with Imagen 4 and videos with Veo 3.

Accesses YouTube Data API v3 for video search, channel info, playlists, and comments via curl.

Optimizes PostgreSQL database interactions using Entity Framework Core with migrations, indexing, transactions, and connection pooling for efficient data management.

Comprehensive spreadsheet creation, editing, and analysis with formula handling, formatting, and data visualization for .xlsx, .xlsm, .csv, and .tsv files.

Synthesizes insights from diverse text sources like transcripts and feedback through AI-powered pattern recognition and theme extraction.

Analyzes specification content and routes tasks to appropriate AI agents for implementation within LLM-based workflows.

Builds production-ready AI agents with Microsoft Foundry and Agent Framework, covering architecture, model selection, orchestration, and evaluation.

Generates an HTML report tracking LLM visibility across geographies using Bright Data datasets, delivering actionable insights for model performance monitoring.

Enables parallel execution of complex LLM tasks through a hub-and-spoke architecture for seamless integration of large-scale problems.

Provides ML operations tools for fine-tuning (LoRA, QLoRA), model evaluation, cost optimization, and observability in AI workflows.

Calculates confidence scores to determine optimal execution paths for AI agent workflows, auto-triggered via /aeo command.

Creates and validates AI agents for LLM workflows, ensuring proper configuration and deployment readiness.

Configures SQL views in Spice for virtual tables and query abstraction, simplifying data access and management.