3. Data, Science & AI

Provides statistical modeling and analysis for business analytics, including A/B testing, causal inference, customer lifetime value, churn prediction, and time series forecasting.

Builds and optimizes machine learning systems with best practices including experiment tracking, cross-validation, and model explainability for production deployment.

Optimizes database performance for PostgreSQL, MySQL, and MongoDB through query tuning, indexing, schema design, and scaling strategies including EXPLAIN analysis and N+1 fixes.

Expert in building end-to-end MLOps workflows for automated training, deployment, and monitoring of machine learning models using MLflow and Kubeflow.

Manages individual AI agents through CRUD operations including creation, update, deletion, listing, and description.

Manages message flow for AI agents, including sending messages, viewing conversation history, and handling message state in conversational AI systems.

Transforms vague user prompts into structured inputs for the researchCodebase agent, enhancing codebase research accuracy and efficiency.

Expert in complex SQL query optimization, database design, and performance tuning across PostgreSQL, MySQL, SQL Server, and Oracle with advanced indexing and data warehousing techniques.

Orchestrates multi-agent AI workflows using Evolve SDK, enabling parallel processing, quality competition, and synthesis across models like Claude, Codex, and Gemini.

Inspects cross-agent witness ledger for recent seals, verdicts, and session artifacts using Codex CLI.

Orchestrates parallel and sequential AI agents to minimize system entropy, treating each agent as a cooling subsystem to reduce disorder.

Lists and inspects Dagster project elements including assets, jobs, schedules, and environment variables for data pipeline management.

Guides users in launching Dagster data assets with step-by-step support for partitions, configuration, environment setup, and troubleshooting.

Generates boilerplate code for Dagster data assets, schedules, and integrations (dbt, Fivetran).

Guides developers in implementing Dataverse data operations including CRUD, SQL queries, and metadata management via Python SDK.

Validates consistency between brainbase's main _codex and project references, checking broken links, edit errors, and mandatory fields.

Enterprise-grade prompt engineering framework based on 1000+ hours of optimization, boosting AI response accuracy by 340% and reducing design time by 67%.

Configures Claude Code to integrate with alternative AI model providers via custom API endpoints and authentication, enabling users to switch from Anthropic models to services like Z.AI or OpenRouter.

Enables 60% compression while preserving semantic integrity and identity through semantic shorthand for token-efficient transmission.

Provides semantic shorthand compression for OCTAVE documents to optimize content for LLM processing, avoiding unnecessary prose.

Sets up KPI frameworks, designs data dashboards, and performs cohort analysis for growth metric optimization and data-driven decisions.

Automates extraction and structuring of unstructured data from multiple sources into AI-interpretable formats.

Merges multiple CSV and Excel files with intelligent column matching, deduplication, and conflict resolution to consolidate diverse data sources.

Analyzes sales pipeline health, identifies stalled deals, predicts close probability, and suggests actions to improve forecast accuracy and prevent revenue leakage.