3. Data, Science & AI

Generates evidence-based clinical decision support reports with biomarker-stratified analyses, statistical evidence, and regulatory compliance for pharmaceutical research.

Programmatic PDF toolkit for extracting text/tables, creating, merging, splitting, and filling forms to enable document data analysis.

Generates testable hypotheses from observations, designs experiments, and proposes mechanisms for scientific inquiry across domains.

Collects and synthesizes diverse perspectives from multiple AI agents to provide comprehensive answers to user queries requiring multi-agent input.

Generates summaries, insights, and Korean translations from YouTube transcripts, along with comprehension quizzes at varying difficulty levels.

Aggregates and compresses time-series data daily with merge logic for dashboards and storage estimation for capacity planning.

Validates incoming data with quality scoring, quarantining suspicious records for manual review without disrupting the data pipeline.

Guides users through structured conversations to extract creative intent and prepare for content generation using conversational AI techniques.

Builds high-performance real-time analytics pipelines using Redis counters and PostgreSQL storage with time-series aggregation to eliminate database bottlenecks.

Efficient grid-based geographic clustering with medoid selection and risk scoring using event density, sentiment, and recency data.

Centralized data transformation logic for consistent shaping across API routes, featuring aggregation, ranking, and sanitization.

Ensures exactly-once processing for file-based data pipelines with atomic file claiming, status tracking, and automatic retry fallback.

AI prompt engine using YAML templates for structured prompt creation, with brand integration, security sanitization, and token-efficient context blocks for LLM workflows.

Manages data recovery and audit trails through soft delete implementation, handling filtering, restoration, and permanent deletion workflows.

Tracks provenance, decision factors, data lineage, confidence scoring, and cost for AI-generated content to ensure transparency and accountability in LLM systems.

Handles event deduplication using canonical selection, reputation scoring, and hash-based grouping for multi-source data aggregation.

Multi-stage pipeline for entity reconciliation using PostgreSQL trigram pre-filtering, overlap checks, and multi-factor scoring to match similar entities.

Statistical scoring with z-scores, percentiles, freshness decay, and cross-category normalization for ranking items with confidence.

Provides a robust client for external AI image generation API integration with retry logic, rate limiting, and content safety checks for multi-turn conversations.

Quantum-resistant version control system for AI agents with self-learning capabilities and multi-agent coordination via ReasoningBank intelligence.

Queries BSL semantic models using aggregation, filtering, and visualization for data analysis from semantic tables.

Creates and modifies semantic data models with dimensions, measures, and joins using BSL and YAML configuration for business intelligence applications.

Guides writing standard SQL queries (SELECT, INSERT, UPDATE, DELETE) using the workers-qb library for database interactions.

Advanced prompt engineering for Claude, optimizing system messages, tool descriptions, and LLM interaction techniques.