3. Data, Science & AI

Automates Google Trends data extraction and analysis to detect all-time high search trends and classify signal types (seasonal/event-driven/structural).

Compares US CPI (fixed-weight) and PCE (dynamic-weight) inflation metrics to identify PCE acceleration signals when CPI appears stable, supporting Fed policy analysis.

Analyzes Shanghai silver inventory data to quantify depletion rates and generate tradable supply contraction signals.

Quantifies long-term correlation between platinum prices and Brazil equities (EWZ) using statistical analysis, generating dual-axis charts and lead-lag metrics.

Orchestrates multi-agent teams for complex development tasks, managing leader sessions, progress monitoring, and session resumption from plan files.

Automates AI model selection across task types, routing planning to Opus, coding to Sonnet, and simple tasks to Haiku for optimal cost and quality.

Generates images using ModelScope's Z-Image AI models for artwork and visual content. Supports async processing and LoRA configurations.

Orchestrates multiple AI agents to work in parallel for efficient task coordination and complex problem-solving.

Decision framework for selecting between code-reasoning and shannon-thinking MCP tools in AI reasoning workflows.

Enables AI agents to store and retrieve persistent knowledge using text embeddings for long-term context retention across sessions.

Assists in designing, querying, optimizing, and administering MongoDB and PostgreSQL databases, including schema design, performance tuning, and backup strategies.

Optimizes LLM context usage through token budget management, compression, and observation masking for efficient processing.

Guides creation of Model Context Protocol (MCP) servers for integrating external APIs with LLMs using MCP SDK in Go.

Provides a decision tree framework to select optimal AI agents and tools based on task patterns, improving agent-based system efficiency.

Analyzes content analytics data to identify trends, calculate ROI, and provide data-driven content optimization recommendations for improved performance.

Guides implementation of event tracking systems, analytics SDKs, and dashboards for user behavior analysis and retention optimization.

Builds AI agents with OpenAI Agents SDK, supporting MCP server integration and multiple LLMs (OpenAI, Gemini).

Provides data persistence solutions including session management, migration, and backup systems for reliable storage of user progress and settings.

Provides consistent SQL naming, formatting, and style guidelines for database code to enhance readability and maintainability.

Detects deepfakes using multimodal forensic analysis and LLM-augmented interpretation for media authenticity verification.

Manages time series forecasting models using Facebook's Prophet library for data analysis and prediction.

Integrates Replicate API for AI image generation using models such as SDXL and custom LoRA.

Fetches aggregated metrics from MLflow tracking servers, including token usage, latency, and LLM costs for model performance analysis.

Provides best practices for writing PL/pgSQL stored procedures and triggers to implement business logic within PostgreSQL databases.