3. Data, Science & AI

Routes user input to specialized AI agents using intent and domain analysis for context-aware task delegation in multi-agent systems.

Backtests trading strategies against a baseline using historical data to evaluate performance metrics.

Converts PDF, Word, images, audio, and other formats to Markdown for text analysis and LLM processing.

Provides access to Google Gemini API for AI/ML applications, supporting text generation, image understanding, video processing, and multimodal capabilities.

Provides rigorous reasoning for complex problems using Atom of Thoughts (AoT) and Chain of Verification (CoVe) frameworks to ensure accuracy and systematic analysis.

Provides documentation and integration tools for OpenAI's GPT models, chat completions, embeddings, and AI services in applications.

Provides Anthropic's 10-component prompt framework, Claude 4.x best practices, and templates for effective LLM prompt engineering.

Analyzes Claude Code conversation logs to identify recurring user instruction patterns, enabling new AI skill development.

Manages PostgreSQL database schema migrations and stored procedures, including functions, triggers, and sequences.

Enables structured iterative AI development cycles through the Ralph Wiggum technique using Kiro CLI for task refinement and model iteration.

Forces Gemini to output valid JSON/XML for reliable data extraction, API integrations, and machine-readable responses.

Enables server-side AI agent capabilities with data access, prompt templates, and model sampling for two-way interactions.

Enables implementation of the Agent2Agent (A2A) protocol and Agent Cards to create collaborative ecosystems among AI agents.

Provides scalable agent architecture strategies to solve N x M integration via dynamic tool discovery and agent-tool decoupling.

Manages AI agent operationalization including tool integration, prompt optimization, and task decomposition for efficient execution.

Provides structured prompt engineering techniques for Gemini AI, including Zero-shot, Few-shot, Role, and System prompting to enhance query accuracy and style.

Framework for defining and measuring AI agent quality using the Outside-In approach and Four Pillars of Quality, shifting from traditional software verification.

Manages AI agent sessions by handling conversation history and applying summarization/truncation to prevent context overflow in Gemini-based applications.

Manages agent memory lifecycle (extraction, consolidation, retrieval) for long-term learning and personalization beyond single sessions.

Provides strategies for building Agentic RAG systems with autonomous agents enabling adaptive source selection, query expansion, and multi-step reasoning.

Enables Gemini AI to implement the ReAct (Reason and Act) paradigm for multi-step research, tool integration, and API interaction.

Provides a decision matrix to select between Extensions, Functions, and Data Stores for AI agents, considering security, execution location, and data type.

Enables complex math, multi-step reasoning, and deep analysis using Gemini's enhanced AI reasoning capabilities.

Provides methodologies to assess AI agent capabilities, tool-use trajectories, and response quality for automated testing and human validation.