3. Data, Science & AI

Advanced collective intelligence system enabling queen-led multi-agent coordination through consensus mechanisms and persistent memory.

Self-learning, quantum-resistant version control system for AI agents with multi-agent coordination and ReasoningBank intelligence.

Trains and deploys neural networks in distributed E2B sandboxes using Flow Nexus.

Retrieves structured data from Agentica library to enable AI-powered analysis and decision support.

Transforms Swift/Xcode build output into structured TOON format for efficient LLM processing during development workflows.

Provides Firestore database utilities for real-time data synchronization, offline support, document querying, and security rule management, preventing common errors.

Manages Model Context Protocol (MCP) servers through configuration file editing, enabling efficient AI model context setup and modification.

Tracks and analyzes agent, skill, and model performance metrics including success rates, latency, and cost for AI system optimization.

Coordinates multiple AI agents in parallel or sequential workflows, enabling delegation, task decomposition, and dynamic agent switching for complex task execution.

Reference guide for PROXY_MODE configuration with external AI models, including routing prefixes for MiniMax, Kimi, and GLM APIs to enable multi-model integration and debugging.

Validates agent outputs against original objectives at checkpoints to prevent goal drift in complex multi-agent workflows.

Enables structured tracking of multiple external AI models during parallel execution, ensuring comprehensive validation and results presentation.

Quick-reference guide for correctly using external AI models with Task tool, clarifying PROXY_MODE placement in prompts.

Runs multiple AI models in parallel for code review and consensus analysis, with performance tracking and cost-saving API prefixes for models like Grok, Gemini, and GPT-5.

Optimizes LLM model selection by analyzing task complexity to reduce API costs and improve efficiency through tiered routing.

Provides a CLI interface to interact with Model Context Protocol (MCP) servers, enabling programmatic access to tools, resources, and prompts for AI model context management.

Provides audio/video transcription via OpenAI Whisper, supporting SRT/VTT formats, timing sync, and speaker diarization for media and subtitles.

Analyzes GA4 and GSC data to provide benchmarked insights, status indicators, and actionable recommendations for data-driven decision making.

Fetches trending AI models from OpenRouter, delivering IDs, context windows, pricing, and usage stats for coding-related model selection and trend analysis.

Executes parallel validation across multiple AI models (e.g., Grok, Gemini) for code review and consensus analysis with performance tracking.

Reference guide for PROXY_MODE configuration, detailing routing prefixes and integration with external AI models including MiniMax, Kimi, and GLM APIs.

Provides reusable patterns for programmatically extracting analytics data from Google Analytics 4 and Google Search Console via their APIs.

Quick reference for correctly configuring external AI models (e.g., Gemini, Grok) with Task tool, clarifying PROXY_MODE placement in prompts.

Coordinates multiple AI agents in parallel or sequential workflows, enabling delegation, task decomposition, and dynamic agent switching for complex problem-solving.