3. Data, Science & AI

Generates synthetic ChatML format (JSONL) datasets for fine-tuning AI models, including validation and framework integration guidance.

Monitors security, liquidity, and transaction status of cross-chain bridges across multiple blockchain networks.

Auto-detects document types from file paths or content, prioritizing path-based analysis before content analysis with confidence scoring.

Builds and evaluates supervised classification models using labeled data for predictive tasks.

Generates plots, charts, and graphs from data with automatic selection of optimal visualization types based on input data.

Detects anomalies and outliers in datasets using machine learning algorithms.

Processes images using object detection, classification, and segmentation for analysis requests like 'analyze image' or 'object detection'.

Provides modular orchestration for Anthropic's agent patterns including prompt chaining, routing, and autonomous agents with language-specific implementations.

Optimizes deep learning models with Adam, SGD, and learning rate scheduling to enhance accuracy and reduce training time.

Enables development, debugging, and optimization of AI workflows and LLM prompts using Microsoft's GenAIScript framework for prompt-as-code and file processing.

Automates feature engineering tasks including scaling, encoding, and importance analysis to enhance machine learning model performance.

Configures ML experiment tracking with MLflow or Weights & Biases, enabling logging of parameters, metrics, and artifacts for machine learning projects.

Tracks real-time valuations and performance metrics for multi-chain cryptocurrency portfolios, providing up-to-date holdings analysis.

Automates splitting datasets into training, validation, and test sets for machine learning model development and evaluation.

Optimizes machine learning model hyperparameters using grid search, random search, or Bayesian optimization to maximize performance.

Tracks crypto derivatives positions (futures, options, perps) and calculates real-time P&L for traders.

Automates table partitioning strategies to optimize performance of large datasets using SQL.

Validates database data against types, ranges, formats, referential integrity, and business rules for consistent data management.

Automates end-to-end machine learning workflows including data preparation, feature engineering, and hyperparameter tuning for model deployment.

Inspects and validates Vertex AI Agent Engine deployments, including security posture and A2A protocol compliance, to generate production readiness scores.

Analyzes and optimizes database query performance, providing automated guidance for improved speed and efficiency in data processing tasks.

Enables local LLM inference with GGUF models via OpenAI-compatible API for offline AI tool development and troubleshooting.

Executes subagents via the Codex framework's 'codex exec' command, enabling AI agent management and workflow automation.

Generates automated data cleaning pipelines for pandas and polars, handling missing values, duplicates, outliers, and data validation.