3. Data, Science & AI

Analyzes Turkish electricity market prices including PTF, SMF, WAP, and imbalance prices for market clearing, day-ahead, and system marginal price queries.

Provides real-time and forecasted electricity generation data for Turkey, including resource types, plant-level production, and UEVM metrics. Triggers on Turkish energy terms.

Tool for creating and updating AI skills that extend an AI's capabilities with specialized knowledge, workflows, and tool integrations.

Guide for creating Model Context Protocol (MCP) servers to integrate external APIs and build tool interfaces for large language models (LLMs).

Guides users through configuring nodes, connecting agents, and designing multi-agent workflows in the LangConfig platform.

Guides design of LangGraph state machines and multi-agent workflows for complex AI control flow and agent coordination.

Guides creation of LangChain agents with proper tool binding, memory management, and model configuration for AI applications.

Evaluates LLM outputs using multi-dimensional rubrics, handles non-determinism, and implements LLM-as-judge patterns for model validation and comparison.

Provides documented prompt engineering principles and tools to significantly enhance LLM response quality across technical, creative, and research tasks.

Builds production-ready LLM applications and data pipelines using structured methodology, task-model analysis, and cost estimation.

Optimizes AI agent tool design through consolidation, improving success rates for MCP and Claude Code integrations.

Comprehensive Pydantic v2 data validation with schema alignment and quality monitoring for PlanetScale PostgreSQL databases.

Designs and implements long-term memory systems for AI agents using vector stores, knowledge graphs, and hybrid approaches, including RAG and knowledge base frameworks.

Configures Codex CLI settings including profiles, sandbox mode, and approval policies via config.toml.

Enables creation and configuration of AI agents, including writing agent frontmatter and setting up subagents for task execution.

Coordinates AI agents, delegates specialized tasks, and manages multi-agent system workflows for complex AI applications.

Analyzes conversation data to identify patterns, frustration signals, and success indicators, providing insights for improving user interactions.

Coordinates multiple AI agents for parallel tasks, handoffs, and concurrent operations in multi-agent systems.

Converts natural language queries to SQL for databases (SQLite, PostgreSQL, etc.), setting up project structure and schema extraction.

Provides model evaluation metrics, testing protocols, and performance analysis for Somali dialect classification using accuracy, F1-score, and confusion matrix in multi-class settings.

Provides best practices for MLOps including model versioning, experiment tracking, deployment, monitoring, and retraining in production ML systems.

Provides ETL workflow patterns, data pipeline architecture, and ingestion strategies for data processing, particularly for AI/ML applications.

Provides low-resource NLP techniques for Somali language processing, including data augmentation and cross-lingual transfer.

Manages coordination between main Claude Code agent and subagents via explicit user commands, handling orchestration, registry, and handoff protocols.