3. Data, Science & AI

Enables persistent memory patterns for AI agents with AgentDB, supporting session memory, long-term storage, and context management in stateful systems.

Provides expert guidance on implementing, customizing, and debugging legend components in G2 visualizations, covering rendering, layout, and interactions.

Accesses cryptocurrency market data including prices, market cap, volume, and historical data through CoinGecko API for integration.

Provides documentation and guidance for PostgreSQL database tasks including SQL queries, design, administration, and performance tuning.

Provides high-performance time-series data analytics through a PostgreSQL extension with hypertables, continuous aggregates, and compression.

Provides real-time cryptocurrency market data feeds from 40+ exchanges with normalized data for algorithmic trading and market analysis.

Enables creation, editing, and analysis of spreadsheets with formula support, formatting, and data visualization for data-driven tasks.

Helps users find Redpanda Connect components for streaming pipelines by searching use cases or technology terms like 'kafka consumer' or 'postgres output'.

Creates and debugs Bloblang transformation scripts for data mapping, format conversion, and filtering operations.

A tool for crafting and executing SQL queries, supporting simple to complex operations including multi-table joins and aggregations.

Discovers and documents database structures, including tables, columns, and relationships for data analysis and integration.

Provides foundational knowledge on context engineering for AI agent systems, covering context windows, attention mechanics, and optimization techniques.

Provides a collection of agent skills for context management, multi-agent system design, and debugging in AI production environments.

Compresses conversation history and reduces token usage for efficient LLM context management in long-running sessions.

Enables implementation of agent memory systems including cross-session persistence, knowledge graphs, and vector store-based entity tracking for AI applications.

Models agent mental states using BDI architecture, enabling cognitive agent development and neuro-symbolic AI integration through belief-desire-intention frameworks.

Analyzes AI agent reasoning traces to debug, optimize, and improve performance, resolving failures and context degradation.

Diagnoses and mitigates context degradation, attention issues, and context confusion in AI agent systems.

Automates book data extraction, SFT dataset creation, and style model training with LoRA and author voice replication capabilities.

Provides design and implementation guidance for multi-agent system patterns including supervisor, swarm, context isolation, and agent coordination.

Guides LLM project planning, pipeline architecture design, task-model evaluation, and cost estimation for agent-assisted development.

Optimizes AI model context to reduce token usage and improve efficiency through techniques such as KV-cache and context partitioning.

Enables evaluation of AI agent performance through test frameworks, rubrics, and multi-dimensional quality gates for agent pipelines.

Enables systematic LLM output evaluation through rubric creation, bias mitigation, and automated scoring for model comparison and quality assessment.