Agent Skills Hub

bazinga-db-core

Manage database orchestration sessions, state snapshots, and system-level operations for the BAZINGA-DB core engine.

long-running-agent

Framework for building AI agents that persist state across multiple context windows, enabling them to complete complex, multi-day coding tasks without losing progress or context.

ReasoningBank with AgentDB

Implement ReasoningBank adaptive learning with AgentDB's ultra-fast vector backend. Features trajectory tracking, verdict judgment, memory distillation, and pattern recognition for self-learning autonomous agents.

qras

A local RAG semantic memory system using Qdrant and Ollama. Ideal for recalling workspace files, notes, project decisions, and user preferences with high-relevance vector search.

dlt-skill

Create, manage, and debug dlt (data load tool) pipelines for ingesting data from APIs, databases, and custom sources into destinations like DuckDB, BigQuery, and Snowflake.

test-delete-skill

Unified AI gateway for 100+ LLMs with OpenAI-compatible API, model fallbacks, load balancing, and enterprise-grade tools.

context-cascade

A nested plugin architecture for Claude Code that optimizes context by dynamically loading playbooks, skills, and agents to save over 90% in token usage.

building-ai-agent-on-cloudflare

Build stateful AI agents on Cloudflare Workers using the Agents SDK. Features real-time WebSockets, persistent state management, scheduled background tasks, and native tool integration for production-ready deployments.

common

Infrastructure for cross-product HealthSim data persistence, entity correlation via SSN, and DuckDB database operations.

polars

High-performance in-memory DataFrame library for Python and Rust. Features lazy evaluation, parallel execution, and an Apache Arrow backend for efficient ETL, data processing, and faster pandas alternatives.

librarian

Fetch, index, and search developer documentation from GitHub and websites to provide AI agents with accurate, grounded, and version-specific code context.

training-data-curation

Guidelines for curating high-quality datasets for LLM post-training (SFT/DPO/RLHF), covering data formats, quality filtering, and collection strategies.