Agent skills

Write, execute, or optimize analytical DuckDB SQL against MotherDuck data.

Cross-engine query optimization guide. EXPLAIN/EXPLAIN ANALYZE across PostgreSQL, MySQL, MongoDB, Cassandra, ClickHouse. Index strategies…

Execute use when you need to work with query optimization.

Answer an explicitly vault-scoped question from an Obsidian wiki without changing it.

Queries the live CARLA world and resolves an entity description into a concrete actor id — find actors by type, role, or colour, or the…

Query bq-analytics data in BigQuery via the bq CLI.

Take a surface question and forge the question actually worth asking — diagnose what's broken in the asked question (smuggled answers…

Quick, safe tidy of the code — remove dead code, unused imports, stray debug prints, and minor mess, fast and with minimal talk.

Make a few quick, tiny, low-risk improvements — small readability, clarity, or obvious-win cleanups — fast and with minimal talk.

Generates and displays OVN-Kubernetes network topology diagrams showing logical switches, routers, ports with IP/MAC addresses in Mermaid…

One-line summary of key Agent Monitor metrics — active sessions, total cost from the pricing engine, events today from daily_events, top…

QuickBooks Automation: manage invoices, customers, accounts, and payments in QuickBooks Online for streamlined bookkeeping

QuickBooks Online payment handling: recording customer payments and applying them to invoices, partial and multi-invoice application…

QuickNode performance tuning \u2014 blockchain RPC and Web3 infrastructure\

QuickNode rate limits \u2014 blockchain RPC and Web3 infrastructure\

QuickNode reference architecture \u2014 blockchain RPC and Web3 infrastructure\

Quicknode blockchain infrastructure for Solana — RPC endpoints, DAS API (Digital Asset Standard) for NFTs and compressed assets…

QuickNode webhooks events \u2014 blockchain RPC and Web3 infrastructure\

Comprehensive documentation available in the repository.

Generate and vectorize SVG graphics via the QuiverAI API (Arrow model).

Generates multiple-choice quiz questions for each chapter, aligned to the learning graph and distributed across Bloom's Taxonomy levels.