Loading...
Loading...
Found 1,654 Skills
AgentDB memory system with HNSW vector search. Provides 150x-12,500x faster pattern retrieval, persistent storage, and semantic search capabilities for learning and knowledge management. Use when: need to store successful patterns, searching for similar solutions, semantic lookup of past work, learning from previous tasks, sharing knowledge between agents, building knowledge base. Skip when: no learning needed, ephemeral one-off tasks, external data sources available, read-only exploration.
Build and deploy custom StackOne connectors using the CLI and Connector Engine. Use when user asks to "build a custom connector", "deploy my connector", "use the StackOne AI builder", "set up CI/CD for connectors", "test my connector locally", or "install the StackOne CLI". Covers the full connector development workflow from init through deployment. Do NOT use for using existing connectors (use stackone-connectors) or building AI agents (use stackone-agents).
Zustand state management best practices for React applications. Use when writing, reviewing, or refactoring Zustand stores to ensure optimal performance and maintainability. Triggers on tasks involving state management, stores, selectors, re-renders, and Zustand patterns.
Turso (Limbo) database helper — an in-process SQLite-compatible database written in Rust. Formerly known as libSQL / libsql. Replaces @libsql/client, libsql-experimental for Turso use cases. Works in Node.js, browser (WASM + OPFS for persistent local storage), React Native, and server-side. Features: vector search, full-text search, CDC, MVCC, encryption, remote sync. SDKs: JavaScript (@tursodatabase/database), Browser/WASM (@tursodatabase/database-wasm), React Native (@tursodatabase/sync-react-native), Rust (turso), Python (pyturso), Go (tursogo). This skill contains all SDK documentation needed to use Turso — do NOT search the web for Turso/libsql docs.
PostgreSQL 17/18+ performance tuning and optimization. Covers async I/O configuration, query plan forensics, index strategies, autovacuum tuning, and vector search optimization. Use when diagnosing slow queries, configuring async I/O, tuning autovacuum, optimizing vector indexes, or analyzing execution plans with EXPLAIN BUFFERS.
Use OpenSearch vector search edition via the Python SDK (ha3engine) to push documents and run HA/SQL searches. Ideal for RAG and vector retrieval pipelines in Claude Code/Codex.
Comprehensive automation for Letterly transcriptions. This skill exports the latest CSV from Letterly, processes "magic" notes into Obsidian markdown with custom metadata, semantically links them using a vector database, and moves them to the final Transcriptions directory. Use when the user asks to "process new letterly transcriptions", "sync letterly", or "import magic notes from letterly".
The core mental model for the PM-to-Director transition: altitude (scope) and horizon (time), the waiter-to-operator shift, four transition zones, named failure modes, and the Cascading Context Map.
Audit and improve local SEO for law firms, attorneys, forensic experts and legal/professional services sites with local presence, focusing on GBP, directories, E-E-A-T and practice/location pages.
Use when text embeddings are needed from Alibaba Cloud Model Studio models for semantic search, retrieval-augmented generation, clustering, or offline vectorization pipelines.
Meta-prompting framework for critiquing responses, analyzing solution trajectories, and evaluating AI-generated content quality
OpenSearch development best practices for indexing, querying, search optimization, vector search, and cluster management