Browse published AI skills maintained by juanre. Compare skill descriptions and follow each source repository for setup instructions.
Indexed skills
22Repository stars across indexed skills
48Repository forks
0Average repository stars per indexed skill
2Repository stars may repeat across skills from the same repository.
Use when getting started with llmemory document storage and search - covers installation, initialization, adding documents, vector search, hybrid search, semantic search, BM25 full-text search, document managem...
Use when building search systems that need both semantic similarity and keyword matching - covers combining vector and BM25 search with Reciprocal Rank Fusion, alpha tuning for search weight control, and optimi...
Use when building SaaS applications needing data isolation between customers - implements owner-based filtering for secure multi-tenant document storage and search with workspace, organization, or tenant-level...
Use when search queries need better recall through query expansion - generates multiple query variants, retrieves with each, and fuses results using RRF for improved retrieval quality especially with ambiguous...
Use when building Retrieval-Augmented Generation systems - covers document ingestion, hybrid search retrieval, reranking results, and prompt augmentation for accurate LLM responses grounded in your knowledge ba...
Use when implementing function calling, tool use, or agents with LLMs - unified tool API works across OpenAI, Anthropic, Google, and Ollama with consistent tool definition and execution patterns
Use when building real-time chat interfaces, displaying incremental LLM responses, or streaming output from OpenAI, Anthropic, Google, or Ollama - async iteration with usage tracking works across all providers
Use when starting a new project with llmring, building an application using LLMs, making basic chat completions, or sending messages to OpenAI, Anthropic, Google, or Ollama - covers lockfile creation (MANDATORY...
Use when extracting structured data from LLMs, parsing JSON responses, or enforcing output schemas - unified JSON schema API works across OpenAI, Anthropic, Google, and Ollama with automatic validation and pars...
Use when switching between LLM providers, accessing provider-specific features (Anthropic caching, OpenAI logprobs), or using raw SDK clients - covers multi-provider patterns and direct SDK access for OpenAI, A...
Use when creating llmring.lock file for new project (REQUIRED for all applications), configuring model aliases with semantic task-based names, managing environment-specific profiles (dev/staging/prod), or setti...
How to update and publish pgdbm skills for Claude Code users
Use when implementing shared connection pool for multiple services in same application - provides complete setup steps for FastAPI/multi-service apps without needing examples
Use when building reusable PyPI package with pgdbm - provides complete pattern for libraries that work standalone or embedded without needing examples
Use when building simple standalone service or microservice with its own database - provides complete setup without shared pool complexity
Use when implementing pgdbm database operations - provides complete AsyncDatabaseManager and DatabaseConfig API with all methods and parameters
Use when writing tests for pgdbm-based code - provides fixture selection decision tree and complete usage patterns without needing testing docs
Use when setting up pgdbm database connections, migrations, or multi-service architecture - provides mental model of one pool/many schemas/template syntax and complete API reference without needing to read docs
How to release a new pgdbm library version to PyPI
Use when working with database migrations in pgdbm - provides complete AsyncMigrationManager API with all methods and migration file format
Use before implementing pgdbm patterns to avoid common mistakes - provides rationalization table and red flags that prevent pool multiplication, schema errors, and template syntax violations
Use when deciding which pgdbm pattern to use (standalone, dual-mode library, or shared pool) - provides decision tree based on deployment context without requiring doc exploration
The default order uses GitHub repository stars from our indexed data. Stars belong to the source repository and may be shared by multiple skills. They indicate repository interest, not skill installs or an independent quality score.