claude-skills-librechat

Use when creating LibreChat agents, writing agent system prompts, configuring agent capabilities (file search, code interpreter, tools, artifacts), managing agent sharing/permissions, or debugging agent behavior. Also use for agent-based access control patterns.

Use when configuring LibreChat, editing librechat.yaml, adding endpoints, setting up model providers, configuring modelSpecs, or adjusting interface settings. Also use when asked about LibreChat configuration options, YAML structure, or endpoint setup.

Use when LibreChat has errors, containers won't start, API calls fail, file uploads break, models return errors, or something isn't working as expected. Also use for checking service health, reading logs, and diagnosing performance issues.

Use when setting up LibreChat RAG (Retrieval-Augmented Generation), configuring embeddings providers, setting up file search in agents, configuring PGVector/PostgreSQL for vector storage, or troubleshooting document indexing and retrieval. Also use when users ask about 'chat with documents' or 'file search' features.

Use when configuring LibreChat agent tools and capabilities: code interpreter, web search, image generation (Flux, Stable Diffusion, DALL-E), Google Search, Wolfram Alpha, Azure AI Search, OpenWeather, artifacts, or OCR. Also use when enabling or disabling specific agent capabilities in librechat.yaml.

Use when configuring MCP (Model Context Protocol) servers in LibreChat, connecting external tools via MCP, setting up mcpServers in librechat.yaml, or debugging MCP tool connections. Also use when asked about extending LibreChat agents with external capabilities through MCP.

Use when deploying LibreChat with Docker Compose, setting up docker-compose.yml or docker-compose.override.yml, configuring containers, setting up reverse proxies (nginx, Traefik), deploying to cloud providers (Azure, DigitalOcean, Railway), or running LibreChat locally for development. Also use when asked about container networking, volume mounts, or GPU passthrough.

Use when configuring LibreChat's MongoDB database, Redis caching, MeiliSearch message search, file storage strategy (local vs CDN/S3), or PGVector for RAG. Also use when asked about database backup, connection strings, or storage scaling.

Use when setting up LibreChat logging, configuring metrics collection, integrating Langfuse for LLM observability, tracking token usage and costs, or analyzing LibreChat server logs. Also use when asked about monitoring LibreChat in production or debugging performance from logs.

Use when upgrading LibreChat to a new version, migrating data between versions, handling breaking changes, running database migrations, or migrating agent permissions. Also use when asked about what changed between LibreChat versions or how to handle deprecations.

Use when restricting what LibreChat users can access: limiting models, enforcing agent-only mode, setting spending limits, managing user balances, creating per-course or per-group access patterns, disabling features, or controlling registration. Also use when asked about RBAC, user roles, or multi-tenant LibreChat setups.

Use when configuring LibreChat authentication: setting up OAuth2/OIDC providers (Azure AD, Google, GitHub, Keycloak, Auth0), configuring SAML, setting up LDAP, enabling social logins, configuring email verification, or integrating with institutional identity providers. Also use when SSO or login is not working.