Accelerate data locally for sub-second query performance — the feature and its configuration. Use this skill whenever the user asks about data acceleration concepts, enabling acceleration on a dataset, choosing refresh modes (full, append, changes, caching), configuring retention policies, setting up snapshots for cold-start, adding indexes and constraints, or understanding the difference between federated and accelerated queries. This skill covers the "what and why" of acceleration. For choosing which acceleration engine to use (Arrow vs DuckDB vs SQLite vs Cayenne), see spice-accelerators.
Choose and configure the right acceleration engine — Arrow, DuckDB, SQLite, Cayenne, PostgreSQL, or Turso. Use this skill whenever the user needs to pick an accelerator engine, compare engines (e.g. "should I use DuckDB or Cayenne?"), configure engine-specific parameters (duckdb_file, sqlite_file), tune memory vs file mode, or understand engine capabilities and limitations. This skill is the engine selection and tuning guide. For the broader acceleration feature (refresh modes, retention, snapshots, indexes), see spice-acceleration.
Add AI and LLM capabilities to Spice — tools, NSQL (text-to-SQL), memory, model routing/workers, and evals. Use this skill whenever the user wants to enable LLM tools (SQL, search, memory, MCP, web search), set up text-to-SQL via /v1/nsql, add persistent conversational memory, configure model routing with workers (load balancing, fallback, weighted distribution), set up evals, or use the OpenAI-compatible chat API. This skill covers AI features and orchestration. For configuring individual model providers (OpenAI, Anthropic, etc.), see spice-models.
Configure Spice.ai in-memory result caching for SQL queries, search results, and embeddings. Use this skill whenever the user asks about caching configuration, tuning cache TTL or max size, choosing eviction policies (LRU vs TinyLFU), enabling stale-while-revalidate, setting up cache-control headers, using custom cache keys (Spice-Cache-Key), monitoring cache metrics, choosing between plan vs SQL cache key types, or enabling zstd compression for cached results. Also use when the user asks why they're getting MISS/STALE responses or wants to optimize cache hit rates.
Manage Spice.ai Cloud resources via the Management API — apps, deployments, secrets, API keys, and org members. Use this skill whenever the user wants to create or manage a Spice.ai Cloud app, trigger a deployment, manage cloud secrets or API keys, list regions or runtime versions, add/remove org members, or automate any Spice.ai Cloud operation. Also use when the user mentions "spice.ai cloud", "deploy to spice", "cloud API", or wants to use the Spice.ai hosted platform. For infrastructure-as-code with Terraform, see spice-terraform.
Connect Spice to data sources and query across them with federated SQL — including datasets, catalogs, views, and writes. Use this skill whenever the user wants to set up federated queries across multiple sources, create views, configure catalogs (Unity Catalog, Databricks, Iceberg), write data with INSERT INTO, or understand how Spice's query federation works. This skill focuses on the federation layer — cross-source joins, views, catalogs, and data writes. For configuring individual data source connectors (PostgreSQL params, S3 file formats, etc.), see spice-data-connector.
Configure individual data source connectors in Spice — PostgreSQL, MySQL, S3, Databricks, Snowflake, DuckDB, GitHub, Kafka, and 25+ more. Use this skill whenever the user wants to add a dataset, connect to a specific database or data source, load data from S3 or files, configure connector-specific parameters, understand file formats (Parquet, CSV, PDF, DOCX), or set up hive partitioning. This skill is the reference for the `from:` and `params:` fields in dataset configuration. For cross-source federation, views, and catalogs, see spice-connect-data.
Configure AI/LLM model providers and connections in Spice — OpenAI, Anthropic, Azure, Google, xAI, Bedrock, Perplexity, Databricks, HuggingFace, and local GGUF models. Use this skill whenever the user wants to add a model, configure a specific LLM provider, set up an OpenAI-compatible endpoint (e.g. Groq, Ollama), serve a local model, configure system prompts, set parameter overrides (temperature, response format), or understand which providers are available. This skill is the model connector reference. For AI features like tools, memory, workers, and NSQL, see spice-ai.