Connect data → infer semantics → query with natural language → get answers.
All output is JSON unless noted. No API key needed.
bash
lucid-skill connect csv /path/to/sales.csv # Connect data
lucid-skill overview # Check connected sources
lucid-skill search "月度销售额趋势" # Find relevant tables + suggested SQL
lucid-skill query "SELECT month, SUM(amount) FROM sales GROUP BY month" # Execute
CommandPurposeoverviewShow all connected sources, tables, semantic statusconnect csv/excel/mysql/postgresConnect a data sourcetablesList all tables with row countsdescribe <table>Column details + sample data + semanticsprofile <table>Deep stats: null rate, distinct, min/max, quartilesinit-semanticExport schemas for semantic inferenceupdate-semantic <file|->Save semantic definitions (JSON from file or stdin)search <query> [--top-k N]Natural language → relevant tables + JOIN hints + metric SQLjoin-paths <a> <b>Discover JOIN paths between two tablesdomainsAuto-discovered business domainsquery <sql> [--format json|md|csv]Execute read-only SQLserveStart MCP Server (stdio JSON-RPC)
For full command reference with all parameters: read references/commands.md
When a user asks a data question:
lucid-skill search "关键词" — find relevant tables, suggestedJoins, suggestedMetricSqlslucid-skill join-paths table_a table_b — get JOIN SQLlucid-skill query "SELECT ..." — execute and present resultsFirst-time setup to enable intelligent search:
bash
lucid-skill init-semantic # Export schemas
# Analyze output → infer business meanings for each column
echo '{"tables":[...]}' | lucid-skill update-semantic - # Save semantics
For JSON schema details: read references/json-schema.md
overview first to check existing state.~/.lucid-skill/semantic_store/ (YAML, human-readable).~/.lucid-skill/ (override with LUCID_DATA_DIR env var).LUCID_EMBEDDING_ENABLED=true for better multilingual search (downloads ~460 MB model on first use).lucid-skill serve starts stdio JSON-RPC server for MCP integrations.update-semantic JSON format specification