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gen-table

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Create database tables from SQL (CTAS) or natural language descriptions

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Source SKILL.md: https://github.com/Datus-ai/Datus-agent/blob/HEAD/datus/resources/skills/gen-table/SKILL.md

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CRITICAL: Interactive vs Workflow Mode

  • When ask_user is available, use it for DDL confirmation and clarification.
  • When ask_user is not available (workflow, batch, or print mode), never call ask_user and never wait for user input. Treat the original request as authorization only for the specific non-destructive CREATE TABLE / CTAS it explicitly asks for.
  • If critical schema details, target table, target database, or destructive authorization are missing and ask_user is unavailable, stop and report exactly what is missing.

CRITICAL: Cancel = Immediate Stop

If the user selects "Cancel" at ANY point (any ask_user response), you MUST immediately stop ALL work. Do NOT:

  • Ask follow-up questions
  • Regenerate DDL
  • Continue to any subsequent phase
  • Propose alternatives

Return immediately with:

{"table_name": "", "output": "Cancelled by user."}

Phase 1: Analyze Input

Detect input mode:

  • SQL mode: User provides a JOIN SQL or other SELECT statement → CTAS path
  • Description mode: User describes table structure in natural language → CREATE TABLE path

SQL Mode (CTAS) — Go Directly to DDL

The user's SQL already fully defines the output schema. Do NOT ask the user about table usage, purpose, or column selection — the SQL is the spec.

  1. Parse the input SQL: Identify source tables, JOIN conditions, selected columns, and transformations.
  2. Call describe_table for each source table to understand column types.
  3. Optionally call execute_sql with LIMIT 10 to validate the query output.
  4. Determine table name: Derive from the SQL context (e.g., wide_order_customer). If the user specified a name, use it.
  5. Go directly to Phase 2 — do NOT ask about table usage, purpose, or column selection.

Description Mode (CREATE TABLE) — Confirm Schema First

Natural language is ambiguous, so clarification may be needed before generating DDL.

  1. Parse user description: Extract table name, columns, types, constraints.
  2. Call describe_table for any referenced existing tables to infer column types.
  3. If critical information is missing (e.g., no column names or types specified), clarify with ask_user when available. If ask_user is unavailable, stop and report the missing fields instead of guessing.
  4. Go to Phase 2 once the schema is clear.

Phase 2: Generate DDL and Authorize

Generate the exact DDL SQL statement.

SQL Mode

Generate CTAS: CREATE TABLE {schema}.{table_name} AS ({select_sql})

Description Mode

Generate: CREATE TABLE {schema}.{table_name} ({column_defs})

When ask_user is available — DDL Confirmation

Call ask_user with the complete DDL embedded in the question:

ask_user(questions=[{
  "question": "Generated DDL:\n\nCREATE TABLE {schema}.{table_name} AS (\n  SELECT ...\n);\n\nConfirm execution?",
  "options": ["Execute", "Modify", "Cancel"]
}])

Formatting rules for the question text:

  • Start with a label: "Generated DDL:" or "DDL to execute:"
  • Include the COMPLETE DDL statement — do NOT abbreviate or truncate
  • Use \n for line breaks to keep the SQL readable
  • End with a short confirmation prompt: "Confirm execution?"

Based on user response:

  • Execute: proceed to Phase 3
  • Modify: ask what to change, regenerate DDL, call ask_user again with the updated DDL
  • Cancel: STOP IMMEDIATELY. Return {"table_name": "", "output": "Cancelled by user."}. Do NOT continue.

When ask_user is unavailable — Workflow Authorization

  • Do not call ask_user.
  • Proceed to Phase 3 only when the request explicitly asks to create this table or CTAS result and the target database/table is unambiguous.
  • If the target table already exists, proceed only if the request explicitly authorizes replacement, overwrite, drop/recreate, or equivalent destructive behavior.
  • If the DDL includes DROP, ALTER, TRUNCATE, CREATE OR REPLACE, or any existing-object replacement, require explicit authorization in the original request. Otherwise stop and report the required authorization.
  • Include the final DDL in the output summary so the caller can audit what was executed.

Phase 3: Execute and Verify

  1. Call execute_sql(sql) with the confirmed or workflow-authorized DDL statement.
  2. Verify:
    • SQL Mode: Call execute_sql("SELECT COUNT(*) FROM {schema}.{table_name}") to confirm row count
    • Description Mode: Call describe_table("{schema}.{table_name}") to confirm schema matches
  3. Call describe_table("{schema}.{table_name}") to confirm the created schema.

If DDL fails:

  • Parse the error message
  • If ask_user is available, fix the SQL, show the updated DDL to the user via ask_user, and retry (up to 3 attempts)
  • If ask_user is unavailable, fix and retry directly up to 3 attempts when the intent remains the same and no new destructive action is introduced
  • If still failing, report the final error and the last attempted DDL in the output

Phase 4: Summary

Output a summary including:

  • Created table name and location
  • Row count (for CTAS) or column count (for CREATE TABLE)
  • Column list with types
  • Original SQL (for CTAS) or user description (for CREATE TABLE)
  • Hint: if the user needs a semantic model, suggest task(type="gen_semantic_model", prompt="{table_name}")

Important Rules

  • Use ask_user before executing DDL only when the tool is available.
  • In workflow mode, execute only explicitly requested non-destructive table creation; block ambiguous or destructive work instead of guessing.
  • DDL is irreversible — always include the exact DDL SQL in ask_user confirmation when interactive, or in the final output when workflow mode executes.
  • If the target table already exists, ask whether to drop/recreate/abort when interactive; require explicit replacement authorization when workflow mode.
  • Language: match user's language (Chinese input → Chinese output)
  • Do NOT modify the source tables — only create new tables
  • Single responsibility — gen-table only creates tables, does not generate semantic model YAML. For semantic model, suggest using gen_semantic_model