Warehouse SQL

Use this skill when the user asks natural-language questions of a data warehouse, analytics database, or TPC-H-style reporting dataset.

Scope

• schema introspection for SQLite eval databases and pluggable Postgres, ClickHouse, BigQuery, and Snowflake backends
• cached schema summaries with explicit refresh commands for scheduled runs
• reproducible TPC-H-style evaluation cases for generated SQL
• deterministic SQL safety review before execution
• read-only execution by default
• write detection and explicit per-skill grant checks before any mutation

Default Workflow

1. Refresh or read cached schema before asking the model to draft SQL:

   python3 skills/warehouse-sql/scripts/warehouse_sql.py --format json schema --backend sqlite --database ./warehouse.db
   

2. Have the model draft SQL using the cached schema, then review it before execution:

   python3 skills/warehouse-sql/scripts/warehouse_sql.py --format json review "SELECT c_name FROM customer LIMIT 10"
   

   For review-only commands, pass --model-review with the original question to invoke HybridClaw's OpenAI-compatible gateway for the business-meaning review:

   python3 skills/warehouse-sql/scripts/warehouse_sql.py --format json review --model-review --question "Show the first 10 customers" "SELECT c_name FROM customer LIMIT 10"
   

3. Return the SQL to the user before execution when the user asks for review, when the query is broad, or when the result could expose sensitive business data.
4. Execute only after the SQL review passes and include the original question:

   python3 skills/warehouse-sql/scripts/warehouse_sql.py --format json query --backend sqlite --database ./warehouse.db --execute --question "Show the first 10 customers" "SELECT c_name FROM customer LIMIT 10"
   

   query --execute always invokes model review before running SQL. Configure model review with HYBRIDCLAW_GATEWAY_URL / GATEWAY_BASE_URL and HYBRIDCLAW_WAREHOUSE_SQL_MODEL_REVIEW_TOKEN, HYBRIDCLAW_GATEWAY_TOKEN, or GATEWAY_API_TOKEN. Execution requires --question so the model can check whether the SQL answers the user's request.

Backend Contract

Supported backend names:

• sqlite — executable through Python stdlib; used by the bundled eval suite
• postgres — psycopg driver, or a connector command
• clickhouse — clickhouse-connect driver, or a connector command
• bigquery — google-cloud-bigquery driver, or a connector command
• snowflake — snowflake-connector-python driver, or a connector command

For non-SQLite warehouses, install the backend driver package in the skill runtime or provide --backend-command / HYBRIDCLAW_WAREHOUSE_SQL_<BACKEND>_COMMAND. Connector commands read SQL on stdin and emit a JSON row array, {"rows": [...]} or CSV with headers. Do not invent credentials.

BigQuery schema introspection requires --bigquery-dataset or HYBRIDCLAW_WAREHOUSE_SQL_BIGQUERY_DATASET. Set --bigquery-project / HYBRIDCLAW_WAREHOUSE_SQL_BIGQUERY_PROJECT when the dataset is not in the default project.

Schema Cache

The helper writes schema cache files outside the repo by default at:

~/.hybridclaw/warehouse-sql/schema-cache

Use --cache-dir for tests or customer-specific workspaces. Use --refresh to force re-introspection. Use schedule-refresh to register the refresh with the HybridClaw gateway scheduler:

python3 skills/warehouse-sql/scripts/warehouse_sql.py --format json schedule-refresh --backend postgres --profile analytics --every "0 */6 * * *"

Scheduled refreshes default to last-channel delivery. Use --delivery-kind channel --delivery-to <channel-id> only when the target channel id is known.

Set HYBRIDCLAW_GATEWAY_TOKEN or GATEWAY_API_TOKEN in the environment for production scheduler registration. --gateway-token is supported for tests, but tokens passed as CLI arguments can be visible in process listings.

Read/Write Rules

• Default posture is read-only. SELECT, WITH, and EXPLAIN are allowed.
• Mutating SQL is blocked unless all of these are true:
  • the user explicitly requested a write
  • the command includes --allow-write
  • the environment contains HYBRIDCLAW_WAREHOUSE_SQL_WRITE_GRANT
  • --write-grant exactly matches that environment value
• Do not set or reveal the write grant in chat. Treat it like an operator capability, not a user-facing token.
• Even with a write grant, explain the mutation and ask for confirmation before running it unless the user already gave a concrete write instruction.

SQL Review Rules

Before execution, check:

• single statement only
• read-only unless the explicit write grant path is used
• selected columns are narrow enough for the task
• LIMIT is present for exploratory row reads
• joins use known keys from the schema cache
• date and tenant filters are present when the question implies scope

The helper emits a review object with safety status and findings. Treat that as a deterministic guardrail. Pass --model-review on review-only commands to have the helper invoke the configured OpenAI-compatible model endpoint and add that verdict to review.modelReview; query --execute invokes model review automatically. Execution is blocked unless both the deterministic guardrail and model review pass.

Model review defaults to HYBRIDCLAW_GATEWAY_URL / GATEWAY_BASE_URL plus /v1/chat/completions and model auxiliary/eval_judge, with authentication from HYBRIDCLAW_WAREHOUSE_SQL_MODEL_REVIEW_TOKEN, HYBRIDCLAW_GATEWAY_TOKEN, or GATEWAY_API_TOKEN. Use --model-review-url, --model-review-model, --schema-cache, and --question to make the review explicit in tests or operator workflows.

Eval Suite

Run the offline TPC-H-style scenarios:

python3 skills/warehouse-sql/scripts/warehouse_sql.py --format json eval-scenarios

The fixture at evals/tpch_tiny.sql contains a tiny public-schema-compatible dataset using TPC-H-style tables (customer, orders, lineitem, supplier, part, nation). It is an offline deterministic fixture for SQL generation coverage, not a TPC-H benchmark run. The scenario file at evals/tpch_scenarios.json verifies read-only review and execution against deterministic answers.

To measure model SQL generation against the same deterministic fixture, run the model-backed planner:

python3 skills/warehouse-sql/scripts/warehouse_sql.py --format json eval-scenarios --model-planner --model-review-model gpt-5

Use --scenario-id <id> to isolate one scenario while iterating.

Validation

Run:

python3 skills/skill-creator/scripts/quick_validate.py skills/warehouse-sql
python3 skills/warehouse-sql/scripts/warehouse_sql.py --help
python3 skills/warehouse-sql/scripts/warehouse_sql.py --format json eval-scenarios