Show HN: Sqlsure – deterministic semantic checks for AI-generated SQL

Sqlsure, a deterministic semantic checker for AI-generated SQL, catches logical errors like double-counted revenue and exposed patient identifiers that databases and linters miss. The tool, which runs in 0.1 ms without network calls, flagged 45 errors in 2,568 expert-written benchmark queries with zero false alarms, including a BIRD dev gold answer provably wrong by 8×.

AI writes your SQL. sqlsure makes sure it's right. A query can be perfectly valid, run without error, and return a number that's silently wrong — revenue double-counted by a join, an average summed, a patient identifier exposed. Databases don't catch this. Linters don't catch this. LLMs reviewing their own SQL don't catch this. sqlsure does — deterministically, in 0.1 ms, before the query runs. Proof, not promises:we ran sqlsure over the gold answers of the two benchmarks every text-to-SQL model is graded on.2,568 expert-written queries, 45 flags, zero false alarms— including a BIRD dev gold answer that is provably wrong by 8× from the exact bug class sqlsure targets, and a schema defect now filed upstream . sqlsure judges SQL against facts your team already declared — dbt unique tests become grain, relationships tests become join cardinality, one-line meta tags mark what's safe to sum. No new language to learn, no model to maintain by hand. Rules are dictionary lookups, not LLM calls: same input, same verdict, every time, offline. Every rejection carries a machine-actionable fix , so AI agents self-repair: draft → check → fix → check → execute. In our benchmark, applying the fix verbatim produced a passing query 10/10 times. pip install sqlsure python from sqlsure import SemanticModel, check violations = check sql, model means semantically safe Or clone and run the 30-second demo: python check.py 5 wrong queries rejected, 1 approved — with fixes python -m sqlsure.scan path/to/dbt-repo --report report.md audit any dbt repo 1. CI gate — blocks the merge when a PR double-counts: python -m sqlsure.cli --model model.json query.sql exit 1 on violations 2. MCP server — your AI agent must pass inspection before executing: claude mcp add sqlsure -- python -m sqlsure.mcp server --model /abs/path/model.json See docs/MCP.md /sqlsure/sqlsure/blob/main/docs/MCP.md for tool reference and agent-loop patterns. 3. Library — embed check inside any text-to-SQL product or agent framework. A drop-in SemanticGate /sqlsure/sqlsure/blob/main/integrations/semantic gate.py wraps Vanna/WrenAI-style generators; a semantic eval metric /sqlsure/sqlsure/blob/main/integrations/eval metric.py scores NL2SQL output where execution-accuracy is blind. | Rule | Severity | Catches | |---|---|---| | FANOUT | error | SUM/COUNT of additive measure after one-to-many join | | CHASM | error | two+ fan-out joins multiplying each other | | ADDITIVITY | error | SUM of a non-additive measure rates, averages | | SEMI ADDITIVE | error | balances/censuses summed across their snapshot dimension | | JOIN KEY | error | join on columns matching no declared relationship | | CROSS JOIN | error | join with no predicate | | WEIGHTED AVG | warning | AVG silently re-weighted by fan-out | | UNDECLARED JOIN | warning | join with no declared relationship unverifiable ≠ safe | | SENSITIVE COLUMN | policy | PHI/PII column exposed in query output | When sqlsure can't verify something, it says "can't verify" — never "looks fine." Honest uncertainty is a feature. Deterministic — same SQL + same rulebook = same verdict, always; rules are dictionary lookups, auditable line by line Offline — zero network calls; your SQL never leaves your machine No data access — parses query text ; never connects to a database No telemetry — nothing collected, ever SECURITY.md /sqlsure/sqlsure/blob/main/SECURITY.md Supply chain — releases ship exclusively via PyPI Trusted Publishing OIDC from tagged commits with public CI runs; two runtime deps - dbt works today : manifest.json or schema.yml — the tests teams already wrote become enforceable semantics, zero config - Plain PK/FK declarations works today — powered the benchmark audits - The live database itself works today : no semantic layer at all? sqlsure.introspect builds the rulebook from the catalog — SQLite PRAGMAs or information schema PK/FK postgres/mysql . Introspecting BIRD's own database files recovered 2 foreign keys missing from the benchmark's published schema bird-bench/mini dev 37 https://github.com/bird-bench/mini dev/issues/37 python from sqlsure.introspect import model from sqlite model = model from sqlite "app.db" PK - grain, FK - join edges - Hand-written JSON — model.example.json /sqlsure/sqlsure/blob/main/model.example.json - OSI and WrenAI MDL working loaders in integrations/ /sqlsure/sqlsure/blob/main/integrations : OSI /sqlsure/sqlsure/blob/main/integrations/osi loader.py demonstrated on the spec's published examples; WrenAI MDL /sqlsure/sqlsure/blob/main/integrations/mdl loader.py demonstrated on WrenAI's own shipped example manifest — primaryKey → grain, relationship joinType + condition → join edges, cube measures → additivity - Cube, Snowflake Semantic Views — adapters on the roadmap; the engine only ever sees one SemanticModel 16/16 rule tests, 100% recall / 0% false positives on the paired benchmark docs/METRICS.md /sqlsure/sqlsure/blob/main/docs/METRICS.md Real production repos Mattermost's warehouse, Fivetran packages, dbt's jaffle shop — docs/TEST-REPORTS.md /sqlsure/sqlsure/blob/main/docs/TEST-REPORTS.md Spider + BIRD gold queries — the zero-noise external audit above docs/EVIDENCE.md /sqlsure/sqlsure/blob/main/docs/EVIDENCE.md — what it does for you, every claim linked to a rerunnable measurement docs/ARCHITECTURE.md /sqlsure/sqlsure/blob/main/docs/ARCHITECTURE.md — how it physically works, ELI5 → god level, with real intermediate outputs docs/FOR-DUMMIES.md /sqlsure/sqlsure/blob/main/docs/FOR-DUMMIES.md — every concept from zero docs/INTEGRATIONS.md /sqlsure/sqlsure/blob/main/docs/INTEGRATIONS.md — GitHub Action, pre-commit, MCP, Snowflake UDF / Cortex Agent tool, query-history audit docs/MCP.md /sqlsure/sqlsure/blob/main/docs/MCP.md — MCP server documentation CONTRIBUTING.md /sqlsure/sqlsure/blob/main/CONTRIBUTING.md — adding rules and loaders Apache-2.0 · sqlsure.ai https://sqlsure.ai mcp-name: io.github.sqlsure/sqlsure