db-snooper 0.0.9

Generate compact, LLM-ready database context with schema profiles and inferred join links.

DB Snooper

DB Snooper generates compact, LLM-ready database context for SQL generation, query debugging, and schema exploration. Profiling alone drives state-of-the-art text-to-SQL accuracy (Automatic Metadata Extraction for Text-to-SQL). Supports SQLite, PostgreSQL, MySQL, MariaDB, and DuckDB. Requires Python ≥ 3.10.

Specification: see spec/main.md for the design notes behind profiling, schema linking, and the text-to-SQL pipeline.

It inspects an existing database and produces two artifacts:

• A SQL profile (<database>/<schema>.sql): DDL, row counts, sampled rows, and per-column summaries. Use --per-table for one .sql per table.
• A schema-link report (<database>/<schema>_schema_links.md): declared PK/FK relationships and inferred join candidates.

AI agents and text-to-SQL pipelines can read this context instead of guessing table meanings or join paths.

Quick Start

Install with pip:

pip install db-snooper

Or run instantly with uvx (no install needed):

uvx db-snooper profile --db-type mysql --user user --password password --database db --schema sch --port 3306

This creates a profile at db/sch.sql. To (re)generate just the schema-link report for the same database:

db-snooper links --db-type mysql --user user --password password --database db --schema sch --port 3306

The schema-link report lists declared PK/FK joins and inferred join candidates with evidence labels. See What The Outputs Contain for details.

What The Outputs Contain

The profile .sql file contains:

• Metadata with db-snooper version, UTC generation timestamp, SQL dialect, database name, and schema.
• CREATE TABLE DDL, indexes, and constraints.
• Total row counts.
• Deterministic sampled rows for small tables.
• Latest and random sampled rows for larger tables.
• Per-column null, non-null, distinct, numeric range, median, top-value, and shape summaries for larger tables.
• Redacted values for sensitive column names containing password, passwd, pwd, hash, salt, secret, or token.

The schema links .md file contains:

• Declared primary-key and foreign-key links from database constraints.
• Inferred links from name, type, cardinality, and containment evidence.
• Evidence labels for each inferred join candidate.

Treat inferred links as candidates, not guaranteed joins. Validate them against the user question and the generated profile before writing final SQL.

Database Examples

SQLite

db-snooper profile --db-type sqlite --database path/to/app.sqlite
db-snooper links --db-type sqlite --database path/to/app.sqlite

PostgreSQL

Profile

db-snooper profile --db-type postgres --database app_db --schema sch --user readonly_user --host localhost --port 5432 --ask-password

Schema links

db-snooper links --db-type postgres --database app_db --schema sch  --user readonly_user --host localhost --port 5432 --ask-password

MySQL

db-snooper profile --db-type mysql --database app_db --user readonly_user --host localhost --port 3306 --ask-password
db-snooper links --db-type mysql --database app_db --user readonly_user --host localhost --port 3306 --ask-password

MariaDB

db-snooper profile --db-type mariadb --database app_db --user readonly_user --host localhost --port 3306 --ask-password
db-snooper links --db-type mariadb --database app_db --user readonly_user --host localhost --port 3306 --ask-password

DuckDB

db-snooper profile --db-type duckdb --database warehouse.duckdb --schema sch
db-snooper links --db-type duckdb --database warehouse.duckdb --schema sch

Environment Variables

Connection values can come from environment variables instead of flags:

DB_SNOOPER_DB_TYPE=sqlite \
DB_SNOOPER_DATABASE=eval-dataset/student_club/student_club.sqlite \
db-snooper profile

Supported variables:

• DB_SNOOPER_DB_TYPE
• DB_SNOOPER_DATABASE
• DB_SNOOPER_DB_HOST
• DB_SNOOPER_DB_PORT
• DB_SNOOPER_DB_USER
• DB_SNOOPER_DB_PASSWORD
• DB_SNOOPER_SCHEMA

For server databases, --host defaults to localhost, --port defaults to the database default, and DB Snooper securely prompts for a password when DB_SNOOPER_DB_PASSWORD is not set.

Help

db-snooper -h
db-snooper profile -h
db-snooper links -h

Table filters:

db-snooper profile --db-type sqlite --database app.sqlite --include-tables users,orders,line_items
db-snooper links --db-type sqlite --database app.sqlite --exclude-tables audit_log,temp_imports

Schema filter:

db-snooper profile --db-type postgres --database app_db --schema reporting --user readonly_user --port 5432 --ask-password
DB_SNOOPER_SCHEMA=reporting db-snooper links --db-type postgres --database app_db --user readonly_user --port 5432 --ask-password

Profile options:

• --small-table-threshold 50: tables with this many rows or fewer are sampled instead of column-profiled.
• --large-table-threshold 100000000: tables whose catalog row estimate is at/above this count are profiled from internal database stats only. COUNT(*), sampled rows, and per-column queries are skipped because they would be too slow on hundreds of millions of rows; the output shows an approximate row count instead.
• --sample-row-limit 50: maximum sampled rows for small tables.
• --include-tables table_a,table_b: only profile selected tables.
• --exclude-tables table_c: skip selected tables.
• --per-table: generate one .sql profile for each table instead of a single schema profile.

Schema-link options:

• --include-tables table_a,table_b: only inspect selected tables.
• --exclude-tables table_c: skip selected tables.
• --containment-threshold 0.8: minimum exact containment for inferred links.
• --max-distinct-values 10000: maximum distinct values loaded per candidate column.

Python API

Use the simple helpers when you have a SQLAlchemy URL:

from db_snooper import generate_profile, generate_schema_links

database_url = "sqlite:///eval-dataset/superhero/superhero.sqlite"

profile_sql = generate_profile(database_url)
schema_links_md = generate_schema_links(database_url)

Use the lower-level API when you already have a SQLAlchemy engine or need options:

from sqlalchemy import create_engine
from db_snooper import ProfileOptions, SchemaLinkOptions, link_schema, profile_database

engine = create_engine("sqlite:///eval-dataset/superhero/superhero.sqlite")

profile_sql = profile_database(
    engine,
    ProfileOptions(sample_row_limit=25, include_tables=frozenset({"superhero", "publisher"})),
)
schema_links_md = link_schema(
    engine,
    SchemaLinkOptions(containment_threshold=0.9),
)

Agent Skills

DB Snooper ships reusable agent skills that teach AI agents when and how to profile a database and discover join links. Three skills are bundled:

Skill	Triggers on	Command	Output
db-snooper-profile	profiling, schema/data context, table summaries, column distributions	db-snooper profile	<db>/<schema>.sql
db-snooper-schema-links	join paths, relationships, FK discovery, schema links	db-snooper links	<db>/<schema>_schema_links.md
db-snooper-context	both / general text-to-SQL context	profile then links	both files

List the bundled skills:

uvx db-snooper skills list

Install the skills into an agent's discovery directory (no repo clone needed; the SKILL.md files ship inside the wheel):

# Default: opencode global (~/.config/opencode/skills)
uvx db-snooper skills install

# All three common discovery locations at once (opencode + Claude + agents)
uvx db-snooper skills install --target all

# Custom or project-local directory
uvx db-snooper skills install --dir ./.opencode/skills --force

Discovery directories:

• --target opencode → ~/.config/opencode/skills (default)
• --target claude → ~/.claude/skills
• --target agents → ~/.agents/skills
• --target all → all three
• --dir PATH → any custom path (overrides --target)

For zero per-user setup, commit the installed skill folders (for example .opencode/skills/) into your repository. Agents that walk the working directory discover them automatically.

License

The DB Snooper source code is licensed under the MIT License. See LICENCE.

Third-party Python dependencies remain under their own upstream licenses. See THIRD_PARTY_NOTICES.md for a dependency license summary.

The dataset files included under eval-dataset/ are derived from birdsql by The BIRD Team, and are used and redistributed under the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0).

These files are not covered by the MIT source-code license. They retain their original CC BY-SA 4.0 terms. Any derivative works that include these files must also be distributed under CC BY-SA 4.0.