Datasette

Part of MOOLLM · skills/datasette/

Datasette (datasette.io) is the CLI and runtime for publishing SQLite files as a read-only web application and JSON API. It sits on top of the same .db file format as the sqlite engine: this skill is the tooling and ecosystem layer (serve, metadata, plugins, CORS), not a second database engine.

sqlite → datasette: you pass the same path as sqlite3 my.db. Datasette does not build a second on-disk database for serving—read-only HTTP over that file. That is the usual sense of “zero copy” live publishing here: no duplicate .db artifact; refreshes see committed data from other writers when the file uses a normal WAL setup. For a static snapshot, serve a copy or use --immutable on a file you do not change.

What you get

• datasette — local web server; table browse, filters, Custom SQL, export links.
• Metadata YAML — per-database titles, facets, sortable columns, canned queries (named SQL with optional :parameters).
• --crossdb — attach multiple SQLite files; run SQL that references more than one database (see Datasette docs for exact SQL shape).
• Plugins — ecosystem extends CSV export, maps, full-text helpers, etc. (Plugin directory).
• JSON API — append .json to many URLs for programmatic use (JSON API).

Official documentation: https://docs.datasette.io/

Install

Use pip / pipx / your OS package manager per current Datasette docs. You need the datasette command on PATH.

Basic serve

datasette mydata.db

Opens a local port (default in docs) with table list and row browsing.

Metadata

Point at a YAML file with -m / --metadata. It can define title, description, per-databases, per-tables (facets, labels), and queries (canned SQL surfaced in the UI).

cursor-mirror integration

MOOLLM’s cursor-mirror skill ships a consolidated export and a large metadata file tuned for introspection:

Artifact	Role
lib/datasette_export.py → export_datasette(output_path)	Builds a single cursor-mirror.db with tables such as composers, transcript_sections, shell_commands, tool_calls, usage_events, feature_flags (see module docstring).
reference/universal/datasette-metadata.yml	Datasette metadata: canned queries (tool usage, shell audit, cost, timelines), facets, cross-database layout for cursor-mirror + cursor-model.
reference/universal/TIME-INDEXED-TABLES.md	Notes on time columns for bucketing and correlating with other SQLite DBs in the same Datasette process.

Typical flow

1. From the cursor-mirror skill directory (or with PYTHONPATH set so lib imports resolve), run Python to call export_datasette(Path("cursor-mirror.db")) (see datasette_export.py for parameters and defaults).
2. Ensure cursor-model.db exists if you want the universal model side (same metadata file names both databases).
3. Serve with cross-database and metadata:

datasette cursor-mirror.db cursor-model.db --crossdb \
  -m reference/universal/datasette-metadata.yml \
  --cors -p 8001

Adjust paths to your checkout. Extra SQLite files (e.g. other Cursor history DBs) can be additional positional arguments; they appear as separate databases and can be joined in Custom SQL when timestamps align (see TIME-INDEXED-TABLES.md).

Security: Datasette is powerful; do not expose it on untrusted networks without authentication and threat modeling. Treat exported DBs as sensitive (chat content, tool history, shell commands).

Relationship to other skills

Skill	Role
sqlite	Engine and .db file semantics; sqlite3 CLI for direct SQL.
cursor-mirror	Source data and export; mirror CLI and YAML model docs.
schema	Schemapedia datasette mechanism points here.

Related skills

• sqlite — file format, pragmas, JSON1 when you edit the DB outside Datasette.
• cursor-mirror — full introspection protocol and mirror commands.

Part of MOOLLM

This skill's directory (browse and fetch everything): skills/datasette/

• MOOLLM: repo · Skill index and docs: skills/README