SimHacker 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
.jsonto 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
- From the cursor-mirror skill directory (or with
PYTHONPATHset solibimports resolve), run Python to callexport_datasette(Path("cursor-mirror.db"))(seedatasette_export.pyfor parameters and defaults). - Ensure
cursor-model.dbexists if you want the universal model side (same metadata file names both databases). - 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