Sqlite DB Generator

SQLite Store Skill

A skill for designing and building a local SQLite database. Always start with the brainstorming phase — rushing to tables before understanding the domain produces schemas that need to be rewritten. The schema.sql file is the single source of truth once design is done. After any schema change, run the sync command to keep the db and this skill in sync.

Other skills can consume this db via the standard context and schema commands — no Markdown parsing required.

File Layout

Local scope (default) — DB belongs to a specific project

<project>/
├── schema.sql                          ← source of truth; edit this to change the db
├── migrations/                         ← versioned .sql files for post-deploy changes
│   └── 001_initial.sql
├── scripts/
│   ├── init_db.py                      ← bootstrap + sync; run after every schema change
│   └── db_cli.py                       ← template; init_db.py bakes DB_PATH and copies it
├── db/
│   └── <name>.db                       ← DB lives here (add to .gitignore)
└── .claude/skills/<name>-db/
    ├── SKILL.md                        ← auto-generated agent skill (schema + commands)
    └── db_cli.py                       ← baked copy with absolute DB_PATH

Global scope — personal DB usable from any project

Everything lives together in one folder; nothing is written to the current project directory.

~/db/<name>/
├── <name>.db                           ← the database
├── schema.sql                          ← source of truth
├── init_db.py                          ← run from anywhere: python3 ~/db/<name>/init_db.py
├── db_cli.py                           ← template; init_db.py bakes DB_PATH and copies it
└── migrations/
    └── 001_initial.sql

~/.claude/skills/<name>-db/
├── SKILL.md                            ← auto-generated agent skill (schema + commands)
└── db_cli.py                           ← baked copy with absolute DB_PATH

init_db.py copies db_cli.py into the skill folder at first run with DB_PATH hardcoded to the absolute path — so the agent can call it from any working directory.

Scope options (set at the top of init_db.py):

Phase 1 — Brainstorm Before Writing SQL

Never open a text editor until this phase is complete. Ask the user these questions one group at a time, waiting for answers before moving on. Use their answers to catch design problems early — it is much cheaper to rename an entity now than to migrate data later.

1.1 — Goals and scope

Ask:

• What are you trying to achieve with this database? What problem does it solve?
• Who or what reads from it — a human via CLI, an agent, a web app, or all three?
• Roughly how much data do you expect — hundreds of rows, millions, or somewhere in between?
• Does this need to be shared across machines, or is local-only fine?
• Will other skills or tools need to read from this db? If so, what do they need to know about it?

Listen for: scale hints (affects index strategy), sharing needs (flag if they describe multi-writer or networked access — SQLite may be the wrong choice), and interop signals (another skill consuming this db means the context/schema commands are essential).

1.2 — Major entities

Ask:

• What are the main "things" your app tracks? (e.g. users, orders, products, sessions, events)
• For each thing: what's the natural identifier — a name people type, an auto-generated ID, or something from an external system (like an order number from an API)?
• Are any of these things hierarchical? (e.g. "a cart has items")

From their answers, draft a plain-English entity list like:

Entities identified:
- Order       — one per placed order; identified by auto-increment ID
- Template    — named reusable config; identified by a human-chosen name
- OrderItem   — child of Order; one row per line item

Show this to the user and confirm before continuing.

1.3 — Relationships

Ask:

• Which entities belong to other entities? (one order has many items, one user has many orders)
• Can something belong to multiple parents? (a product can be in many orders — many-to-many)
• When a parent is deleted, what should happen to its children?
  • Cascade delete (children disappear too)?
  • Restrict (block the delete if children exist)?
  • Set null (children become orphaned but stay)?

Map this to foreign keys:

OrderItem.order_id → Order.id   ON DELETE CASCADE

Flag many-to-many relationships early — they need a join table and the user may not realize it.

1.4 — Column special cases

For each entity, ask:

Full-text search

• Do users need to search this entity by keyword? (e.g. "find orders mentioning pepperoni")
• If yes → plan an FTS5 virtual table + sync trigger for that entity (see Column Patterns below).

JSON / flexible payloads

• Are there fields where the shape varies per row, or you don't know all the keys yet?
• If yes → TEXT column storing JSON. Name it payload, metadata, or config (not data — too vague). Always note in the comment what keys you expect: -- JSON: {store_id, items[], note}.

Enumerations

• Are there status or type fields with a fixed set of values?
• If yes → TEXT with a CHECK constraint: CHECK(status IN ('pending','active','done')). Don't use integers for enums in SQLite — unreadable in queries.

Vectors

• Do you need semantic similarity search (find records "like" this one)?
• If yes → plan a vec0 virtual table via sqlite-vec. Requires pip install sqlite-vec and storing embeddings separately from the main table (see Column Patterns below).

Soft deletes

• Will any other skill or tool need to see historical records even after they're "removed"?
• Will you need an audit trail or undo capability?
• If yes (or if unsure) → add deleted_at TEXT DEFAULT NULL to the table. Rows are never hard-deleted; instead UPDATE t SET deleted_at = datetime('now') WHERE id = ?. All read commands filter WHERE deleted_at IS NULL by default; pass include_deleted=true to see all.

1.5 — Query patterns

Ask:

• What are the three most common queries you'll run? (e.g. "get all pending orders", "find a template by name", "order history for a customer")
• Do you need to filter or sort by date often?
• Do you need to look things up by a non-primary-key field frequently? (e.g. "find order by phone number" — that column needs an index)
• Will you ever need to run ad-hoc queries not covered by named commands? (if yes → the query escape hatch in db_cli.py handles this safely)

Use answers to plan indexes beyond the primary key.

1.6 — Storage scope

Ask:

• Will this DB be used only inside this project, or do you want it accessible from any project (or by a webapp running independently)?

Local (DB_SCOPE=local, default):

• DB at ./db/<name>.db — travels with the project, easy to gitignore
• Skill at .claude/skills/<name>-db/ — only visible inside this project
• Best for: a DB that belongs to a specific codebase

Global (DB_SCOPE=global):

• Everything at ~/db/<name>/ — DB, schema.sql, migrations/, init_db.py all in one folder; nothing written to the project directory
• Skill at ~/.claude/skills/<name>-db/ — visible from every project
• Best for: personal tools (second brain, bug tracker, link vault)

Webapp access: In both scopes, webapps connect directly to the .db file path — no API layer needed. The generated skill documents the path and driver snippets.

Flag if they say: "share it", "export it", "download it" — use db-export command in db_cli.py which copies the .db file itself. Add present_files call if available.

1.7 — Design confirmation

Before writing any SQL, present a summary:

Proposed design:
  Tables:       orders, order_items, templates
  Soft deletes: orders (deleted_at column)
  FTS:          orders_fts (on customer, item fields)
  Indexes:      orders(status), orders(created_at), order_items(order_id)
  JSON cols:    templates.payload {store_id, items[], payment_ref}
  Enums:        orders.status IN ('pending','confirmed','delivered','cancelled')
  DB path:      ./myapp.db  (project-local, added to .gitignore)
  Migrations:   migrations/ folder, versioned from 001

Questions / flags:
  - order_items is a new table not mentioned initially — confirm this is right
  - Soft deletes on orders only; order_items cascade-delete so no need there
  - No vector search planned; add later if needed

Get explicit confirmation before moving to Phase 2.

Phase 2 — Write schema.sql

With the design confirmed, translate it into SQL. Follow all rules below.

Mandatory columns on every table

created_at  TEXT NOT NULL DEFAULT (datetime('now')),  -- set once on insert, never updated
updated_at  TEXT NOT NULL DEFAULT (datetime('now')),  -- updated via trigger on every UPDATE
deleted_at  TEXT DEFAULT NULL                         -- NULL = live; timestamp = soft-deleted

Always add updated_at and soft-delete triggers for every table:

CREATE TRIGGER orders_updated_at
AFTER UPDATE ON orders
BEGIN
    UPDATE orders SET updated_at = datetime('now') WHERE id = NEW.id;
END;

Column patterns

Standard row (append-only records):

CREATE TABLE IF NOT EXISTS orders (
    id           INTEGER PRIMARY KEY AUTOINCREMENT,
    created_at   TEXT    NOT NULL DEFAULT (datetime('now')),
    updated_at   TEXT    NOT NULL DEFAULT (datetime('now')),
    deleted_at   TEXT    DEFAULT NULL,                         -- soft delete
    customer     TEXT    NOT NULL,                             -- display name or user ID
    status       TEXT    NOT NULL DEFAULT 'pending'
                         CHECK(status IN ('pending','confirmed','delivered','cancelled')),
    total_cents  INTEGER NOT NULL                              -- price in cents, never floats
);

Named/keyed resource (upsertable):

CREATE TABLE IF NOT EXISTS templates (
    name         TEXT PRIMARY KEY,                         -- human key, e.g. "friday-night"
    payload      TEXT NOT NULL,                            -- JSON: {store_id, items[], payment_ref}
    created_at   TEXT NOT NULL DEFAULT (datetime('now')),
    updated_at   TEXT NOT NULL DEFAULT (datetime('now')),
    deleted_at   TEXT DEFAULT NULL
);

Child table (foreign key):

CREATE TABLE IF NOT EXISTS order_items (
    id           INTEGER PRIMARY KEY AUTOINCREMENT,
    order_id     INTEGER NOT NULL REFERENCES orders(id) ON DELETE CASCADE,
    created_at   TEXT    NOT NULL DEFAULT (datetime('now')),
    updated_at   TEXT    NOT NULL DEFAULT (datetime('now')),
    product_code TEXT    NOT NULL,                         -- e.g. "S_PIZPH"
    quantity     INTEGER NOT NULL DEFAULT 1,
    unit_cents   INTEGER NOT NULL
);

Full-text search (FTS5 virtual table + sync triggers):

CREATE VIRTUAL TABLE IF NOT EXISTS orders_fts USING fts5(
    customer,
    product_code,
    content='orders',
    content_rowid='id'
);

CREATE TRIGGER orders_fts_ai AFTER INSERT ON orders BEGIN
    INSERT INTO orders_fts(rowid, customer, product_code)
    VALUES (new.id, new.customer, new.product_code);
END;

CREATE TRIGGER orders_fts_ad AFTER DELETE ON orders BEGIN
    INSERT INTO orders_fts(orders_fts, rowid, customer, product_code)
    VALUES ('delete', old.id, old.customer, old.product_code);
END;

Vector search (sqlite-vec, installed separately):

-- Requires: pip install sqlite-vec
CREATE VIRTUAL TABLE IF NOT EXISTS order_embeddings USING vec0(
    id      INTEGER PRIMARY KEY,
    vector  FLOAT[1536]
);

Index patterns

CREATE INDEX IF NOT EXISTS idx_orders_created_at   ON orders(created_at);
CREATE INDEX IF NOT EXISTS idx_orders_updated_at   ON orders(updated_at);
CREATE INDEX IF NOT EXISTS idx_orders_deleted_at   ON orders(deleted_at);   -- fast live-row filter
CREATE INDEX IF NOT EXISTS idx_orders_status       ON orders(status);
CREATE INDEX IF NOT EXISTS idx_orders_status_date  ON orders(status, created_at);
CREATE INDEX IF NOT EXISTS idx_order_items_order   ON order_items(order_id);

Rule of thumb: if a query filters or sorts by a column more than occasionally, it needs an index. deleted_at always gets an index — nearly every query filters on it.

Phase 3 — Migrations

Schema changes after the initial deploy go in migrations/, not back into schema.sql directly. This preserves history and lets init_db.py apply only the deltas that haven't run yet.

Migration file naming

migrations/
  001_initial.sql       ← copy of the original schema.sql at deploy time
  002_add_phone.sql     ← ALTER TABLE orders ADD COLUMN phone TEXT;
  003_add_notes.sql     ← ALTER TABLE orders ADD COLUMN notes TEXT DEFAULT '';

Each file is append-only — never edit a migration that has already run.

Running the sync

python3 init_db.py          # global scope
python3 scripts/init_db.py  # local scope

This does three things in one shot:

1. Creates the _migrations tracking table if it doesn't exist.
2. Applies pending migrations/*.sql files in order.
3. Rewrites the Schema Reference section of the generated SKILL.md with the live column structure.

Self-check after every migration — REQUIRED

init_db.py updates the DB and the Schema Reference. It does not update db_cli.py or schema.sql. After every migration you MUST do all of the following, then re-run init_db.py to rebake the skill copy.

Always — update schema.sql to match the new state:

schema.sql is the source of truth for the complete current schema. Every migration must be reflected back into it so the file always represents what a fresh deploy would look like.

• For every ALTER TABLE ... ADD COLUMN: add the column to the matching CREATE TABLE block in schema.sql
• For every column removed or renamed: update the CREATE TABLE block in schema.sql accordingly
• For every new table: add the full CREATE TABLE + triggers + indexes to schema.sql

Work through this checklist for db_cli.py:

For every new column added:

• cmd_insert_<entity> — add the column as an optional arg; include it in the INSERT cols/vals if present
• cmd_update_<entity> — add if "col" in args: fields["col"] = args["col"] and update the error message listing updatable fields
• cmd_list_<entity> — if the column is filterable (status, type, badge number), add a if col: conditions.append(...) branch
• cmd_get_<entity> — usually no change needed (uses SELECT *)
• Returned JSON in insert/update — include the new column in the ok({...}) response so callers see it

For every column removed or renamed:

• Remove or rename all references in insert, update, list, and any named-query commands
• Check bulk-import / bulk-export — they use column names from the caller; document the change

For every new table added:

• Add a full set of CRUD commands: insert-<entity>, get-<entity>, list-<entity>, update-<entity>
• Add the table to cmd_context if it needs a live/total row count
• Add the table name to the COMMANDS dispatch dict
• Update the ## Command Reference table in the generated SKILL.md

After updating db_cli.py, re-run init_db.py to copy the updated template into the skill folder:

python3 init_db.py   # rebakes ~/.claude/skills/<name>-db/db_cli.py

Then verify end-to-end with a quick smoke test:

python3 ~/.claude/skills/<name>-db/db_cli.py insert-<entity> '{"new_col":"value",...}'
python3 ~/.claude/skills/<name>-db/db_cli.py update-<entity> '{"id":1,"new_col":"value"}'

Phase 4 — db_cli.py

The full command surface lives in scripts/db_cli.py — bundled with this skill. Other skills call it as a subprocess and parse the JSON output. Every command outputs JSON on stdout; errors go to stderr with a non-zero exit code.

Agent call examples:

python3 scripts/db_cli.py context '{}'
python3 scripts/db_cli.py schema '{}'
python3 scripts/db_cli.py insert-order '{"customer":"aly","total_cents":1299}'
python3 scripts/db_cli.py recent-orders '{"limit":5,"status":"pending","since":"2026-01-01"}'
python3 scripts/db_cli.py fts-search '{"query":"pepperoni","limit":5}'
python3 scripts/db_cli.py save-template '{"name":"friday-night","payload":{"store":7144}}'
python3 scripts/db_cli.py get-template '{"name":"friday-night"}'
python3 scripts/db_cli.py update-order-status '{"order_id":3,"status":"delivered"}'
python3 scripts/db_cli.py soft-delete '{"table":"orders","record_id":3}'
python3 scripts/db_cli.py restore '{"table":"orders","record_id":3}'
python3 scripts/db_cli.py query '{"sql":"SELECT * FROM orders WHERE status = ?","params":["pending"]}'
python3 scripts/db_cli.py bulk-import '{"table":"orders","records":[...],"mode":"upsert"}'
python3 scripts/db_cli.py bulk-export '{"table":"orders","fmt":"csv","dest":"orders.csv"}'
python3 scripts/db_cli.py db-export '{}'

Command Reference

Interop commands (for other skills)

How another skill consumes this db:

# 1. Discover what's here
python3 db_cli.py context '{}'

# 2. Get full column detail for a specific table
python3 db_cli.py schema '{}' | python3 -c "import sys,json; s=json.load(sys.stdin); print(json.dumps(s['tables']['orders'], indent=2))"

# 3. Run a targeted read
python3 db_cli.py recent-orders '{"limit":10,"status":"pending"}'

Write commands

Read commands

Bulk commands

Schema Reference

Run python3 init_db.py once to populate this section with your live schema.

DB Path Convention

DB_PATH is set once in both init_db.py and db_cli.py — change it in one place during Phase 1 so it never drifts between the two files.

The Sync Loop

Edit schema.sql  (or add migrations/<N>_change.sql for existing dbs)
        ↓
python3 scripts/init_db.py
        ↓
  db/<name>.db updated
  .claude/skills/<name>-db/SKILL.md  ← Schema Reference section spliced
  .claude/skills/<name>-db/db_cli.py ← re-copied with absolute DB_PATH
        ↓
Agent loads <name>-db skill → knows exact tables/columns/indexes
        ↓
Other skills call:  python3 .claude/skills/<name>-db/db_cli.py context '{}'
Webapps connect to: db/<name>.db directly

No manual skill edits. No schema inspection at runtime. One command keeps everything in sync.

Phase 5 — The Generated Skill

After the first python3 scripts/init_db.py run, a self-contained skill is written to .claude/skills/<name>-db/ (or ~/.claude/skills/<name>-db/ for global scope).

What it contains:

• Frontmatter name and description tailored to the DB's entity names so it activates on phrases like "add a bug", "list orders", "search bugtracker"
• DB path and CLI reference (absolute, works from any directory)
• Schema Reference — the only section that changes after migrations
• Full command reference with entity-specific examples
• Webapp access snippets (Python / Node / Bun+Drizzle)
• Migration instructions

What gets re-generated on every init_db.py run:

• db_cli.py in the skill folder (in case source changed)
• The ## Schema Reference section in SKILL.md only — everything else is stable

What to set before generating:

• DB_SCOPE — local (default) or global
• DB_NAME — defaults to the current directory name; override if different
• Entity names in the description — init_db.py derives them from the first table name; edit the generated skill's frontmatter description if the auto-derived name is wrong (e.g. "goose" → "geese", "person" → "people")