# Skill: Connect Data

## Purpose
Guided wizard to connect a new dataset. Walks the user through selecting
a connection type, configuring credentials, validating the connection,
profiling the schema, and setting up the knowledge brain.

## When to Use
- User says `/connect-data` or "connect my database" or "add a new dataset"
- First-run welcome suggests connecting data
- After `/switch-dataset` when the target dataset doesn't exist yet

## Invocation
`/connect-data` — start the connection wizard
`/connect-data type=postgres` — skip type selection

## Instructions

### Step 1: Choose Connection Type
Present options:
1. **CSV files** — "I have CSV files in a local directory"
2. **DuckDB** — "I have a local DuckDB database file"
3. **MotherDuck** — "I have a MotherDuck cloud database"
4. **PostgreSQL** — "I have a PostgreSQL database"
5. **BigQuery** — "I have a Google BigQuery dataset"
6. **Snowflake** — "I have a Snowflake warehouse"

### Step 2: Collect Connection Details

**For CSV:**
- Ask: "What's the path to your CSV directory? (relative to this repo)"
- Verify the directory exists and contains .csv files
- List found files and ask to confirm

**For DuckDB:**
- Ask: "Path to your .duckdb file?"
- Verify file exists
- Test connection with `SELECT 1`

**For MotherDuck:**
- Ask: "Database name and schema?"
- Note: "MotherDuck connects via MCP. Make sure your token is configured."

**For PostgreSQL / BigQuery / Snowflake:**
- Copy the appropriate template from `connection_templates/`
- Ask user to fill in required fields
- **IMPORTANT:** Never ask for or store passwords directly. Guide the user
  to use environment variables (e.g., `$PG_PASSWORD`).

### Step 3: Create Dataset Brain
1. Generate a dataset_id from the display name (lowercase, hyphens)
2. Create `.knowledge/datasets/{id}/` directory
3. Write `manifest.yaml` from the connection template + user inputs
4. Create empty `quirks.md` with section headers
5. Create empty `metrics/index.yaml`

### Step 4: Test Connection
Use `ConnectionManager` from `helpers/connection_manager.py`:
1. Instantiate with the new config
2. Call `test_connection()`
3. If fails: show error, offer to retry or edit config
4. If passes: proceed

### Step 5: Profile Schema
1. Call `list_tables()` to enumerate tables
2. For each table: get column names and types via `get_table_schema()`
3. Generate `schema.md` using `schema_to_markdown()` from `helpers/data_helpers.py`
4. Write to `.knowledge/datasets/{id}/schema.md`
5. Offer to run full data profiling: "Want me to deep-profile this dataset?"

### Step 6: Set Active
1. Update `.knowledge/active.yaml` to point to the new dataset
2. Confirm: "Connected! **{display_name}** is now your active dataset."
3. Show: table count, estimated row count, date range (if detected)
4. Suggest next steps: `/explore` to browse, `/metrics` to define metrics,
   or just ask a question

## Rules
1. Never store credentials in plain text in manifest files
2. Always test the connection before declaring success
3. Always generate a schema.md — it's required for analysis
4. Create the full .knowledge/datasets/{id}/ tree even if profiling fails
5. If the user already has this dataset, ask before overwriting

## Edge Cases
- **Directory doesn't exist:** Offer to create it
- **No CSV files found:** Check for other formats (.parquet, .json)
- **Connection fails repeatedly:** Suggest checking credentials, firewall, VPN
- **Schema too large (>100 tables):** Profile only, skip per-table details
- **Dataset name collision:** Append a number (e.g., "mydata-2")