onboarding

# Wren Onboarding — Agent Workflow This skill walks the agent through onboarding — environment checks, project scaffolding, profile creation, MDL generation, and first query. **Procedural details, per-datasource setup notes, and the troubleshooting playbook live in the docs**, not here. The skill's job is to enforce the agent-side rules (one step per turn, never ask for credentials in chat) and to dispatch the agent to the right doc / sibling skill at each step. Reference docs (the skill points to these — never duplicate their content): - [`docs/core/get_started/installation.md`](https://github.com/Canner/WrenAI/blob/main/docs/core/get_started/installation.md) — CLI install + skill install - [`docs/core/guides/connect.md`](https://github.com/Canner/WrenAI/blob/main/docs/core/guides/connect.md) — full connection procedure, **per-datasource setup notes, complete troubleshooting playbook** - [`docs/core/get_started/quickstart.md`](https://github.com/Canner/WrenAI/blob/main/docs/core/get_started/quickstart.md) — bundled `jaffle_shop` demo ## Mode of operation — READ THIS FIRST **One step per round-trip.** Each numbered step below is its own turn: explain briefly, ask **only** what the step needs, run the command(s), confirm, move on. - ❌ **Never collect information for future steps upfront.** Do not ask for project name + database type + credentials in one message. - ❌ **Never ask for credentials in chat — not host, port, user, password, tokens, anything.** Credentials always go through `.env`. The user fills the file in their editor; the agent never sees the values. - ❌ **Never query the database before MDL is built** via `wren skills get generate-mdl`. - ❌ **Never invent connection field names.** Always run `wren docs connection-info <ds>` to see the real fields — it's introspected from the live Pydantic schema, so it's always correct. - ✅ Wait for each command to finish, report its output in plain language, then move on. - ✅ For any error, consult `connect.md#troubleshooting` and surface the relevant section to the user — don't carry a copy of the playbook here. ## Preflight (environment only — no user questions about the project) Read-only checks. Report findings, do **not** ask about project / credentials / datasource yet. 1. `python3 --version` — requires Python 3.11+. If older, ask the user to upgrade and stop. 2. Check virtualenv: `python3 -c "import sys; print(sys.prefix != sys.base_prefix)"`. If `False`, offer to create one (`python3 -m venv .venv && source .venv/bin/activate`). PEP 668 systems will need this. 3. `wren --version` — if already installed, confirm before reinstalling. 4. `pwd` — record it. Don't ask where the project should live yet. Report findings as a 4-bullet list, then continue. ## Early branch — demo or own database? > "Try the bundled `jaffle_shop` demo first (~30s, no DB needed), or connect your own database?" - **demo** → point at [`quickstart.md`](https://github.com/Canner/WrenAI/blob/main/docs/core/get_started/quickstart.md) and stop this skill. - **own DB** → continue. ## Step 1 — Collect project name + database type These two are the only thing Step 2 needs; ask both together so the user has a clean handoff: > "Two things before I scaffold: > 1. **Project name** — I'll create `~/<name>/` and `cd` into it. > 2. **Database type** — run `wren docs connection-info` (no argument) to see the full list, or pick a common one: `postgres` (use for Aurora PostgreSQL), `mysql` (use for Aurora MySQL), `bigquery`, `snowflake`, `clickhouse`, `trino`, `duckdb`, …" Wait for both. Don't ask for credentials. ## Step 2 — Workspace + .env setup (batch) Side effects: creates `~/<project>/`, installs `wrenai[<ds>,main]`, generates an empty `.env` template. The project files (`wren_project.yml` etc.) come later in Step 3.5 — at this point we only have a directory with credentials waiting to be filled. Run as a batch — report each command briefly, then end with one "please fill `.env`" ask: 1. `mkdir -p ~/<project> && cd ~/<project>`. 2. `pip install "wrenai[<ds>,main]"`. For datasource-specific install gotchas (macOS mysql, etc.), see [`connect.md#per-datasource-setup-notes`](https://github.com/Canner/WrenAI/blob/main/docs/core/guides/connect.md). 3. **Generate the `.env` template by introspecting the connector**: ```bash wren docs connection-info <ds> --format md ``` Use the field list to write `.env` with `<DS>_<FIELD>=` keys (UPPER_SNAKE), values **empty**. Example for postgres: ```ini POSTGRES_HOST= POSTGRES_PORT=5432 POSTGRES_DATABASE= POSTGRES_USER= POSTGRES_PASSWORD= ``` Special encodings (BigQuery base64, Snowflake account format, Athena AWS creds, etc.) are documented in [`connect.md#per-datasource-setup-notes`](https://github.com/Canner/WrenAI/blob/main/docs/core/guides/connect.md). Surface the relevant section to the user verbatim — don't paraphrase. 4. Add `.env` to `.gitignore` if the project is a git repo. Suggest `chmod 600 .env`. 5. Tell the user: `.env` is at `<path>`, please fill every value and reply **"done"**. ## Step 3 — Create the connection profile Only after the user replies "done". Write `/tmp/conn.yml` with **every field as a `${VAR}` placeholder** matching the `.env` keys you generated in Step 2: ```yaml datasource: <ds> host: ${<DS>_HOST} port: ${<DS>_PORT} # … one line per field from `wren docs connection-info <ds>` ``` Then: ```bash wren profile add <project> --from-file /tmp/conn.yml ``` Validation runs automatically. The CLI overwrites profiles silently — there is no `--force` flag. - ✓ **Success** → continue to Step 3.5. - ⚠ **Any warning** → consult [`connect.md#troubleshooting`](https://github.com/Canner/WrenAI/blob/main/docs/core/guides/connect.md) for the exact symptom (missing secret, driver auth failure, ValidationError, unreachable host, …) and tell the user what to fix. ## Step 3.5 — Scaffold the project ```bash wren context init --empty ``` Refuses to overwrite an existing `wren_project.yml`. Creates the project directory layout (`models/`, `views/`, `relationships.yml`, `knowledge/` (rules + sql), `AGENTS.md`). ## Step 3.6 — Bind the profile to the project ```bash wren context set-profile <project> ``` Writes both `profile: <project>` and `data_source: <ds>` into `wren_project.yml` (data_source is taken from the profile we just validated, so it's guaranteed correct). Future CLI commands and the SDK resolve the connection deterministically — independent of which profile is globally active. This step also future-proofs the project for multi-project setups: once the binding is recorded, switching `wren profile switch` elsewhere never breaks this project's queries. ## Step 4 — Generate MDL (hand off) > ⚠️ The agent **must** build MDL before any data query. Queries against tables not in MDL will fail. Run `wren skills get generate-mdl` and follow it. It walks the agent through table introspection, type normalization, and YAML generation. When it finishes, return here and run: ```bash wren context validate wren context build ``` Report the model count and any validate warnings. **Memory recommendation**: count models with `wren context show | grep -c '^model:'`. If `>= 200`, suggest `pip install "wrenai[memory]"` + `wren memory index` (~800 MB). If `< 200`, skip. ## Step 5 — Ready to explore (hand off) Suggest 2–3 NL questions based on the discovered tables (e.g. for an orders schema: "How many orders last month?", "Top 5 customers by total"). Then end this skill: for day-to-day querying the agent should run `wren skills get usage`. ## Cross-skill routing | Trigger | Skill | |---------|-------| | User mentions a SaaS source (HubSpot, Stripe, Salesforce, GitHub, Slack, …) | `wren skills get dlt-connector` | | User has a connected DB but no MDL yet | `wren skills get generate-mdl` | | User has MDL ready, wants to query | `wren skills get usage` | | Anything else from-scratch | `wren skills get onboarding` (this skill) | ## On error Don't carry an error playbook here — surface [`connect.md#troubleshooting`](https://github.com/Canner/WrenAI/blob/main/docs/core/guides/connect.md) sections to the user. The doc covers: - `wren: command not found` - `pip install … externally-managed-environment` - Missing secret (`MissingSecretError`) - Driver authentication failures - Pydantic `ValidationError` / unknown datasource - Connection refused / firewall / cloud DB IP allow-list - `wren context validate` warning categories If you hit something not in the playbook, tell the user: > "I hit an error I don't know how to fix: `<error>`. > See <https://docs.getwren.ai/oss/introduction> or open an issue at <https://github.com/Canner/WrenAI/issues>."