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 — CLI install + skill install
• docs/core/guides/connect.md — full connection procedure, per-datasource setup notes, complete troubleshooting playbook
• 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 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.

3. Generate the .env template by introspecting the connector:

   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:

   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. 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:

datasource: <ds>
host: ${<DS>_HOST}
port: ${<DS>_PORT}
# … one line per field from `wren docs connection-info <ds>`

Then:

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 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

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

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:

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

On error

Don't carry an error playbook here — surface connect.md#troubleshooting 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."