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feat(skills): detect existing project in wren-generate-mdl skill (#1525)

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Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
Jax Liu
2026-04-08 11:45:40 +08:00
committed by GitHub
parent 72c4917502
commit 5e37bfbe0b
6 changed files with 95 additions and 16 deletions
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docs/core/quickstart.md
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@@ -81,14 +81,16 @@ wren version
## Step 3 — Install CLI skills
Skills are workflow guides that tell Claude Code how to use the Wren CLI effectively. Install both skills:
Skills are workflow guides that tell your AI coding agent how to use the Wren CLI effectively. Install both skills:
```bash
npx skills add Canner/wren-engine --skill '*' --agent claude-code
npx skills add Canner/wren-engine --skill '*'
# or:
curl -fsSL https://raw.githubusercontent.com/Canner/wren-engine/main/skills/install.sh | bash
```
The CLI auto-detects your installed agent. To target a specific one, add `--agent <name>` (e.g., `claude-code`, `cursor`, `windsurf`, `cline`).
This installs two skills:
| Skill | Purpose |
skills/README.md
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@@ -21,12 +21,12 @@ Skills are namespaced as `/wren:<skill>` (e.g., `/wren:wren-generate-mdl`, `/wre
### Option 2 — npx skills
Install all skills for Claude Code:
Install all skills:
```bash
npx skills add Canner/wren-engine --skill '*' --agent claude-code
npx skills add Canner/wren-engine --skill '*'
```
`npx skills` also supports Cursor, Windsurf, and 30+ other agent tools — replace `--agent claude-code` with your agent of choice.
The CLI auto-detects your installed agent. To target a specific one, add `--agent <name>` (e.g., `claude-code`, `cursor`, `windsurf`, `cline`).
### Option 3 — install script (from a local clone)
@@ -71,7 +71,7 @@ Each skill automatically checks for updates when invoked. To update manually:
```bash
# Re-add to reinstall the latest version
npx skills add Canner/wren-engine --skill '*' --agent claude-code
npx skills add Canner/wren-engine --skill '*'
# Or reinstall from a local clone
bash skills/install.sh --force
skills/index.json
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@@ -7,7 +7,7 @@
"skills": [
{
"name": "wren-generate-mdl",
"version": "2.0",
"version": "2.1",
"description": "Generate a Wren MDL project by exploring a database with available tools (SQLAlchemy, database drivers, MCP connectors, or raw SQL). Guides agents through schema discovery, type normalization, and MDL YAML generation using the wren CLI.",
"tags": [
"wren",
@@ -26,8 +26,8 @@
},
{
"name": "wren-usage",
"version": "2.0",
"description": "Wren Engine CLI workflow guide for AI agents. Answer data questions end-to-end using the wren CLI: gather schema context, recall past queries, write SQL through the MDL semantic layer, execute, and learn from confirmed results.",
"version": "2.1",
"description": "Wren Engine CLI workflow guide for AI agents. Triggers on data questions, reports, metrics, revenue, trends, 'how many', 'show me', 'top N', 'compare', 'breakdown'. Answer data questions end-to-end using the wren CLI.",
"tags": [
"wren",
"usage",
skills/versions.json
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@@ -1,4 +1,4 @@
{
"wren-generate-mdl": "2.0",
"wren-usage": "2.0"
"wren-generate-mdl": "2.1",
"wren-usage": "2.1"
}
skills/wren-generate-mdl/SKILL.md
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@@ -4,7 +4,7 @@ description: "Generate a Wren MDL project by exploring a database with available
license: Apache-2.0
metadata:
author: wren-engine
version: "2.0"
version: "2.1"
---
# Generate Wren MDL — CLI Agent Workflow
@@ -18,8 +18,9 @@ If the remote version is newer, notify the user before proceeding:
> A newer version of the **wren-generate-mdl** skill is available.
> Update with:
> ```
> npx skills add Canner/wren-engine --skill wren-generate-mdl --agent claude-code
> npx skills add Canner/wren-engine --skill wren-generate-mdl
> ```
> The CLI auto-detects your installed agent. To target a specific one, add `--agent <name>` (e.g., `claude-code`, `cursor`, `windsurf`, `cline`).
Then continue with the workflow below regardless of update status.
@@ -42,6 +43,27 @@ For memory and query workflows after setup, see the **wren-usage** skill.
---
## Phase 0 — Detect existing project
**Goal:** If the current directory is already inside a wren project, let the user decide how to proceed.
Check whether `wren_project.yml` exists in the current working directory
(or any parent up to the repository root). If found:
1. Tell the user that an existing wren project was detected and show its path.
2. Ask:
- **Reset** — wipe the existing project (`models/`, `views/`,
`relationships.yml`, `instructions.md`, and rebuild `wren_project.yml`)
and regenerate from scratch in the same directory.
- **New path** — keep the existing project untouched and choose a
different directory for the new project. Ask the user for the new path,
then `wren context init --path <new_path>` and continue from Phase 1
using that path.
If no existing project is detected, proceed directly to Phase 1.
---
## Phase 1 — Establish connection and scope
**Goal:** Confirm the agent can reach the database and agree on scope with the user.
skills/wren-usage/SKILL.md
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@@ -1,10 +1,10 @@
---
name: wren-usage
description: "Wren Engine CLI workflow guide for AI agents. Answer data questions end-to-end using the wren CLI: gather schema context, recall past queries, write SQL through the MDL semantic layer, execute, and learn from confirmed results. Use when: agent needs to query data, connect a data source, handle errors, or manage MDL changes via the wren CLI."
description: "Wren Engine CLI workflow guide for AI agents. Answer data questions end-to-end using the wren CLI: gather schema context, recall past queries, write SQL through the MDL semantic layer, execute, and learn from confirmed results. Use when: user asks a data question, requests a report or analysis, asks about metrics, revenue, customers, orders, trends, or any business data; user says 'how many', 'show me', 'what is the', 'top N', 'compare', 'trend', 'growth', 'breakdown'; user wants to explore, analyze, filter, aggregate, or summarize data from a database; agent needs to query data, connect a data source, handle errors, or manage MDL changes via the wren CLI."
license: Apache-2.0
metadata:
author: wren-engine
version: "2.0"
version: "2.1"
---
# Wren Engine CLI — Agent Workflow Guide
@@ -18,13 +18,68 @@ If the remote version is newer, notify the user before proceeding:
> A newer version of the **wren-usage** skill is available.
> Update with:
> ```
> npx skills add Canner/wren-engine --skill wren-usage --agent claude-code
> npx skills add Canner/wren-engine --skill wren-usage
> ```
> The CLI auto-detects your installed agent. To target a specific one, add `--agent <name>` (e.g., `claude-code`, `cursor`, `windsurf`, `cline`).
Then continue with the workflow below regardless of update status.
---
## Preflight — Verify environment and installation
**Goal:** Ensure the `wren` CLI is available before entering any workflow.
### Step 1 — Check Python virtual environment
Run `python -c "import sys; print(sys.prefix)"` (or equivalent) to determine
whether a virtual environment is active.
- If **no venv is active**, warn the user and ask whether to:
- Create one (e.g., `python -m venv .venv && source .venv/bin/activate`)
- Continue without a venv (not recommended — may pollute global packages)
### Step 2 — Check if `wren-engine` is installed
Run `wren --version`. If the command is not found or errors:
1. Tell the user that the `wren` CLI is not installed.
2. Ask if you should help install it.
3. If the user agrees, determine the **datasource extra** to install:
**Auto-detect from project:** Check whether the current directory is inside
a wren project (look for `wren_project.yml` up to the repository root).
If found, read the active profile with `cat ~/.wren/profiles.yml` or look
for a datasource hint in the project's profile configuration. Extract the
datasource type from there.
**Ask the user:** If no project is detected or no datasource can be
inferred, ask the user which database they plan to connect to. Valid
extras: `postgres`, `mysql`, `bigquery`, `snowflake`, `clickhouse`,
`trino`, `mssql`, `databricks`, `redshift`, `spark`, `athena`, `oracle`.
DuckDB is included by default — no extra needed.
4. Install with the detected or chosen extra:
```bash
# DuckDB (no extra needed)
pip install "wren-engine"
# Other datasources
pip install "wren-engine[<datasource>]"
```
To also enable semantic memory and web UI (recommended):
```bash
pip install "wren-engine[<datasource>,memory,ui]"
# or for DuckDB:
pip install "wren-engine[memory,ui]"
```
5. Verify: `wren --version`
If `wren --version` succeeds, proceed to the relevant workflow below.
---
The `wren` CLI queries databases through an MDL (Model Definition Language) semantic layer. You write SQL against model names, not raw tables. The engine translates to the target dialect.
Two things drive everything: