Wren Engine CLI — Agent Workflow Guide

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key with this skill's version (from the frontmatter above). If the remote version is newer, notify the user before proceeding:

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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:

Tell the user that the
```
wren
```
CLI is not installed.
Ask if you should help install it.
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.

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, interactive prompts, and web UI (recommended):

bash

pip install "wren-engine[<datasource>,main]"
# or for DuckDB:
pip install "wren-engine[main]"

Verify:
```
wren --version
```

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:

Profile — database connection + datasource type, managed via
```
wren profile
```
(stored in
```
~/.wren/profiles.yml
```
)
Project — MDL model definitions in YAML, compiled to
```
target/mdl.json
```
via
```
wren context build
```

The CLI reads the active profile for connection info and datasource. Use

wren profile list

to see which profile is active,

wren profile switch <name>

to change it.

dry-plan

also accepts

--datasource

-d

for transpile-only use without a profile.

For memory-specific decisions, see references/memory.md. For SQL syntax, CTE-based modeling, and error diagnosis, see references/wren-sql.md. For project structure, MDL field definitions, and CLI workflow details, see the documentation.

Workflow 1: Answering a data question

Step 1 — Gather context

Situation	Command
Default	`wren memory fetch -q "<question>"`
Need specific model's columns	`wren memory fetch -q "..." --model <name> --threshold 0`
Memory not installed	Read `target/mdl.json` in the project directory, or run `wren context show`

If this is the first query in the conversation, also run:

text

wren context instructions

If it returns content, treat it as rules that override defaults — apply them to all subsequent queries in this session.

Step 2 — Recall past queries

bash

wren memory recall -q "<question>" --limit 3

Use results as few-shot examples. Skip if empty.

Step 2.5 — Assess complexity (before writing SQL)

If the question involves any of the following, consider decomposing:

Multiple metrics or aggregations (e.g., "churn rate AND expansion revenue")
Multi-step calculations (e.g., "month-over-month growth rate")
Comparisons across segments (e.g., "by plan tier, by region")
Time-series analysis requiring baseline + change (e.g., "retention curve")

Decomposition strategy:

Identify the sub-questions (e.g., "total subscribers at start" + "subscribers who cancelled" → churn rate)
For each sub-question:
- ```
wren memory recall -q "<sub-question>"
```
  — check if a similar pattern exists
- Write and execute a simple SQL
- Note the result
Combine sub-results to answer the original question

When NOT to decompose:

Single-table aggregation with GROUP BY — just write the SQL
Simple JOINs that the MDL relationships already define
Questions where
```
memory recall
```
returns a near-exact match

This is a judgment call, not a rigid rule. If you're confident in a single query, go ahead. Decompose when the SQL would be hard to debug if it fails.

Step 3 — Write, verify, and execute SQL

For simple queries (single table or simple MDL-defined JOINs, straightforward aggregation): Execute directly:

bash

wren --sql 'SELECT c_name, SUM(o_totalprice) FROM orders
JOIN customer ON orders.o_custkey = customer.c_custkey
GROUP BY 1 ORDER BY 2 DESC LIMIT 5'

For complex queries (non-trivial JOINs not covered by MDL relationships, subqueries, multi-step logic): Verify first with dry-plan:

bash

wren dry-plan --sql 'SELECT ...'

Check the expanded SQL output:

Are the correct models and columns referenced?
Do the JOINs match expected relationships?
Are CTEs expanded correctly?

If the expanded SQL looks wrong, fix before executing. If it looks correct, proceed:

bash

wren --sql 'SELECT ...'

SQL rules:

Target MDL model names, not database tables
Write dialect-neutral SQL — the engine translates

Step 4 — Store and continue

After successful execution, store the query by default:

bash

wren memory store --nl "<user's original question>" --sql "<the SQL>"

Skip storing only when:

The query failed or returned an error
The user said the result is wrong
The query is exploratory (
```
SELECT * ... LIMIT N
```
without analytical clauses)
There is no natural language question — just raw SQL
The user explicitly asked not to store

The CLI auto-detects exploratory queries — if you see no store hint after execution, the query was classified as exploratory.

Outcome	Action
User confirms correct	Store
User continues with follow-up	Store, then handle follow-up
User says nothing (but question had clear NL description)	Store
User says wrong	Do NOT store — fix the SQL
Query error	See Error recovery below

Workflow 2: Error recovery

"table not found"

Verify model name:

wren memory fetch -q "<name>" --type model --threshold 0

Check MDL exists:
```
ls target/mdl.json
```
(or
```
wren context show
```
)

Verify column:

wren memory fetch -q "<column>" --model <name> --threshold 0

Connection error

Check active profile:
```
wren profile debug
```
Verify datasource and connection fields are correct
Test:
```
wren --sql "SELECT 1"
```

Valid datasource values:

postgres

mysql

bigquery

snowflake

clickhouse

trino

mssql

databricks

redshift

spark

athena

oracle

duckdb

If no profile exists, create one:

wren profile add --ui

(or

--interactive

--from-file

)

SQL syntax / planning error (enhanced)

Layer 1: Identify the failure point

bash

wren dry-plan --sql "<failed SQL>"

dry-plan result	Failure layer	Next step
dry-plan fails	MDL / semantic	→ Layer 2A
dry-plan succeeds, execution fails	DB / dialect	→ Layer 2B

Layer 2A: MDL-level diagnosis (dry-plan failed)

The dry-plan error message tells you exactly what's wrong:

Error pattern	Diagnosis	Fix
`column 'X' not found in model 'Y'`	Wrong column name	`wren memory fetch -q "X" --model Y --threshold 0` to find correct name
`model 'X' not found`	Wrong model name	`wren memory fetch -q "X" --type model --threshold 0`
`ambiguous column 'X'`	Column exists in multiple models	Qualify with model name: `ModelName.column`
Planning error with JOIN	Relationship not defined in MDL	Check available relationships in context

Key principle: Fix ONE issue at a time. Re-run dry-plan after each fix to see if new errors surface.

Layer 2B: DB-level diagnosis (dry-plan OK, execution failed)

The DB error + dry-plan output together pinpoint the issue:

Read the dry-plan expanded SQL — this is what actually runs on the DB
Compare with the DB error message:

Error pattern	Diagnosis	Fix
Type mismatch	Column type differs from assumed	Check column type in context, add explicit CAST
Function not supported	Dialect-specific function	Use dialect-neutral alternative
Permission denied	Table/schema access	Check connection credentials
Timeout	Query too expensive	Simplify: reduce JOINs, add filters, LIMIT

For small models: If the error message is unclear, try simplifying the query to the smallest failing fragment. Execute subqueries independently to isolate which part fails.

For the CTE rewrite pipeline and additional error patterns, see references/wren-sql.md.

Workflow 3: Connecting a new data source

Add a profile:

wren profile add --ui

(or

--interactive

--from-file

)

Test connection:
```
wren profile debug
```
Test query:
```
wren --sql "SELECT 1"
```
Initialize project:
```
wren context init
```
Build manifest:
```
wren context build
```
Index:
```
wren memory index
```

Verify:

wren --sql "SELECT * FROM <model> LIMIT 5"

Workflow 4: After MDL changes

When model YAML files are updated, rebuild and re-index:

bash

# 1. Validate changes
wren context validate

# 2. Rebuild manifest
wren context build

# 3. Re-index schema memory
wren memory index

# 4. Verify
wren --sql "SELECT * FROM <changed_model> LIMIT 1"

Command decision tree

text

Get data back           → wren --sql "..."
Aggregation across dims → wren cube query --cube <name> --measures <m> (if cube defined)
See translated SQL only → wren dry-plan --sql "..." (accepts -d <datasource> if no active profile)
Validate against DB     → wren dry-run --sql "..."
Schema context          → wren memory fetch -q "..."
Filter by type/model    → wren memory fetch -q "..." --type T --model M --threshold 0
Store confirmed query   → wren memory store --nl "..." --sql "..."
Few-shot examples       → wren memory recall -q "..."
Index stats             → wren memory status
Re-index after MDL change → wren memory index
Show project context    → wren context show
Rebuild manifest        → wren context build
Check profile           → wren profile debug
Switch profile          → wren profile switch <name>

Cube Query Workflow

When the user asks an aggregation question (e.g., "total revenue by month", "top customers"), check if the MDL defines cubes before writing raw SQL.

Step 1: Discover cubes

bash

wren cube list

If cubes exist and cover the user's question, prefer cube query over raw SQL. Lower error rate, especially for small / local models — agents don't have to hand-write GROUP BY / DATE_TRUNC.

Step 2: Inspect cube structure

bash

wren cube describe <cube_name>

Shows the cube's baseObject, measures (with expressions), dimensions, time dimensions, and hierarchies.

Step 3: Match user's question to cube measures + dimensions

User phrase	Maps to
"total revenue"	`--measures revenue`
"by month"	`--time-dimension "created_at:month"`
"in 2024"	`--time-dimension "created_at:month:2024-01-01,2025-01-01"`
"for completed orders"	`--filter "status:eq:completed"`
"top N customers"	`--dimensions customer --limit N`

Step 4: Execute via CLI flags OR JSON input

CLI flags:

bash

wren cube query \
  --cube order_metrics \
  --measures revenue,order_count \
  --time-dimension "created_at:month:2024-01-01,2025-01-01" \
  --filter "status:eq:completed" \
  --limit 100

JSON input (good for agent-generated structured queries):

bash

echo '{"cube":"order_metrics","measures":["revenue"]}' | wren cube query --from -

Add

--sql-only

to print the generated SQL without executing — useful for verification before paying for execution on a remote warehouse.

Step 5: Error recovery

Error	Action
`Unknown measure 'X'`	`wren cube describe <cube>` for available measures
`Unknown dimension 'X'`	`wren cube describe <cube>` for available dimensions
`Cube 'X' not found`	`wren cube list`
`Circular dependency detected`	Derived measure references itself — inspect the cube YAML

When NOT to use cube query

Fall back to

wren --sql

when:

Custom JOINs across multiple models
Window functions, CTEs, or subqueries
Queries with no aggregation
No cubes defined in the MDL

Aggregation decision tree

text

User question → Is it an aggregation question?
                (SUM, COUNT, AVG, GROUP BY, "by month", "per customer", ...)
  ├── Yes → Are cubes defined? (`wren cube list` once at start of session)
  │         ├── Yes → Does a cube cover the question? (`wren cube describe`)
  │         │         ├── Yes → Use `wren cube query` (preferred — lower error rate)
  │         │         └── No  → Write raw SQL with `wren --sql`
  │         └── No  → Write raw SQL with `wren --sql`
  └── No  → Write raw SQL with `wren --sql` (look for memory recall first)

Things to avoid

Do not guess model or column names — check context first
Do not store failed queries or queries the user said are wrong
Do not skip storing successful queries with a clear NL question — default is to store
Do not re-index before every query — once per MDL change
Do not pass passwords via
```
--connection-info
```
if shell history is shared — use profiles (
```
wren profile add
```
) or
```
--connection-file
```

wren-usage

NPX Install

Tags

SKILL.md Content

Wren Engine CLI — Agent Workflow Guide

Version check

Preflight — Verify environment and installation

Step 1 — Check Python virtual environment

Step 2 — Check if
`wren-engine`
is installed

Workflow 1: Answering a data question

Step 1 — Gather context

Step 2 — Recall past queries

Step 2.5 — Assess complexity (before writing SQL)

Step 3 — Write, verify, and execute SQL

Step 4 — Store and continue

Workflow 2: Error recovery

"table not found"

Connection error

SQL syntax / planning error (enhanced)

Layer 1: Identify the failure point

Layer 2A: MDL-level diagnosis (dry-plan failed)

Layer 2B: DB-level diagnosis (dry-plan OK, execution failed)

Workflow 3: Connecting a new data source

Workflow 4: After MDL changes

Command decision tree

Cube Query Workflow

Step 1: Discover cubes

Step 2: Inspect cube structure

Step 3: Match user's question to cube measures + dimensions

Step 4: Execute via CLI flags OR JSON input

Step 5: Error recovery

When NOT to use cube query

Aggregation decision tree

Things to avoid