setup
Skill0 starsChanged 43 days agoMITView on GitHub
Interactive setup wizard for the Debbie, Vera and Max data agents. It detects your data stack, asks where your pipeline repo lives, and writes data-agents.config.yaml at the project root. Run it once per project, or again to change settings.
- Pipes a download into a shell
191 other projects use a version of this.
Adopted by
Most recently taken up by
- hydra-db/hydradb-claude-codetheir own version, first seen Nov 8, 2026
- WALKERKILLER/Look-Tongji-Notestheir own version, first seen Nov 8, 2026
- steerlabs/opensteertheir own version, first seen Nov 8, 2026
- ansvisor/ansvisortheir own version, first seen Nov 8, 2026
- dsifry/metaswarmtheir own version, first seen Nov 8, 2026
- anombyte93/prd-taskmastertheir own version, first seen Nov 8, 2026
Used with
Projects with it also use add-voice-transcription, add-discord and 10 more.
Other files people use alongside this one.
Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
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Also in 7 projects that use this
- +8 more from microsoft
Other versions191 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 191
- anthropics/knowledge-work-plugins★ 35ksame name
- garrytan/gbrain★ 32ksame name
- gavrielc/nanoclaw★ 31ksame name
- nanocoai/nanoclaw★ 31ksame name
- qwibitai/nanoclaw★ 31ksame name
- Q00/ouroboros★ 6.3ksame name
- pytorch/executorch★ 5.2ksame name
- agenticnotetaking/arscontexta★ 3.5ksame name
- aws/agent-toolkit-for-aws★ 3.4ksame name
- spotify/portal-ai-plugins★ 2.8ksame name
181 more not listed
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository4 other files it gives its agents.
- data-qualitySkill
- ml-engineeringSkill
- pipeline-engineeringSkill
- squadSkill
The file
We split this skill into the rules it gives an agent
5 rules. 0 are common, 3 are distinctive, and 2 are very specific to this project.
0
Common
Most of the 171 edited versions we read keep these.
3
Distinctive
This author's own choices. Worth a look.
2
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 15
If $ARGUMENTS is a path, use it as repo.path.
- Very specific: line 18
If a data-agents.config.yaml already exists, read it, show the current values in a short table, and ask what to change.
- Distinctive: line 58
Use the AskUserQuestion tool if it's available, and keep it to 4 questions or fewer.
- Distinctive: line 62
Start from the template, using the first of these that exists:
- Distinctive: line 91
Use real table or file names you found during detection in the examples.
“Common” is measured: kept by at least 6 in 10 of the 171 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Data Agents — Setup Wizard
Goal: produce a correct data-agents.config.yaml at the root of the current project so Debbie, Vera and Max
know where the code lives and how this team works. Be quick and friendly. Detect as much as you can, and ask only what you can't infer.
Step 1 — Where is the repo?
- If
$ARGUMENTSis a path, use it asrepo.path. - Otherwise ask: "Where does your data platform code live? Paste a path, or say 'here' for this repo." Accept relative or absolute paths. Check that the path exists before continuing.
- If a
data-agents.config.yamlalready exists, read it, show the current values in a short table, and ask what to change. Don't start over.
Step 2 — Detect the stack (read-only)
Scan repo.path (skip .git, node_modules, .venv, venv) and infer values from these signals:
| Signal in the repo | Infer |
databricks.yml, resources/*.yml, # Databricks notebook source, dbutils. | compute: databricks, catalog: unity-catalog if 3-part names |
pipeline/*.json + trigger/, linkedService/, factory/ | orchestrator: adf, cloud: azure |
dags/*.py with from airflow | orchestrator: airflow (cloud-composer if GCP signals) |
glue jobs, awsglue imports, serverless.yml, CDK or Terraform aws_glue_job | compute: aws-glue, cloud: aws, storage: s3 |
emr / EmrServerless in IaC | compute: aws-emr |
google.cloud, dataproc, bigquery imports | cloud: gcp, compute: gcp-dataproc or bigquery |
dbt_project.yml | transform_language: dbt, and data_quality.engine: dbt-tests is a candidate |
great_expectations/, gx/ | data_quality.engine: great-expectations |
checks.yml / soda imports | data_quality.engine: soda |
@dlt.expect / @dp.expect | data_quality.engine: dlt-expectations |
.format("delta"), DeltaTable, MERGE INTO | table_format: delta |
iceberg catalog config | table_format: iceberg |
abfss:// | storage: adls |
s3:// | storage: s3 |
gs:// | storage: gcs |
dbutils.secrets.get | secrets: dbutils.secrets |
mlflow. imports | ml.tracking: mlflow; models:/ or 3-part registered names mean the registry is unity-catalog or mlflow-workspace |
utils/, common/, lib/ folders imported by many notebooks | repo.shared_utils |
.ipynb vs .py with # COMMAND ---------- | conventions.notebook_format |
*_audit*, pipeline_log table names in code | conventions.audit_table |
README, docs/, ARCHITECTURE*, adr/ | context_files |
Also list the top-level folders with a one-line guess of each one's purpose. They become code_dirs candidates.
Step 3 — Confirm in one go
Show a compact table: setting | detected value | evidence (file). Then ask, in a single question, only for what's still missing:
- Folders that must never be edited (handed-over or vendored code)?
- Folders agents may write new work into?
- Who approves plans? (default: "the user")
- Any architecture docs they should always read?
Use the AskUserQuestion tool if it's available, and keep it to 4 questions or fewer.
Step 4 — Write the config
Start from the template, using the first of these that exists:
${CLAUDE_PLUGIN_ROOT}/templates/data-agents.config.yaml(plugin install).claude/skills/setup/data-agents.config.yaml(script install)- otherwise recreate the structure: sections
project, repo, platform, conventions, data_quality, ml, workflow, context_files. Fill in the confirmed values, leave unknowns as""/[], and keep the comments. Write it to./data-agents.config.yaml.
Step 5 — Recommend power-ups (don't install without a yes)
Based on the detected stack, suggest the matching external skills. Agents get sharper when these are present:
| Stack | Suggest |
| databricks | Databricks AI Dev Kit skills: curl -sSL https://raw.githubusercontent.com/databricks-solutions/ai-dev-kit/main/databricks-skills/install_skills.sh | bash (run from the project root; installs into .claude/skills/) |
| dbt | /plugin marketplace add dbt-labs/dbt-agent-skills then /plugin install dbt@dbt-agent-marketplace |
| mlflow (agents / GenAI tracing) | git clone https://github.com/mlflow/skills and copy the skill folders into .claude/skills/ |
Step 6 — Finish
Print:
🎉 Squad ready for <project.name or folder name>
Repo: <repo.path> Stack: <compute> + <orchestrator> + <table_format>
Try:
"Debbie, build an incremental silver table for <a real table you found>"
"Vera, profile <a real table> and propose DQ rules"
"Max, frame a baseline model for <a plausible target>"Use real table or file names you found during detection in the examples.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
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People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
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- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

