annotate-sources

name	annotate-sources
description	Annotate dlt pipeline sources for transformation. Use when the user wants to transform data, do data modelling, design a data model, describes their data sources and use cases, or wants to build a CDM from existing pipelines.
argument-hint	[sources] [use-cases]

Annotate sources

Map the user's data sources to canonical business concepts, ready for ontology and CDM design.

Parse $ARGUMENTS:

• source names: comma-separated pipeline or source names (e.g. "hubspot, luma, stripe")
• use cases: what the user wants to do with the data (e.g. "track event attendance, link contacts to companies")

If not provided in arguments, ask the user for:

1. Which data sources / dlt pipelines they have
2. What they want to achieve (use cases, analytics goals, reports)
3. How the sources relate to each other (important)

IMPORTANT: Confirm the exact pipeline name (or dataset name + destination) for every source before doing anything else. Do not proceed to any extraction step until all names are known. Wrong pipeline names will cause all subsequent MCP calls to fail silently or with confusing errors.

All .schema/ files are written under <project_root>/.schema/<cdm-name>/. The CDM folder name is derived from the user's use cases and confirmed in step 3 below.

Steps

1. Check dlt pipelines exist

Use list_pipelines MCP tool to list all local dlt pipelines.

For each source the user mentioned, one of three cases applies:

Case A — local pipeline found → note the pipeline name, dataset name, and destination. Schema will be extracted via export_schema in step 2.

Case B — no local pipeline, but data already exists on a remote destination → ask the user for:

• The exact dataset name on the destination (e.g. luma_events_data)
• The destination type (e.g. bigquery, snowflake)

Schema will be extracted via a dlt ibis script in step 2. Do NOT hand off to rest-api-pipeline — the data is already there.

Case C — no pipeline and no remote dataset → stop and hand over to rest-api-pipeline toolkit:

Pipeline for "<source>" not found locally or remotely.
You need to ingest it first — use the rest-api-pipeline toolkit to build a dlt pipeline for it.

Only continue when all stated sources are confirmed as Case A or Case B.

2. Confirm CDM folder name

Derive a folder name from the user's stated use cases using the same grain-based naming convention as dataset_name in create-transformation — what the data mart is about (e.g. person_interactions, order_fulfillment, event_attendance). Never use source system names or generic names.

Propose the name and confirm with the user:

I'll store all schema files under .schema/person_interactions/

Does this name work, or would you like to change it?

Wait for confirmation. This name will also be used as the dataset_name when the transformation script is written — so it's worth getting right now.

3. Extract source schemas

For Case A (local pipeline): call export_schema MCP tool with output_format: "dbml" and save_to_file: "<project_root>/.schema/<cdm-name>/<pipeline_name>.dbml".

For Case B (remote dataset, no local pipeline): write and run a Python script using dlt ibis to extract the schema and write it as DBML.

Write the script to tools/get_<source>_schema.py:

"""Get <source> schema from <destination> via dlt ibis and write as DBML."""
import dlt

pipeline = dlt.pipeline(
    pipeline_name="<pipeline_name>",   # use the dataset name as pipeline name
    destination="<destination>",        # e.g. "bigquery"
    dataset_name="<dataset_name>",      # e.g. "luma_events_data"
)

dataset = pipeline.dataset()
ibis_conn = dataset.ibis()
tables = ibis_conn.list_tables()

lines = []
for table_name in tables:
    if table_name.startswith("_dlt"):
        continue
    t = ibis_conn.table(table_name)
    lines.append(f'Table "{table_name}" {{')
    for name, dtype in zip(t.schema().names, t.schema().types):
        nullable = "" if str(dtype).endswith("!") else ""
        lines.append(f'    "{name}" {dtype}')
    lines.append("}")
    lines.append("")

dbml = "\n".join(lines)
output_path = "<project_root>/.schema/<cdm-name>/<pipeline_name>.dbml"
with open(output_path, "w") as f:
    f.write(dbml)
print(f"Schema written to {output_path}")
print("Tables found:", tables)

Run with uv run python tools/get_<source>_schema.py. Confirm the file was written before proceeding.

This produces one DBML file per pipeline. These files are the working artifacts for all subsequent steps — they will be annotated in place as mappings and natural keys are confirmed.

4. Identify core business entities

Read the use cases the user stated. Using the source schemas and stated use cases only:

SCOPE CONSTRAINT — no inference beyond source data: Entity names, descriptions, and use-case coverage must be grounded strictly in (a) columns that actually exist in the source schemas and (b) use cases the user explicitly stated. Do not add, suggest, or imply attributes, metrics, or business concepts that have no corresponding column in the source data. For example: if the source has a contacts table but no roi, lead_score, or is_icp columns, do not mention or include those concepts anywhere — not in descriptions, not in assumptions, not as "could be added later". Only record what the data actually contains.

1. Propose the core business entities the use cases revolve around.

   • Collapse synonyms: guest → Person, contact → Person, attendee → Person
   • Use neutral, domain-agnostic names (PascalCase nouns): Person, Company, Event, Order
   • Explain each entity and why it covers the stated use cases — based only on columns present in the source schema

2. Present the proposed entities to the user and confirm:

Here are the core business entities I see in your data:

  Person — any individual (contact, guest, attendee, lead)
    Covers: track event attendance, link contacts to companies

  Company — an organisation
    Covers: link contacts to companies

Does this look right? You can rename, merge, or add anything.

• Wait for explicit confirmation before proceeding

3. Write .schema/<cdm-name>/taxonomy.json with the confirmed concepts.

Format: top-level keys are canonical concept names (PascalCase). Each concept holds its references (source-system synonyms) and all related metadata. Excluded tables, version, and CDM name are stored under reserved _excluded, _version, and _name keys.

{
  "_version": "1.0",
  "_name": "person_interactions",
  "Person": {
    "description": "Any individual — contact, guest, attendee, or lead",
    "use_cases": ["track event attendance", "link contacts to companies"],
    "references": ["guest", "contact", "attendee"],
    "tables": [],
    "natural_key": null,
    "assumptions": ["'guest' and 'contact' collapsed into Person"]
  },
  "Company": {
    "description": "An organisation",
    "use_cases": ["link contacts to companies"],
    "references": ["organization", "account"],
    "tables": [],
    "natural_key": null,
    "assumptions": []
  },
  "_excluded": []
}

5. Filter source tables by relevance

Read each .schema/<cdm-name>/<pipeline_name>.dbml. For each table, automatically judge relevance against the confirmed canonical concepts.

Excluded = tables with no plausible connection to any concept (e.g. internal audit logs, pipeline metadata, dlt system tables like _dlt_loads, _dlt_pipeline_state).

Do NOT ask the user — apply your judgement. Record each exclusion under _excluded:

{"table": "hubspot__email_events__propertyhistory", "reason": "property change log, not a business entity"}

6. Match source tables to business entities

For each remaining (non-excluded) table, propose which business entity it belongs to.

Present a mapping table to the user:

Source table	Represents	Confidence	Notes
hubspot__contacts	Person	high	primary contact record
luma__guests	Person	high	event attendee
hubspot__companies	Company	high

• User may correct mappings, reassign tables, or mark a table as excluded
• Wait for explicit confirmation

Add confirmed tables under each concept's tables array:

"Person": {
  ...
  "tables": [
    {"table": "hubspot__contacts", "source_pipeline": "hubspot_crm_pipeline", "role": "primary"},
    {"table": "luma__guests", "source_pipeline": "luma_pipeline", "role": "secondary"}
  ]
}

7. Identify cross-source natural keys

Find all concepts whose tables array contains entries from more than one source pipeline.

For each such concept:

1. List the contributing tables
2. Propose a natural key (the column(s) that can union/link rows across sources)
   • Common candidates: email, external_id, phone, name (last resort)
   • Prefer stable, unique, non-nullable fields

Present proposals to the user:

Person appears in HubSpot (contacts) and Luma (guests) — we can link them using a shared field.
  Suggested link field: email
  Reason: both sources have email as a unique identifier for the same person

Does this work, or would you prefer a different field?
(Say "keep separate" if these should not be merged across sources.)

• User may override the field or keep the two tables separate
• Wait for explicit confirmation

Set the confirmed natural key on the concept:

"Person": {
  ...
  "natural_key": "email"
}

7b. Annotate DBML files

After steps 6 and 7 are confirmed, edit each .schema/<cdm-name>/<pipeline_name>.dbml to embed semantic annotations as DBML Note blocks and inline comments.

Table-level note — on every mapped table, add a Note with the canonical concept, role, and (if applicable) natural key:

Table "contacts" [note: 'concept: Person | role: primary | natural_key: email'] {
    ...
}

Field-level note — on the natural key column, mark it explicitly:

    "email" text [note: 'natural_key']

Excluded tables — add a note so they are visually distinct:

Table "_dlt_loads" [note: 'excluded: dlt internal table'] {
    ...
}

This makes the DBML files self-documenting — create-ontology can read concept mappings directly from the DBML without cross-referencing taxonomy.json.

8. Confirm with user

Read .schema/<cdm-name>/taxonomy.json and present a summary of all recorded decisions:

• Concepts and their synonym collapses
• Excluded tables and reasons

Ask the user to review before proceeding:

Decisions recorded:
1. "guest" and "contact" are both treated as Person
2. hubspot__email_events__propertyhistory skipped — property change log, not a business entity
3. ...

Anything to correct before we move on?

Apply any corrections to taxonomy.json.

Output

• .schema/<cdm-name>/<pipeline_name>.dbml — one annotated file per pipeline (table/field notes carry concept, role, natural_key, exclusion)
• .schema/<cdm-name>/taxonomy.json — concept-keyed: references, table mappings, natural keys, assumptions, exclusions; _name holds the confirmed CDM folder name

Hand over to create-ontology skill.