Sigma Ingest

Sigma ingest turns staged data model specs and workbook summaries into durable ktx wiki knowledge. The deterministic project() step has already written semantic-layer YAML for all warehouse-table data model elements before this skill runs — do not re-write those SL sources.

Work unit structure

Sigma produces at minimum two work units per ingest run:

• sigma-data-models or sigma-data-models-N
  • rawFiles: data-models/<id>.json files (one per data model in this batch)
  • peerFileIndex: workbooks/<id>.json files + sigma-manifest.json + sigma-projection-config.json
  • When the workspace has more than 50 data models, split into batches: sigma-data-models-0, sigma-data-models-1, … with displayLabel like "Sigma: data models (1/8)". When ≤50 data models, the unitKey is simply sigma-data-models with no suffix.
• sigma-workbooks or sigma-workbooks-N
  • rawFiles: workbooks/<id>.json files (one per workbook in this batch)
  • peerFileIndex: data-models/<id>.json files + sigma-manifest.json + sigma-projection-config.json
  • When the workspace has more than 2000 workbooks, split into batches: sigma-workbooks-0, sigma-workbooks-1, … with displayLabel like "Sigma: workbooks (1/4)". When ≤2000 workbooks, the unitKey is simply sigma-workbooks with no suffix.

sigma-manifest.json and sigma-projection-config.json are never in rawFiles. They live at the staged dir root and always appear in peerFileIndex.

Staged file shapes

data-models/<id>.json — one per data model (in rawFiles for data-model units):

{
  "sigmaId": "abc-123",
  "name": "Revenue Model",
  "path": "Finance/Revenue Model",
  "latestVersion": 3,
  "updatedAt": "2026-01-15T00:00:00Z",
  "isArchived": false,
  "spec": {
    "name": "Revenue Model",
    "pages": [{
      "id": "p1",
      "name": "Main",
      "elements": [{
        "id": "elem1",
        "kind": "table",
        "name": "Opportunities",
        "hidden": false,
        "source": {
          "kind": "warehouse-table",
          "connectionId": "<sigma-internal-uuid>",
          "path": ["DATABASE", "SCHEMA", "OPPORTUNITIES"]
        },
        "columns": [
          { "id": "c1", "name": "Deal Amount", "formula": "[OPPORTUNITIES/Amount]", "description": "Net contract value in USD" },
          { "id": "c2", "name": "Total ARR", "formula": "Sum([OPPORTUNITIES/ARR])", "description": "Annualised recurring revenue" }
        ]
      }]
    }]
  }
}

source.kind discriminates:

• warehouse-table — element maps directly to a warehouse table. Has connectionId and path (array of path segments forming the fully-qualified table name). project() writes an SL source when connectionMappings covers this connectionId.
• table — element is a derived view layered on top of another element; identified by source.elementId. No warehouse path. Wiki-only.

workbooks/<id>.json — one per workbook, in rawFiles for workbook units (summary only; no spec endpoint exists):

{
  "sigmaId": "wb-abc",
  "name": "ARR Tracker",
  "path": "Finance/Dashboards",
  "latestVersion": 2,
  "updatedAt": "2026-01-16T00:00:00Z",
  "isArchived": false,
  "workbookUrlId": "57a96EMo3G...",
  "description": "Tracks ARR by segment and cohort for the finance team"
}

Peer files (available via peerFileIndex, not rawFiles):

sigma-manifest.json — fetch summary; use for provenance only.

sigma-projection-config.json — written by fetch(), contains two fields the skill must read:

• connectionMappings: {sigmaInternalUuid: ktxWarehouseConnectionId}. Use the mapped warehouse connection ID for entity_details when verifying warehouse identifiers found in data model specs.
• workbookFilter: the filter settings that were active when workbooks were last fetched:
  • includeArchived (default false) — when false, archived workbooks are not in workbooks/; isArchived: true files will only appear when this was true.
  • includeExplorations (default false) — when false, exploration-type workbooks (unsaved analyses) are excluded; treat present workbooks as intentional, curated reports.
  • updatedSince (optional ISO 8601 string) — when set, only workbooks updated on or after this date are staged; the set is a recent-changes slice, not the full workspace. Do not infer that absent workbooks were deleted.

sigma-manifest.json also reflects any active dataModelFilter. When dataModelFilter.updatedSince was set during fetch, dataModelCount reflects only matching models, not the full workspace. Do not infer that absent data models were deleted.

Read sigma-projection-config.json first and keep workbookFilter in scope while processing the WorkUnit.

Required workflow

1. Read every rawFiles entry for the WorkUnit.
2. Read sigma-projection-config.json from the staged dir to get connectionMappings.
3. For each data model file: extract business semantics from element names, column descriptions, and the domain context of the model. Skip hidden elements and hidden columns.
4. For each workbook file: extract business domain knowledge from the name and description. When workbookFilter.updatedSince is set, treat the staged set as a recent-changes slice — absent workbooks were not deleted, they were simply outside the filter window.
5. Use discover_data before writing to find existing wiki pages on the same topic.
6. Write wiki candidates with context_candidate_write. Do not call wiki_write directly from a Sigma WorkUnit; Stage 4 reconciliation promotes candidates.
7. Do not write or edit SL sources. The project() step owns all SL output for Sigma.

Identifier Verification Protocol

Before writing a wiki page or SL source on any topic:

1. discover_data({query: "<topic>"}) - see what wikis, SL sources, and raw tables already exist. Prefer updating existing pages over creating new ones.

Before emitting any schema.table or schema.table.column into a wiki body, SL source, tables: frontmatter, sl_refs, or emit_unmapped_fallback:

2. entity_details({connectionId, targets: [{display: "<identifier>"}]}) - confirm the identifier resolves; inspect native types, FK/PK, and sampleValues. Use the warehouse connectionId from connectionMappings in sigma-projection-config.json, not the Sigma connection ID. If connectionMappings has no entry for the element's source.connectionId, skip entity_details — there is no mapped warehouse to verify against — and wrap any identifier references with [unverified - from <rawPath>].
3. For literal values from the source, such as status codes or plan tiers, check whether they appear in entity_details sampleValues for the relevant column. If sampleValues is short or the sample may have missed real values, run a sql_execution probe with the same warehouse connection id: sql_execution({connectionId, sql: "SELECT DISTINCT <col> FROM <ref> LIMIT 50"}).
4. If the candidate identifier still does not resolve, do one of:
   • Use sql_execution({connectionId, sql: "SELECT 1 FROM <ref> LIMIT 0"}). If it errors, the identifier is fictional.
   • Wrap the identifier in [unverified - from <rawPath>] in the wiki body, citing the exact raw path that mentioned it.
   • When recording emit_unmapped_fallback with no_physical_table, include the failing probe error in clarification.
5. Never copy <schema>.<table> placeholder strings from these instructions into output.

Data model elements

Warehouse-table elements (source.kind === "warehouse-table")

project() writes an SL source for a warehouse-table element only when the element's source.connectionId has an entry in connectionMappings. When no mapping exists, no SL source is written and the element is wiki-only.

To determine whether an SL source exists: check whether connectionMappings[element.source.connectionId] resolves. If it does, use sl_discover to find the source by its slugified name (<dataModelName>_<elementName>), then:

• Read the existing SL source with sl_read_source to understand what columns and measures are captured.
• Write a wiki candidate about the business domain if the element name, column descriptions, or data model description reveals durable knowledge not already in the wiki.
• sl_refs in the wiki candidate should point to the already-written SL source name.

If connectionMappings has no entry for the element's source.connectionId, treat the element as wiki-only — do not attempt sl_discover or sl_read_source for it, as no source was written.

Joins within a data model

Joins are not projected in v1; joins: [] is always written by project(). Lookup() formulas may be described in wiki prose instead.

Non-warehouse elements (source.kind === "table")

These reference another element by elementId — they are derived views layered on top of a warehouse-table element. They have no warehouse path of their own. Do not attempt SL writes for these elements. They may produce wiki candidates if their column names or descriptions reveal business semantics not captured by the underlying warehouse-table element.

Workbooks

Workbooks have summary metadata only. There is no spec endpoint.

Extract business domain knowledge from:

• name: the workbook's primary topic (e.g. "ARR Tracker" → ARR tracking concepts)
• description: business context and intended audience
• path: team or functional area (e.g. Finance/Dashboards)

Write wiki candidates when the name or description reveals a reusable business concept, metric definition, or domain convention. Write one candidate per distinct concept, not one per workbook.

Skip workbooks whose name or description contains no durable business semantics (e.g. "Untitled Workbook", "Test Dashboard").

Capture rules

Write wiki candidates for:

• Metric definitions mentioned in element names or column descriptions (e.g. "Net ARR", "Churned MRR")
• Domain conventions such as cohort definitions, segment taxonomies, or fiscal calendar rules
• Relationships between business entities revealed by data model joins

Skip:

• Visualization settings, layout, colors, chart types
• Owner names, folder paths, and version numbers as wiki narrative
• Hidden elements and hidden columns
• Data model names that are purely technical with no business meaning
• When workbookFilter.includeExplorations is false (the default), all staged workbooks are intentional reports — no extra exploration filter needed. When it is true, workbooks without a description or with a generic auto-generated name are likely ephemeral explorations; skip those.

Usage signals

Sigma workbooks carry latestVersion but no usage counts. Treat a higher latestVersion as weak evidence of continued maintenance; do not include version numbers in wiki prose.