mirror of
https://github.com/forcedotcom/afv-library.git
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9c47f28221
Adds the curated reference library the skill loads on demand, plus
four synthetic sample BDTs used by docs, tests, and LLM-mode demos.
references/ (4 curated Markdown files):
- bdt-reference.md — top-level BDT JSON anatomy: envelope,
nodes, edges, UI layer, definitions,
businessType semantics. Cites the core-262
upstream JSON schema and Connect API spec.
- bdt-node-catalog.md — every node type (DMO Source, DMO Sink,
Filter, Join, Union, Aggregate, Window,
Formula, Split, Append, etc.) with its
required/optional fields and typical
usage. Audited against core-262 enums.
- bdt-function-catalog.md — the expression-language function surface
(string, numeric, date, conditional,
aggregate). Grouped by category with
signature + one-line semantics.
- bdt-window-functions.md — windowing operators (ROW_NUMBER, RANK,
LEAD/LAG, running aggregates) with PARTITION
BY / ORDER BY grammar and gotchas.
assets/sample_bdts/ (4 synthetic, dependency-free BDTs):
- minimal_dmo_to_dmo.json — smallest valid BDT (1 source, 1 sink).
- joins_and_filters.json — join + filter composition.
- window_and_aggregate.json — window function + aggregate in one graph.
- append_and_split.json — append-then-split branching topology.
Grounding rules enforced in this commit:
- Every claim in references/ cites an upstream source (core-262 JSON
schema, Connect API reference, or the Data Cloud BDT editor spec).
No speculative content.
- No raw DITA or internal-only documentation is shipped; references
are synthesized from public-facing material.
- BusinessTypeEnum values use the canonical camelCase casing from
core-262 (case-cleanup fix included here).
- Sample BDTs are original synthetic fixtures, not redacted customer
data. Each is small enough to read end-to-end.
@W-22196528@
75 lines
2.5 KiB
JSON
75 lines
2.5 KiB
JSON
{
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"version": "66.0",
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"nodes": {
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"LOAD_ORDERS": {
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"action": "load",
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"sources": [],
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"parameters": {
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"dataset": {"name": "ssot__SalesOrder__dlm", "type": "dataModelObject"},
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"fields": ["ssot__Id__c", "ssot__AccountId__c", "ssot__CreatedDate__c", "ssot__GrandTotalAmount__c"],
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"sampleDetails": {"type": "TopN", "sortBy": []}
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}
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},
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"RANK_ORDERS": {
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"action": "computeRelative",
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"sources": ["LOAD_ORDERS"],
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"parameters": {
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"partitionBy": ["ssot__AccountId__c"],
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"orderBy": [{"fieldName": "ssot__CreatedDate__c", "direction": "ASC"}],
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"expressionType": "SQL",
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"fields": [
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{
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"name": "OrderRank__c",
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"label": "Order Rank",
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"formulaExpression": "row_number()",
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"type": "NUMBER",
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"businessType": "Number",
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"precision": 18,
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"scale": 0,
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"defaultValue": ""
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}
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]
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}
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},
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"AGG_BY_ACCOUNT": {
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"action": "aggregate",
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"sources": ["RANK_ORDERS"],
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"parameters": {
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"groupings": ["ssot__AccountId__c"],
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"aggregations": [
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{"action": "SUM", "name": "TotalAmount__c", "source": "ssot__GrandTotalAmount__c"},
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{"action": "COUNT", "name": "OrderCount__c", "source": "ssot__Id__c"}
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],
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"nodeType": "STANDARD"
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}
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},
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"OUTPUT_SUMMARY": {
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"action": "outputD360",
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"sources": ["AGG_BY_ACCOUNT"],
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"parameters": {
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"name": "Account_Summary__dlm",
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"type": "dataModelObject",
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"writeMode": "OVERWRITE",
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"fieldsMappings": [
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{"sourceField": "ssot__AccountId__c", "targetField": "AccountId__c"},
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{"sourceField": "TotalAmount__c", "targetField": "TotalAmount__c"},
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{"sourceField": "OrderCount__c", "targetField": "OrderCount__c"}
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]
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}
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}
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},
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"ui": {
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"nodes": {
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"LOAD_ORDERS": {"label": "Sales Orders", "type": "LOAD_DATASET", "top": 100, "left": 100},
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"RANK_ORDERS": {"label": "Rank by account", "type": "COMPUTE_RELATIVE", "top": 100, "left": 260},
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"AGG_BY_ACCOUNT": {"label": "Totals per account", "type": "AGGREGATE", "top": 100, "left": 420},
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"OUTPUT_SUMMARY": {"label": "Account Summary", "type": "OUTPUT", "top": 100, "left": 580}
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},
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"connectors": [
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{"source": "LOAD_ORDERS", "target": "RANK_ORDERS"},
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{"source": "RANK_ORDERS", "target": "AGG_BY_ACCOUNT"},
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{"source": "AGG_BY_ACCOUNT", "target": "OUTPUT_SUMMARY"}
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]
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}
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}
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