data360-prepare

Name: data360-prepare
Author: forcedotcom

744stars272forks

How to install data360-prepare?

forcedotcom/sf-skills/data360-prepare

$npx -y skills add forcedotcom/sf-skills --skill data360-prepare

Installs into the current project.

Summary

Salesforce Data Cloud Prepare phase. Use this skill when the user creates or manages Data Cloud data streams, DLOs, transforms, or Document AI configurations. TRIGGER when: user creates or manages Data Cloud data streams, DLOs, transforms, or Document AI configurations, or asks a

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SKILL.md

1	# data360-prepare: Data Cloud Prepare Phase
2
3	Use this skill when the user needs ingestion and lake preparation work: data streams, Data Lake Objects (DLOs), transforms, Document AI, unstructured ingestion, or the handoff from connector setup into a live stream.
4
5	## When This Skill Owns the Task
6
7	Use `data360-prepare` when the work involves:
8	- `sf data360 data-stream *`
9	- `sf data360 dlo *`
10	- `sf data360 transform *`
11	- `sf data360 docai *`
12	- choosing how data should enter Data Cloud
13	- rerunning or rescanning ingestion after a source update
14	- preparing Ingestion API-backed streams after connector setup is complete
15
16	Delegate elsewhere when the user is:
17	- still creating/testing source connections → [data360-connect](../data360-connect/SKILL.md)
18	- mapping to DMOs or designing IR/data graphs → [data360-harmonize](../data360-harmonize/SKILL.md)
19	- querying ingested data → [data360-query](../data360-query/SKILL.md)
20
21	---
22
23	## Required Context to Gather First
24
25	Ask for or infer:
26	- target org alias
27	- source connection name
28	- source object / dataset / document source
29	- desired stream type
30	- DLO naming expectations
31	- whether the user is creating, updating, running, or deleting a stream
32	- whether the source is CRM, a database connector, an unstructured file source, or an Ingestion API feed
33
34	---
35
36	## Core Operating Rules
37
38	- Verify the external plugin runtime before running Data Cloud commands.
39	- Run the shared readiness classifier before mutating ingestion assets: `node ../data360-orchestrate/scripts/diagnose-org.mjs -o <org> --phase prepare --json`.
40	- Prefer inspecting existing streams and DLOs before creating new ingestion assets.
41	- Suppress linked-plugin warning noise with `2>/dev/null` for normal usage.
42	- Treat DLO naming and field naming as Data Cloud-specific, not CRM-native.
43	- Confirm whether each dataset should be treated as `Profile`, `Engagement`, or `Other` before creating the stream.
44	- Distinguish stream-level refresh from connection-level reruns when working with unstructured sources.
45	- Use UI setup intentionally when initial stream or unstructured asset creation is platform-gated.
46	- Hand off to Harmonize only after ingestion assets are clearly healthy.
47
48	---
49
50	## Recommended Workflow
51
52	### 1. Classify readiness for prepare work
53	```bash
54	node ../data360-orchestrate/scripts/diagnose-org.mjs -o <org> --phase prepare --json
55	```
56
57	### 2. Inspect existing ingestion assets
58	```bash
59	sf data360 data-stream list -o <org> 2>/dev/null
60	sf data360 dlo list -o <org> 2>/dev/null
61	```
62
63	### 3. Confirm the stream category before creation
64	Use these rules when suggesting categories:
65
66	\| Category \| Use for \| Typical requirement \|
67	\|---\|---\|---\|
68	\| `Profile` \| person/entity records \| primary key \|
69	\| `Engagement` \| time-based events or interactions \| primary key + event time field \|
70	\| `Other` \| reference/configuration/supporting datasets \| primary key \|
71
72	When the source is ambiguous, ask the user explicitly whether the dataset should be treated as `Profile`, `Engagement`, or `Other`.
73
74	### 4. Create or inspect streams intentionally
75	```bash
76	sf data360 data-stream get -o <org> --name <stream> 2>/dev/null
77	sf data360 data-stream create-from-object -o <org> --object Contact --connection SalesforceDotCom_Home 2>/dev/null
78	sf data360 data-stream create -o <org> -f stream.json 2>/dev/null
79	sf data360 data-stream run -o <org> --name <stream> 2>/dev/null
80	```
81
82	### 5. Check DLO shape
83	```bash
84	sf data360 dlo get -o <org> --name Contact_Home__dll 2>/dev/null
85	```
86
87	### 6. Choose the right refresh mechanism
88	Use the smaller refresh scope that matches the user goal:
89
90	```bash
91	sf data360 data-stream run -o <org> --name <stream> 2>/dev/null
92	sf data360 connection run-existing -o <org> --name <connection-id> 2>/dev/null
93	```
94
95	- `data-stream run` is the closest match to a stream-level refresh or re-scan.
96	- `connection run-existing` runs at the connection level and can be useful for some connector workflows, but it is not a reliable replacement for stream refresh on unstructured sources.
97	- For unstructured document connectors, prefer `data-stream run` when the goal is to re-scan newly added or changed files.
98
99	### 7. Handle unstructured sources deliberately
100	For SharePoint-style document ingestion, a minimal unstructured DLO payload can look like:
101
102	```json
103	{
104	"name": "my_udlo",
105	"label": "My UDLO",
106	"category": "Directory_Tab

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data360-prepare

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View on GitHub

How to install data360-prepare?

forcedotcom/sf-skills/data360-prepare

$npx -y skills add forcedotcom/sf-skills --skill data360-prepare

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	# data360-prepare: Data Cloud Prepare Phase
2
3	Use this skill when the user needs ingestion and lake preparation work: data streams, Data Lake Objects (DLOs), transforms, Document AI, unstructured ingestion, or the handoff from connector setup into a live stream.
4
5	## When This Skill Owns the Task
6
7	Use `data360-prepare` when the work involves:
8	- `sf data360 data-stream *`
9	- `sf data360 dlo *`
10	- `sf data360 transform *`
11	- `sf data360 docai *`
12	- choosing how data should enter Data Cloud
13	- rerunning or rescanning ingestion after a source update
14	- preparing Ingestion API-backed streams after connector setup is complete
15
16	Delegate elsewhere when the user is:
17	- still creating/testing source connections → [data360-connect](../data360-connect/SKILL.md)
18	- mapping to DMOs or designing IR/data graphs → [data360-harmonize](../data360-harmonize/SKILL.md)
19	- querying ingested data → [data360-query](../data360-query/SKILL.md)
20
21	---
22
23	## Required Context to Gather First
24
25	Ask for or infer:
26	- target org alias
27	- source connection name
28	- source object / dataset / document source
29	- desired stream type
30	- DLO naming expectations
31	- whether the user is creating, updating, running, or deleting a stream
32	- whether the source is CRM, a database connector, an unstructured file source, or an Ingestion API feed
33
34	---
35
36	## Core Operating Rules
37
38	- Verify the external plugin runtime before running Data Cloud commands.
39	- Run the shared readiness classifier before mutating ingestion assets: `node ../data360-orchestrate/scripts/diagnose-org.mjs -o <org> --phase prepare --json`.
40	- Prefer inspecting existing streams and DLOs before creating new ingestion assets.
41	- Suppress linked-plugin warning noise with `2>/dev/null` for normal usage.
42	- Treat DLO naming and field naming as Data Cloud-specific, not CRM-native.
43	- Confirm whether each dataset should be treated as `Profile`, `Engagement`, or `Other` before creating the stream.
44	- Distinguish stream-level refresh from connection-level reruns when working with unstructured sources.
45	- Use UI setup intentionally when initial stream or unstructured asset creation is platform-gated.
46	- Hand off to Harmonize only after ingestion assets are clearly healthy.
47
48	---
49
50	## Recommended Workflow
51
52	### 1. Classify readiness for prepare work
53	```bash
54	node ../data360-orchestrate/scripts/diagnose-org.mjs -o <org> --phase prepare --json
55	```
56
57	### 2. Inspect existing ingestion assets
58	```bash
59	sf data360 data-stream list -o <org> 2>/dev/null
60	sf data360 dlo list -o <org> 2>/dev/null
61	```
62
63	### 3. Confirm the stream category before creation
64	Use these rules when suggesting categories:
65
66	\| Category \| Use for \| Typical requirement \|
67	\|---\|---\|---\|
68	\| `Profile` \| person/entity records \| primary key \|
69	\| `Engagement` \| time-based events or interactions \| primary key + event time field \|
70	\| `Other` \| reference/configuration/supporting datasets \| primary key \|
71
72	When the source is ambiguous, ask the user explicitly whether the dataset should be treated as `Profile`, `Engagement`, or `Other`.
73
74	### 4. Create or inspect streams intentionally
75	```bash
76	sf data360 data-stream get -o <org> --name <stream> 2>/dev/null
77	sf data360 data-stream create-from-object -o <org> --object Contact --connection SalesforceDotCom_Home 2>/dev/null
78	sf data360 data-stream create -o <org> -f stream.json 2>/dev/null
79	sf data360 data-stream run -o <org> --name <stream> 2>/dev/null
80	```
81
82	### 5. Check DLO shape
83	```bash
84	sf data360 dlo get -o <org> --name Contact_Home__dll 2>/dev/null
85	```
86
87	### 6. Choose the right refresh mechanism
88	Use the smaller refresh scope that matches the user goal:
89
90	```bash
91	sf data360 data-stream run -o <org> --name <stream> 2>/dev/null
92	sf data360 connection run-existing -o <org> --name <connection-id> 2>/dev/null
93	```
94
95	- `data-stream run` is the closest match to a stream-level refresh or re-scan.
96	- `connection run-existing` runs at the connection level and can be useful for some connector workflows, but it is not a reliable replacement for stream refresh on unstructured sources.
97	- For unstructured document connectors, prefer `data-stream run` when the goal is to re-scan newly added or changed files.
98
99	### 7. Handle unstructured sources deliberately
100	For SharePoint-style document ingestion, a minimal unstructured DLO payload can look like:
101
102	```json
103	{
104	"name": "my_udlo",
105	"label": "My UDLO",
106	"category": "Directory_Tab

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