data360-code-extension-generate

Name: data360-code-extension-generate
Author: forcedotcom

744stars272forks

How to install data360-code-extension-generate?

forcedotcom/sf-skills/data360-code-extension-generate

$npx -y skills add forcedotcom/sf-skills --skill data360-code-extension-generate

Installs into the current project.

Summary

Develop and deploy Data Cloud Code Extensions using SF CLI plugin. Use this skill when creating custom Python transformations for Data Cloud, deploying code extensions, or testing data transformations. Supports init, run, scan, and deploy operations.

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SKILL.md

1	# data360-code-extension-generate Skill
2
3	## Overview
4
5	This skill provides a complete workflow for developing, testing, and deploying custom Python code extensions to Salesforce Data Cloud. Code extensions allow you to write Python transformations that read from and write to Data Lake Objects (DLOs) and Data Model Objects (DMOs).
6
7	## When to Use
8
9	- User wants to create a new code extension project
10	- User needs to test a code extension locally
11	- User wants to scan code for required permissions
12	- User needs to deploy a code extension to Data Cloud
13	- User is working with Data Cloud transformations
14	- User wants to read/write DLO or DMO data programmatically
15
16	## Prerequisites Check
17
18	Before executing any code extension commands, verify prerequisites:
19
20	1. SF CLI with plugin installed
21	```bash
22	sf plugins --core \| grep data-code-extension
23	```
24	If not installed:
25	```bash
26	sf plugins install @salesforce/plugin-data-code-extension
27	```
28
29	2. Python 3.11
30	```bash
31	python --version # Should show 3.11.x
32	```
33
34	3. Data Cloud Custom Code SDK
35	```bash
36	pip list \| grep salesforce-data-customcode
37	```
38	If not installed:
39	```bash
40	pip install salesforce-data-customcode
41	```
42
43	4. Docker running (for deploy only)
44	```bash
45	docker ps
46	```
47
48	5. Authenticated org
49	```bash
50	sf org display --target-org <org_alias> --json
51	```
52
53	## Skill Workflow
54
55	### Phase 1: Initialize Project
56
57	Create a new code extension project with scaffolding.
58
59	Commands:
60
61	For script-based code extensions (batch transformations):
62	```bash
63	sf data-code-extension script init --package-dir <directory>
64	```
65
66	For function-based code extensions (real-time):
67	```bash
68	sf data-code-extension function init --package-dir <directory>
69	```
70
71	Required Option:
72	- `--package-dir, -p` - Directory path where the package will be created
73
74	What it creates:
75	```text
76	my-transform/ # Project root
77	├── payload/ # CRITICAL: This is what --package-dir must point to for deploy
78	│ ├── entrypoint.py # Main transformation code
79	│ └── config.json # Code extension configuration
80	├── requirements.txt # Python dependencies
81	└── README.md
82	```
83
84	## Directory Context During Workflow
85
86	IMPORTANT: Understanding the directory structure is critical for successful deployment.
87
88	Commands and their directory requirements:
89
90	\| Command \| Run From \| Path/File Argument \|
91	\|---------\|----------\|-------------------\|
92	\| `init` \| Parent directory \| `<project-name>` or `.` \|
93	\| `scan` \| Project root \| `./payload/entrypoint.py` \|
94	\| `run` \| Project root \| `./payload/entrypoint.py` \|
95	\| `deploy` \| Project root \| `--package-dir ./payload` (REQUIRED) \|
96
97	CRITICAL: The `--package-dir` argument in deploy command MUST point to the `payload` directory, not the project root.
98
99	### Phase 2: Develop Transformation
100
101	Edit `payload/entrypoint.py` with transformation logic.
102
103	Script Example (Batch):
104	```python
105	from datacustomcode import Client
106
107	client = Client()
108
109	# Read from DLO
110	df = client.read_dlo('Employee__dll')
111
112	# Transform data (uppercase position field)
113	df['position_upper'] = df['position'].str.upper()
114
115	# Write to output DLO
116	client.write_to_dlo('Employee_Upper__dll', df, 'overwrite')
117	```
118
119	Function Example (Real-time):
120	```python
121	from datacustomcode import FunctionClient
122
123	def transform(event, context):
124	client = FunctionClient(context)
125	input_data = event['data']
126	output = {
127	'name': input_data['name'].upper(),
128	'status': 'processed'
129	}
130	return output
131	```
132
133	Common Operations:
134	- `client.read_dlo('DLO_Name__dll')` - Read from DLO
135	- `client.read_dmo('DMO_Name')` - Read from DMO
136	- `client.write_to_dlo('DLO_Name__dll', df, 'overwrite')` - Write to DLO
137	- `client.write_to_dmo('DMO_Name', df, 'upsert')` - Write to DMO
138
139	### Phase 3: Scan for Permissions
140
141	Scan the entrypoint file to detect required permissions and generate config.json.
142
143	Command:
144	```bash
145	sf data-code-extension script scan --entrypoint ./payload/entrypoint.py
146	```
147
148	What it detects:
149	- Read permissions for DLOs/DMOs
150	- Write permissions for DLOs/DMOs
151	- Python package dependencies
152	- Updates `config.json` and `requirements.txt`
153
154	### Phase 4: Validate DLO Schema (Pre-Test Check)
155
156	CRITICAL: Before running tests locally, validate that all DLOs used in your code exist and have the expected fields.

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Type

data360-code-extension-generate

744stars272forks

View on GitHub

How to install data360-code-extension-generate?

forcedotcom/sf-skills/data360-code-extension-generate

$npx -y skills add forcedotcom/sf-skills --skill data360-code-extension-generate

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	# data360-code-extension-generate Skill
2
3	## Overview
4
5	This skill provides a complete workflow for developing, testing, and deploying custom Python code extensions to Salesforce Data Cloud. Code extensions allow you to write Python transformations that read from and write to Data Lake Objects (DLOs) and Data Model Objects (DMOs).
6
7	## When to Use
8
9	- User wants to create a new code extension project
10	- User needs to test a code extension locally
11	- User wants to scan code for required permissions
12	- User needs to deploy a code extension to Data Cloud
13	- User is working with Data Cloud transformations
14	- User wants to read/write DLO or DMO data programmatically
15
16	## Prerequisites Check
17
18	Before executing any code extension commands, verify prerequisites:
19
20	1. SF CLI with plugin installed
21	```bash
22	sf plugins --core \| grep data-code-extension
23	```
24	If not installed:
25	```bash
26	sf plugins install @salesforce/plugin-data-code-extension
27	```
28
29	2. Python 3.11
30	```bash
31	python --version # Should show 3.11.x
32	```
33
34	3. Data Cloud Custom Code SDK
35	```bash
36	pip list \| grep salesforce-data-customcode
37	```
38	If not installed:
39	```bash
40	pip install salesforce-data-customcode
41	```
42
43	4. Docker running (for deploy only)
44	```bash
45	docker ps
46	```
47
48	5. Authenticated org
49	```bash
50	sf org display --target-org <org_alias> --json
51	```
52
53	## Skill Workflow
54
55	### Phase 1: Initialize Project
56
57	Create a new code extension project with scaffolding.
58
59	Commands:
60
61	For script-based code extensions (batch transformations):
62	```bash
63	sf data-code-extension script init --package-dir <directory>
64	```
65
66	For function-based code extensions (real-time):
67	```bash
68	sf data-code-extension function init --package-dir <directory>
69	```
70
71	Required Option:
72	- `--package-dir, -p` - Directory path where the package will be created
73
74	What it creates:
75	```text
76	my-transform/ # Project root
77	├── payload/ # CRITICAL: This is what --package-dir must point to for deploy
78	│ ├── entrypoint.py # Main transformation code
79	│ └── config.json # Code extension configuration
80	├── requirements.txt # Python dependencies
81	└── README.md
82	```
83
84	## Directory Context During Workflow
85
86	IMPORTANT: Understanding the directory structure is critical for successful deployment.
87
88	Commands and their directory requirements:
89
90	\| Command \| Run From \| Path/File Argument \|
91	\|---------\|----------\|-------------------\|
92	\| `init` \| Parent directory \| `<project-name>` or `.` \|
93	\| `scan` \| Project root \| `./payload/entrypoint.py` \|
94	\| `run` \| Project root \| `./payload/entrypoint.py` \|
95	\| `deploy` \| Project root \| `--package-dir ./payload` (REQUIRED) \|
96
97	CRITICAL: The `--package-dir` argument in deploy command MUST point to the `payload` directory, not the project root.
98
99	### Phase 2: Develop Transformation
100
101	Edit `payload/entrypoint.py` with transformation logic.
102
103	Script Example (Batch):
104	```python
105	from datacustomcode import Client
106
107	client = Client()
108
109	# Read from DLO
110	df = client.read_dlo('Employee__dll')
111
112	# Transform data (uppercase position field)
113	df['position_upper'] = df['position'].str.upper()
114
115	# Write to output DLO
116	client.write_to_dlo('Employee_Upper__dll', df, 'overwrite')
117	```
118
119	Function Example (Real-time):
120	```python
121	from datacustomcode import FunctionClient
122
123	def transform(event, context):
124	client = FunctionClient(context)
125	input_data = event['data']
126	output = {
127	'name': input_data['name'].upper(),
128	'status': 'processed'
129	}
130	return output
131	```
132
133	Common Operations:
134	- `client.read_dlo('DLO_Name__dll')` - Read from DLO
135	- `client.read_dmo('DMO_Name')` - Read from DMO
136	- `client.write_to_dlo('DLO_Name__dll', df, 'overwrite')` - Write to DLO
137	- `client.write_to_dmo('DMO_Name', df, 'upsert')` - Write to DMO
138
139	### Phase 3: Scan for Permissions
140
141	Scan the entrypoint file to detect required permissions and generate config.json.
142
143	Command:
144	```bash
145	sf data-code-extension script scan --entrypoint ./payload/entrypoint.py
146	```
147
148	What it detects:
149	- Read permissions for DLOs/DMOs
150	- Write permissions for DLOs/DMOs
151	- Python package dependencies
152	- Updates `config.json` and `requirements.txt`
153
154	### Phase 4: Validate DLO Schema (Pre-Test Check)
155
156	CRITICAL: Before running tests locally, validate that all DLOs used in your code exist and have the expected fields.

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Type