Salesforce Data Operations Expert (sf-data)

Use this skill when the user needs Salesforce data work: record CRUD, bulk import/export, test data generation, cleanup scripts, or data factory patterns for validating Apex, Flow, or integration behavior.

When This Skill Owns the Task

Use sf-data when the work involves:

• sf data CLI commands
• record creation, update, delete, upsert, export, or tree import/export
• realistic test data generation
• bulk data operations and cleanup
• Apex anonymous scripts for data seeding / rollback

Delegate elsewhere when the user is:

• writing SOQL only → sf-soql
• running or repairing Apex tests → sf-testing
• deploying metadata first → sf-deploy
• discovering schema / field definitions → sf-metadata

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Important Mode Decision

Confirm which mode the user wants:

Mode	Use when
Script generation	they want reusable .apex, CSV, or JSON assets without touching an org yet
Remote execution	they want records created / changed in a real org now

Do not assume remote execution if the user may only want scripts.

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Required Context to Gather First

Ask for or infer:

• target object(s)
• org alias, if remote execution is required
• operation type: query, create, update, delete, upsert, import, export, cleanup
• expected volume
• whether this is test data, migration data, or one-off troubleshooting data
• any parent-child relationships that must exist first

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Core Operating Rules

• sf-data acts on remote org data unless the user explicitly wants local script generation.
• Objects and fields must already exist before data creation.
• For automation testing, prefer 251+ records when bulk behavior matters.
• Always think about cleanup before creating large or noisy datasets.
• Never use real PII in generated test data.
• Prefer CLI-first for straightforward CRUD; use anonymous Apex when the operation truly needs server-side orchestration.

If metadata is missing, stop and hand off to:

• sf-metadata or sf-deploy

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Recommended Workflow

1. Verify prerequisites

Confirm object / field availability, org auth, and required parent records.

2. Run describe-first pre-flight validation when schema is uncertain

Before creating or updating records, use object describe data to validate:

• required fields
• createable vs non-createable fields
• picklist values
• relationship fields and parent requirements

Example pattern:

sf sobject describe --sobject ObjectName --target-org <alias> --json

Helpful filters:

# Required + createable fields
jq '.result.fields[] | select(.nillable==false and .createable==true) | {name, type}'

# Valid picklist values for one field
jq '.result.fields[] | select(.name=="StageName") | .picklistValues[].value'

# Fields that cannot be set on create
jq '.result.fields[] | select(.createable==false) | .name'

3. Choose the smallest correct mechanism

Need	Default approach
small one-off CRUD	sf data single-record commands
large import/export	Bulk API 2.0 via sf data ... bulk
parent-child seed set	tree import/export
reusable test dataset	factory / anonymous Apex script
reversible experiment	cleanup script or savepoint-based approach

4. Execute or generate assets

Use the built-in templates under assets/ when they fit:

• assets/factories/
• assets/bulk/
• assets/cleanup/
• assets/soql/
• assets/csv/
• assets/json/

5. Verify results

Check counts, relationships, and record IDs after creation or update.

6. Apply a bounded retry strategy

If creation fails:

1. try the primary CLI shape once
2. retry once with corrected parameters
3. re-run describe / validate assumptions
4. pivot to a different mechanism or provide a manual workaround

Do not repeat the same failing command indefinitely.

7. Leave cleanup guidance

Data operation: <create / update / delete / export / seed>
Objects: <object + counts>
Target: <org alias or local path>
Artifacts: <record ids / csv / apex / json files>
Verification: <passed / partial / failed>
Cleanup: <exact delete or rollback guidance>