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

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.

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

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.

If metadata is missing, stop and hand off to:

sf-metadata or sf-deploy

Recommended Workflow

1. Verify prerequisites

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

2. 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

3. 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/

4. Verify results

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

5. Leave cleanup guidance

Provide exact cleanup commands or rollback assets whenever data was created.

High-Signal Rules

Bulk safety

use bulk operations for large volumes
test automation-sensitive behavior with 251+ records where appropriate
avoid one-record-at-a-time patterns for bulk scenarios

Data integrity

include required fields
verify parent IDs and relationship integrity
account for validation rules and duplicate constraints

Cleanup discipline

Prefer one of:

delete-by-ID
delete-by-pattern
delete-by-created-date window
rollback / savepoint patterns for script-based test runs

Common Failure Patterns

Error	Likely cause	Default fix direction
`INVALID_FIELD`	wrong field API name or FLS issue	verify schema and access
`REQUIRED_FIELD_MISSING`	mandatory field omitted	include required values
`INVALID_CROSS_REFERENCE_KEY`	bad parent ID	create / verify parent first
`FIELD_CUSTOM_VALIDATION_EXCEPTION`	validation rule blocked the record	use valid test data or adjust setup
`DUPLICATE_VALUE`	unique-field conflict	query existing data first
bulk limits / timeouts	wrong tool for the volume	switch to bulk / staged import

Output Format

When finishing, report in this order:

Operation performed
Objects and counts
Target org or local artifact path
Record IDs / output files
Verification result
Cleanup instructions

Suggested shape:

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>

Cross-Skill Integration

Need	Delegate to	Reason
discover object / field structure	sf-metadata	accurate schema grounding
run bulk-sensitive Apex validation	sf-testing	test execution and coverage
deploy missing schema first	sf-deploy	metadata readiness
implement production logic consuming the data	sf-apex or sf-flow	behavior implementation

Reference Map

Start here

Query / bulk / cleanup

Examples / limits

Score Guide

Score	Meaning
117+	strong production-safe data workflow
104–116	good operation with minor improvements possible
91–103	acceptable but review advised
78–90	partial / risky patterns present
< 78	blocked until corrected