Salesforce Data Operations Expert (handling-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
when the work involves:
- 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 → querying-soql
- running or repairing Apex tests → running-apex-tests
- deploying metadata first → deploying-metadata
- creating or modifying custom objects / fields → generating-custom-object or generating-custom-field
Important Mode Decision
Confirm which mode the user wants:
| Mode | Use when |
|---|
| Script generation | they want reusable , 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
- 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.
- Plan cleanup before creating large or noisy datasets — untracked records accumulate across runs and pollute org state.
- Use synthetic, non-identifying data in test records — real PII creates compliance risk and cannot be safely removed after bulk import.
- 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:
- generating-custom-object or generating-custom-field to create the missing schema, then deploying-metadata to deploy it before retrying the data operation
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
See
references/sf-cli-data-commands.md for the
command and jq filter patterns for inspecting fields, picklist values, and createable constraints.
3. Choose the smallest correct mechanism
| Need | Default approach |
|---|
| small one-off CRUD | single-record commands |
| large import/export | Bulk API 2.0 via |
| 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
when they fit:
5. Verify results
Check counts, relationships, and record IDs after creation or update.
6. Apply a bounded retry strategy
If creation fails:
- try the primary CLI shape once
- retry once with corrected parameters
- re-run describe / validate assumptions
- pivot to a different mechanism or provide a manual workaround
Do not repeat the same failing command indefinitely.
7. 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
- validate picklist values before creation
- verify parent IDs and relationship integrity
- account for validation rules and duplicate constraints
- exclude non-createable fields from input payloads
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 |
|---|
| wrong field API name or FLS issue | verify schema and access |
| mandatory field omitted | include required values from describe data |
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 |
| invalid picklist value | guessed value instead of describe-backed value | inspect picklist values first |
| non-writeable field error | field is not createable / updateable | remove it from the payload |
| 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:
text
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 |
|---|
| create missing custom objects | generating-custom-object | schema must exist before data operations |
| create missing custom fields | generating-custom-field | field-level schema must exist before data creation |
| run bulk-sensitive Apex validation | running-apex-tests | test execution and coverage |
| deploy missing schema first | deploying-metadata | metadata readiness |
| implement production Apex logic consuming the data | generating-apex | Apex class / trigger authoring |
| implement Flow logic consuming the data | generating-flow | Flow authoring and automation |
Reference Map
Start here
- references/sf-cli-data-commands.md
- references/test-data-best-practices.md
- references/orchestration.md
- references/test-data-patterns.md
- references/test-data-factory-usage.md
Query / bulk / cleanup
- references/soql-relationship-guide.md
- references/relationship-query-examples.md
- references/bulk-operations-guide.md
- references/cleanup-rollback-guide.md
- references/cleanup-rollback-example.md
Examples / limits
- references/crud-workflow-example.md
- references/bulk-testing-example.md
- references/anonymous-apex-guide.md
- references/governor-limits-reference.md
Validation scripts
- scripts/soql_validator.py — validate SOQL queries before execution
- scripts/validate_data_operation.py — pre-flight check for data operations (required fields, picklist values, createable fields)
Asset templates
- — Apex test data factory scripts (account, contact, opportunity, lead, user, etc.)
- — Bulk API 2.0 Apex templates (insert 200, 500, 10000 records; upsert by external ID)
- — Cleanup and rollback scripts (delete by name, date, pattern; transaction rollback)
- — SOQL query templates (aggregate, subquery, parent-to-child, child-to-parent, polymorphic)
- — CSV import templates for Account, Contact, Opportunity, custom objects
- — JSON tree import templates (account-contact, account-opportunity, full hierarchy)
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 |