PaperclipOrg
Claude Agent Skill · by Jaganpro

Sf Data

Install Sf Data skill for Claude Code from jaganpro/sf-skills.

Works with Paperclip

How Sf Data fits into a Paperclip company.

Sf Data drops into any Paperclip agent that handles this kind of work. Assign it to a specialist inside a pre-configured PaperclipOrg company and the skill becomes available on every heartbeat — no prompt engineering, no tool wiring.

S
SaaS FactoryPaired

Pre-configured AI company — 18 agents, 18 skills, one-time purchase.

$27$59
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Source file
SKILL.md244 lines
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---name: sf-datadescription: >  Salesforce data operations with 130-point scoring.  TRIGGER when: user creates test data, performs bulk import/export, uses sf data CLI  commands, or needs data factory patterns for Apex tests.  DO NOT TRIGGER when: SOQL query writing only (use sf-soql), Apex test execution  (use sf-testing), or metadata deployment (use sf-deploy).license: MITmetadata:  version: "1.2.0"  author: "Jag Valaiyapathy"  scoring: "130 points across 7 categories"--- # 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](../sf-soql/SKILL.md)- running or repairing Apex tests → [sf-testing](../sf-testing/SKILL.md)- deploying metadata first → [sf-deploy](../sf-deploy/SKILL.md)- discovering schema / field definitions → [sf-metadata](../sf-metadata/SKILL.md) --- ## 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.- 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](../sf-metadata/SKILL.md) or [sf-deploy](../sf-deploy/SKILL.md) --- ## Recommended Workflow ### 1. Verify prerequisitesConfirm object / field availability, org auth, and required parent records. ### 2. Run describe-first pre-flight validation when schema is uncertainBefore 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:```bashsf sobject describe --sobject ObjectName --target-org <alias> --json``` Helpful filters:```bash# Required + createable fieldsjq '.result.fields[] | select(.nillable==false and .createable==true) | {name, type}' # Valid picklist values for one fieldjq '.result.fields[] | select(.name=="StageName") | .picklistValues[].value' # Fields that cannot be set on createjq '.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 assetsUse the built-in templates under `assets/` when they fit:- `assets/factories/`- `assets/bulk/`- `assets/cleanup/`- `assets/soql/`- `assets/csv/`- `assets/json/` ### 5. Verify resultsCheck counts, relationships, and record IDs after creation or update. ### 6. Apply a bounded retry strategyIf creation fails:1. try the primary CLI shape once2. retry once with corrected parameters3. re-run describe / validate assumptions4. pivot to a different mechanism or provide a manual workaround Do **not** repeat the same failing command indefinitely. ### 7. Leave cleanup guidanceProvide 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 disciplinePrefer 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 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:1. **Operation performed**2. **Objects and counts**3. **Target org or local artifact path**4. **Record IDs / output files**5. **Verification result**6. **Cleanup instructions** Suggested shape: ```textData 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](../sf-metadata/SKILL.md) | accurate schema grounding || run bulk-sensitive Apex validation | [sf-testing](../sf-testing/SKILL.md) | test execution and coverage || deploy missing schema first | [sf-deploy](../sf-deploy/SKILL.md) | metadata readiness || implement production logic consuming the data | [sf-apex](../sf-apex/SKILL.md) or [sf-flow](../sf-flow/SKILL.md) | behavior implementation | --- ## Reference Map ### Start here- [references/sf-cli-data-commands.md](references/sf-cli-data-commands.md)- [references/test-data-best-practices.md](references/test-data-best-practices.md)- [references/orchestration.md](references/orchestration.md)- [references/test-data-patterns.md](references/test-data-patterns.md)- [references/test-data-factory-usage.md](references/test-data-factory-usage.md) ### Query / bulk / cleanup- [references/soql-relationship-guide.md](references/soql-relationship-guide.md)- [references/relationship-query-examples.md](references/relationship-query-examples.md)- [references/bulk-operations-guide.md](references/bulk-operations-guide.md)- [references/cleanup-rollback-guide.md](references/cleanup-rollback-guide.md)- [references/cleanup-rollback-example.md](references/cleanup-rollback-example.md) ### Examples / limits- [references/crud-workflow-example.md](references/crud-workflow-example.md)- [references/bulk-testing-example.md](references/bulk-testing-example.md)- [references/anonymous-apex-guide.md](references/anonymous-apex-guide.md)- [references/governor-limits-reference.md](references/governor-limits-reference.md)- [assets/](assets/) --- ## 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 |