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Platform Data Manage

  • 2.2k installs
  • 763 repo stars
  • Updated July 24, 2026
  • forcedotcom/sf-skills

Handles Salesforce data work: create, update, delete, and bulk import/export records, plus test-data generation and cleanup via the sf CLI and anonymous Apex.

About

platform-data-manage is a Salesforce sf-skills entry for data operations: creating, updating, deleting, and bulk importing or exporting records, plus generating realistic test datasets and cleanup scripts using the sf CLI and anonymous Apex. It is meant for seeding and managing org data during development and QA, while delegating SOQL-only queries and metadata deploys to other skills. A Salesforce developer or QA engineer reaches for it to populate and clean up org data during testing.

  • Salesforce record CRUD and bulk operations
  • Test-data generation and cleanup
  • Data factory patterns
  • sf CLI and anonymous Apex

Platform Data Manage by the numbers

  • 2,218 all-time installs (skills.sh)
  • +556 installs in the week ending Jul 28, 2026 (Skillselion tracking)
  • Ranked #50 of 923 Databases skills by installs in the Skillselion catalog
  • Security screen: LOW risk (skills.sh audit)
  • Data as of Jul 28, 2026 (Skillselion catalog sync)
npx skills add https://github.com/forcedotcom/sf-skills --skill platform-data-manage

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Listed on Skillselion
Installs2.2k
repo stars763
Security audit3 / 3 scanners passed
Last updatedJuly 24, 2026
Repositoryforcedotcom/sf-skills

What it does

Handles Salesforce data work: create, update, delete, and bulk import/export records, plus test-data generation and cleanup via the sf CLI and anonymous Apex.

Who is it for?

Seeding and cleaning up Salesforce org data

Skip if: SOQL-only queries or metadata deployment

Files

SKILL.mdMarkdownGitHub ↗

Salesforce Data Operations Expert (platform-data-manage)

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 platform-data-manage 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 → platform-soql-query
  • running or repairing Apex tests → platform-apex-test-run
  • deploying metadata first → platform-metadata-deploy
  • creating or modifying custom objects / fields → platform-custom-object-generate or platform-custom-field-generate

---

Important Mode Decision

Confirm which mode the user wants:

ModeUse when
Script generationthey want reusable .apex, CSV, or JSON assets without touching an org yet
Remote executionthey 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

  • platform-data-manage 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:

  • platform-custom-object-generate or platform-custom-field-generate to create the missing schema, then platform-metadata-deploy 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 sf sobject describe command and jq filter patterns for inspecting fields, picklist values, and createable constraints.

3. Choose the smallest correct mechanism

NeedDefault approach
small one-off CRUDsf data single-record commands
large import/exportBulk API 2.0 via sf data ... bulk
parent-child seed settree import/export
reusable test datasetfactory / anonymous Apex script
reversible experimentcleanup 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

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

ErrorLikely causeDefault fix direction
INVALID_FIELDwrong field API name or FLS issueverify schema and access
REQUIRED_FIELD_MISSINGmandatory field omittedinclude required values from describe data
INVALID_CROSS_REFERENCE_KEYbad parent IDcreate / verify parent first
FIELD_CUSTOM_VALIDATION_EXCEPTIONvalidation rule blocked the recorduse valid test data or adjust setup
invalid picklist valueguessed value instead of describe-backed valueinspect picklist values first
non-writeable field errorfield is not createable / updateableremove it from the payload
bulk limits / timeoutswrong tool for the volumeswitch 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:

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

NeedDelegate toReason
create missing custom objectsplatform-custom-object-generateschema must exist before data operations
create missing custom fieldsplatform-custom-field-generatefield-level schema must exist before data creation
run bulk-sensitive Apex validationplatform-apex-test-runtest execution and coverage
deploy missing schema firstplatform-metadata-deploymetadata readiness
implement production Apex logic consuming the dataplatform-apex-generateApex class / trigger authoring
implement Flow logic consuming the dataautomation-flow-generateFlow 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

  • assets/factories/ — Apex test data factory scripts (account, contact, opportunity, lead, user, etc.)
  • assets/bulk/ — Bulk API 2.0 Apex templates (insert 200, 500, 10000 records; upsert by external ID)
  • assets/cleanup/ — Cleanup and rollback scripts (delete by name, date, pattern; transaction rollback)
  • assets/soql/ — SOQL query templates (aggregate, subquery, parent-to-child, child-to-parent, polymorphic)
  • assets/csv/ — CSV import templates for Account, Contact, Opportunity, custom objects
  • assets/json/ — JSON tree import templates (account-contact, account-opportunity, full hierarchy)

---

Score Guide

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

Related skills

FAQ

Is Platform Data Manage safe to install?

skills.sh reports 3 of 3 security scanners passed. Review the Security Audits panel on this page before installing in production.

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