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Handling Sf Data

  • 532 installs
  • 787 repo stars
  • Updated August 5, 2026
  • forcedotcom/afv-library

This is a copy of handling-sf-data by forcedotcom - installs and ranking accrue to the original listing.

handling-sf-data is a Salesforce skill that performs record CRUD, bulk import and export, test-data generation, and org cleanup using the sf CLI and anonymous Apex with a 130-point scoring rubric.

About

handling-sf-data is version 1.1 of the afv-library Salesforce Data Operations Expert skill. It guides create, update, delete, bulk import and export, synthetic test-data generation, and org cleanup workflows through sf CLI commands and anonymous Apex scripts, returning a 130-point scoring assessment of data-operation quality. Triggers include seeding sandboxes, bulk CSV loads, data-factory patterns for Apex tests, and sf data CLI usage. The skill excludes SOQL-only query writing (querying-soql), Apex test runs (running-apex-tests), and metadata deployment (deploying-metadata). Developers reach for handling-sf-data when org records—not code packages—need programmatic manipulation at scale during feature development and QA preparation.

  • 130-point scoring system for Salesforce data operations
  • Handles record create, update, delete, upsert, export and tree import/export
  • Generates realistic test data and data factory patterns for Apex tests
  • Performs bulk operations and org record cleanup scripts
  • Delegates SOQL-only, Apex test execution, and metadata deployment to specialized skills

Handling Sf Data by the numbers

  • 532 all-time installs (skills.sh)
  • Data as of Aug 5, 2026 (Skillselion catalog sync)
npx skills add https://github.com/forcedotcom/afv-library --skill handling-sf-data

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Listed on Skillselion
Installs532
repo stars787
Last updatedAugust 5, 2026
Repositoryforcedotcom/afv-library

How do you bulk seed Salesforce test data?

Handle all Salesforce record CRUD, bulk import/export, test data generation, and org cleanup using the sf CLI and anonymous Apex.

Who is it for?

Salesforce developers seeding sandboxes, running bulk data imports, or building Apex test data factories with sf CLI.

Skip if: Tasks limited to SOQL queries, metadata deployments, or running Apex tests without data manipulation.

When should I use this skill?

User runs sf data commands, requests bulk import/export, test-data seeding, data-factory patterns, or org record cleanup.

What you get

Executed sf data commands, anonymous Apex scripts, seeded or cleaned records, and a 130-point data-operation score.

  • seeded records
  • bulk load scripts
  • 130-point operation score

By the numbers

  • Uses a 130-point scoring rubric for data operations
  • Skill metadata version 1.1 in afv-library

Files

SKILL.mdMarkdownGitHub ↗

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 handling-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 → 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:

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

  • handling-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.
  • 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 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 objectsgenerating-custom-objectschema must exist before data operations
create missing custom fieldsgenerating-custom-fieldfield-level schema must exist before data creation
run bulk-sensitive Apex validationrunning-apex-teststest execution and coverage
deploy missing schema firstdeploying-metadatametadata readiness
implement production Apex logic consuming the datagenerating-apexApex class / trigger authoring
implement Flow logic consuming the datagenerating-flowFlow 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

How it compares

Use handling-sf-data for record manipulation; switch to querying-soql when the task is read-only SOQL without loads or deletes.

FAQ

What tools does handling-sf-data use for Salesforce records?

handling-sf-data orchestrates the sf CLI data commands and anonymous Apex scripts for CRUD, bulk import/export, synthetic test data, and org cleanup with a 130-point quality score.

Which tasks are outside handling-sf-data scope?

handling-sf-data does not cover SOQL-only querying (querying-soql), Apex test execution (running-apex-tests), or metadata deployment (deploying-metadata).

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