Now liveThe Skillselion MCP - thousands of ranked skills, loaded into your agent mid-task. No install.Get it →
jaganpro avatar

Sf Data

  • 35 installs
  • 423 repo stars
  • Updated April 27, 2026
  • jaganpro/claude-code-sfskills

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

Helps with ai & agent building tasks.

About

sf-data is a Claude Code skill for ai & agent building. It helps solo builders move faster with AI-assisted development.

  • sf-data
  • AI & Agent Building
  • AI-coding skill

Sf Data by the numbers

  • 35 all-time installs (skills.sh)
  • Data as of Aug 2, 2026 (Skillselion catalog sync)
npx skills add https://github.com/jaganpro/claude-code-sfskills --skill sf-data

Add your badge

Show developers this skill is listed on Skillselion. Paste this into your README.

Listed on Skillselion
Installs35
repo stars423
Last updatedApril 27, 2026
Repositoryjaganpro/claude-code-sfskills

What it does

Helps with ai & agent building tasks.

Files

SKILL.mdMarkdownGitHub ↗

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:

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

  • 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 or sf-deploy

---

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

Example pattern:

sf sobject describe --sobject ObjectName --target-org <alias> --json

Helpful filters:

# Required + createable fields
jq '.result.fields[] | select(.nillable==false and .createable==true) | {name, type}'

# Valid picklist values for one field
jq '.result.fields[] | select(.name=="StageName") | .picklistValues[].value'

# Fields that cannot be set on create
jq '.result.fields[] | select(.createable==false) | .name'

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
discover object / field structuresf-metadataaccurate schema grounding
run bulk-sensitive Apex validationsf-testingtest execution and coverage
deploy missing schema firstsf-deploymetadata readiness
implement production logic consuming the datasf-apex or sf-flowbehavior implementation

---

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
  • assets/

---

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

This week in AI coding

Five minutes, every Monday - the tools, releases and tactics for developers.

unsubscribe anytime.