Agent-Skills.md

Agent Skills: Salesforce Data Operations Expert (sf-data)

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UncategorizedID: Jaganpro/sf-skills/sf-data

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Jaganpro/sf-skills
JaganproLicense: MIT
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pnpm dlx add-skill https://github.com/Jaganpro/sf-skills/tree/HEAD/skills/sf-data

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skills/sf-data/SKILL.md

Skill Metadata

Name
sf-data
Description
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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:

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:

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

| 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 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

| 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:

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 | |---|---|---| | discover object / field structure | sf-metadata | accurate schema grounding | | run bulk-sensitive Apex validation | sf-testing | test execution and coverage | | deploy missing schema first | sf-deploy | metadata readiness | | implement production logic consuming the data | sf-apex or sf-flow | behavior implementation |

Reference Map

Start here

Query / bulk / cleanup

Examples / limits

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 |