sf-industry-commoncore-datamapper

sf-industry-commoncore-datamapper is a skill for Claude Code, Codex from Jaganpro/sf-skills. It costs 146 tokens per session (2,963 once invoked), scanned A, original, MIT.

A Salesforce OmniStudio tool for creating and checking Data Mappers, which move and reshape data between Salesforce records and other components.

In plain words
What is it for?
Use it to build Extract, Transform, Load, or Turbo Extract mappings, connect fields between Salesforce objects and outputs, and score or validate Data Mapper configurations.
Why use it?
It reduces errors in field mappings and helps review whether existing data configurations are complete and well structured.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to build Extract, Transform, Load, or Turbo Extract mappings, connect fields between Salesforce objects and outputs, and score or validate Data Mapper configurations.

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Install with agentmods
npx agentmods add skills/jaganpro/sf-skills/sf-industry-commoncore-datamapper
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add Jaganpro/sf-skills --skill sf-industry-commoncore-datamapper
Clone the repo
git clone --depth 1 https://github.com/Jaganpro/sf-skills

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for sf-industry-commoncore-datamapper

README.md
[![agentmods](https://agentmods.dev/badge/skills/jaganpro/sf-skills/sf-industry-commoncore-datamapper/github.svg)](https://agentmods.dev/skills/jaganpro/sf-skills/sf-industry-commoncore-datamapper)
Your own site
<a href="https://agentmods.dev/skills/jaganpro/sf-skills/sf-industry-commoncore-datamapper"><img src="https://agentmods.dev/badge/skills/jaganpro/sf-skills/sf-industry-commoncore-datamapper/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for sf-industry-commoncore-datamapper

Your own site · 80×15
<a href="https://agentmods.dev/skills/jaganpro/sf-skills/sf-industry-commoncore-datamapper"><img src="https://agentmods.dev/badge/skills/jaganpro/sf-skills/sf-industry-commoncore-datamapper.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 146 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,963 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • Socket pass 28 Apr 2026
  • Snyk pass 28 Apr 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00146 $0.02963
Opus 5 $0.00073 $0.01482
Sonnet 5 $0.00029 $0.00593
Haiku 4.5 $0.00015 $0.00296

Measured 11d ago against content hash 6251a3191343, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

sf-industry-commoncore-datamapper scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/sf-industry-commoncore-datamapper/SKILL.md · 267 lines

How it starts

The opening of the file, as written. The whole thing — 267 lines — stays where its author put it; the contents beside it link to each section on GitHub.

sf-industry-commoncore-datamapper: OmniStudio Data Mapper Creation and Validation

Expert OmniStudio Data Mapper developer specializing in Extract, Transform, Load, and Turbo Extract configurations. Generate production-ready, performant, and maintainable Data Mapper definitions with proper field mappings, query optimization, and data integrity safeguards.

Core Responsibilities

  1. Generation: Create Data Mapper configurations (Extract, Transform, Load, Turbo Extract) from requirements
  2. Field Mapping: Design object-to-output field mappings with proper type handling, lookup resolution, and null safety
  3. Dependency Tracking: Identify related OmniStudio components (Integration Procedures, OmniScripts, FlexCards) that consume or feed Data Mappers
  4. Validation & Scoring: Score Data Mapper configurations against 5 categories (0-100 points)

CRITICAL: Orchestration Order

sf-industry-commoncore-omnistudio-analyze -> sf-industry-commoncore-datamapper -> sf-industry-commoncore-integration-procedure -> sf-industry-commoncore-omniscript -> sf-industry-commoncore-flexcard (you are here: sf-industry-commoncore-datamapper)

Data Mappers are the data access layer of the OmniStudio stack. They must be created and deployed before Integration Procedures or OmniScripts that reference them. Use sf-industry-commoncore-omnistudio-analyze FIRST to understand existing component dependencies.


Key Insights

Insight Details
Extract vs Turbo Extract Extract uses standard SOQL with relationship queries. Turbo Extract uses server-side compiled queries for read-heavy, high-volume scenarios (10x+ faster). Turbo Extract does not support formula fields, related lists, or write operations.
Transform is in-memory Transform Data Mappers operate entirely in memory with no DML or SOQL. They reshape data structures between steps in an Integration Procedure. Use for JSON-to-JSON transformations, field renaming, and data flattening.
Load = DML Load Data Mappers perform insert, update, upsert, or delete operations. They require proper FLS checks and error handling. Always validate field-level security before deploying Load Data Mappers to production.
OmniDataTransform metadata Data Mappers are stored as OmniDataTransform and OmniDataTransformItem records. Retrieve and deploy using these metadata type names, not the legacy DataRaptor API names.

Read the full file on GitHub · 267 lines

Files

What ships with it

8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 11d ago First seen · 267 lines · 146 tokens per session scan A 6251a3191343

Subscribe to this mod's changes

sf-industry-commoncore-datamapper is a skill published in the GitHub repository Jaganpro/sf-skills (423 stars, last pushed 4mo ago), licensed MIT. It adds 146 tokens to every session and 2,963 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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