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.
npx skills add Jaganpro/sf-skills --skill sf-industry-commoncore-datamappergit clone --depth 1 https://github.com/Jaganpro/sf-skillsWrote 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.
[](https://agentmods.dev/skills/jaganpro/sf-skills/sf-industry-commoncore-datamapper)<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.
<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>- Socket pass
- Snyk pass
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.
| Model | Per session | Once 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 |
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.
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
- Generation: Create Data Mapper configurations (Extract, Transform, Load, Turbo Extract) from requirements
- Field Mapping: Design object-to-output field mappings with proper type handling, lookup resolution, and null safety
- Dependency Tracking: Identify related OmniStudio components (Integration Procedures, OmniScripts, FlexCards) that consume or feed Data Mappers
- 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. |
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.
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.
- 11d ago First seen · 267 lines · 146 tokens per session scan A 6251a3191343
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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