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Agent Skill · 开发

sf-industry-commoncore-datamapper(SF 行业 Commoncore 数据映射器)

用于辅助数据整理、表格处理、CSV/Excel 分析、指标计算和图表准备。它适合让 Agent 清洗字段、汇总数据、发现异常、生成统计口径或把分析结果转成可读说明。使用时需要确认数据来源、字段含义和时间范围,避免把样本数据当全量事实;涉及敏感数据、导出文件或批量写回时,应先确认权限和脱敏边界。

sf-industry-commoncore-datamapper 是开发类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为需要联网;许可证信息还不明确,第三方扫描已通过,按安装量在开发类的 21,746 个 Skill 里排第 2,337。

总安装 18,121周安装 726GitHub Stars 401下载 5,866核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:sf-industry-commoncore-datamapper(SF 行业 Commoncore 数据映射器)
来源仓库:https://github.com/jaganpro/sf-skills
仓库路径:skills/sf-industry-commoncore-datamapper
安装命令:
npx skills add https://github.com/jaganpro/sf-skills --skill sf-industry-commoncore-datamapper
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/jaganpro/sf-skills --skill sf-industry-commoncore-datamapper

这个 Skill 是做什么的

开发类 Skill 面向写代码、改代码和排查问题,一般会读写仓库文件,部分还会执行构建或测试命令。

用于辅助数据整理、表格处理、CSV/Excel 分析和指标计算。适合清洗字段、汇总数据、发现异常或生成统计口径说明。使用时需确认数据来源、字段含义和时间范围,避免误用样本当全量。涉及敏感数据或导出文件时,应先确认权限和脱敏边界。

sf-industry-commoncore-datamapper 属于开发类 Skill,可作为该场景下的辅助能力补充。安装方式:通过 npx 从指定 GitHub 仓库添加技能。

来源仓库内路径:skills/sf-industry-commoncore-datamapper

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/jaganpro/sf-skills --skill sf-industry-commonc…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

sf-industry-commoncore-datamapper 怎么安装?

在终端执行 npx skills add https://github.com/jaganpro/sf-skills --skill sf-industry-commoncore-datama…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

sf-industry-commoncore-datamapper 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

sf-industry-commoncore-datamapper 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

sf-industry-commoncore-datamapper 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

sf-industry-commoncore-datamapper 用的人多吗?

公开数据是累计安装 18,121 次,近一周安装 726 次,下载 5,866 次,GitHub 401 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

sf-industry-commoncore-datamapper 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理开发类任务、又不想每次都手写提示词的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

开发类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
skill-vetter

技能审查员 · OpenClaw

8,779,018986操作浏览器MIT-0通过
ontology

本体论 · OpenClaw

6,636,960576只读MIT-0通过
entra-app-registration

内部应用程序注册 · Codex、Claude

6,631,872764接触敏感数据未明确通过
entra-app-registration

内部应用程序注册 · Codex、Claude

3,757,680201接触敏感数据未明确通过
nano-banana-pro

纳米香蕉专业版 · OpenClaw

3,526,898364接触敏感数据MIT-0有提醒
sf-industry-commoncore-datamapper

SF 行业 Commoncore 数据映射器 · 本页

18,121401需要联网未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

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

InsightDetails
Extract vs Turbo ExtractExtract 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-memoryTransform 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 = DMLLoad 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 metadataData Mappers are stored as OmniDataTransform and OmniDataTransformItem records. Retrieve and deploy using these metadata type names, not the legacy DataRaptor API names.

Workflow (5-Phase Pattern)

Phase 1: Requirements Gathering

Ask the user to gather:

  • Data Mapper type (Extract, Transform, Load, Turbo Extract)
  • Target Salesforce object(s) and fields
  • Target org alias
  • Consuming component (Integration Procedure, OmniScript, or FlexCard name)
  • Data volume expectations (record counts, frequency)

Then:

  1. Check existing Data Mappers: Glob: **/OmniDataTransform*
  2. Check existing OmniStudio metadata: Glob: **/omnistudio/**
  3. Create a task list

Phase 2: Design & Type Selection

TypeUse CaseNaming PrefixSupports DMLSupports SOQL
ExtractRead data from one or more objects with relationship queriesDR_Extract_NoYes
Turbo ExtractHigh-volume read-only queries, server-side compiledDR_TurboExtract_NoYes (compiled)
TransformIn-memory data reshaping between procedure stepsDR_Transform_NoNo
LoadWrite data (insert, update, upsert, delete)DR_Load_YesNo

Naming Format: [Prefix][Object]_[Purpose] using PascalCase

Examples:

  • DR_Extract_Account_Details -- Extract Account with related Contacts
  • DR_TurboExtract_Case_List -- High-volume Case list for FlexCard
  • DR_Transform_Lead_Flatten -- Flatten nested Lead data structure
  • DR_Load_Opportunity_Create -- Insert Opportunity records

Phase 3: Generation & Validation

For Generation:

  1. Define the OmniDataTransform record (Name, Type, Active status)
  2. Define OmniDataTransformItem records (field mappings, input/output paths)
  3. Configure query filters, sort order, and limits for Extract types
  4. Set up lookup mappings and default values for Load types
  5. Validate field-level security for all mapped fields

For Review:

  1. Read existing Data Mapper configuration
  2. Run validation against best practices
  3. Generate improvement report with specific fixes

Run Validation:

Score: XX/100 Rating
|- Design & Naming: XX/20
|- Field Mapping: XX/25
|- Data Integrity: XX/25
|- Performance: XX/15
|- Documentation: XX/15

Generation Guardrails (MANDATORY)

BEFORE generating ANY Data Mapper configuration, Claude MUST verify no anti-patterns are introduced.

If ANY of these patterns would be generated, STOP and ask the user:

"I noticed [pattern]. This will cause [problem]. Should I: A) Refactor to use [correct pattern] B) Proceed anyway (not recommended)"
Anti-PatternDetectionImpact
Extracting all fieldsNo field list specified, wildcard selectionPerformance degradation, excessive data transfer
Missing lookup mappingsLoad references lookup field without resolutionDML failure, null foreign key
Writing without FLS checkLoad Data Mapper with no security validationSecurity violation, data corruption in restricted profiles
Unbounded Extract queryNo LIMIT or filter on ExtractGovernor limit failure, timeout on large objects
Transform with side effectsTransform attempting DML or calloutRuntime error, Transform is in-memory only
Hardcoded record IDs15/18-char ID literal in filter or mappingDeployment failure across environments
Nested relationship depth >3Extract with deeply nested parent traversalQuery performance degradation, SOQL complexity limits
Load without error handlingNo upsert key or duplicate rule considerationSilent data corruption, duplicate records

DO NOT generate anti-patterns even if explicitly requested. Ask user to confirm the exception with documented justification.

See: references/best-practices.md for detailed patterns See: references/naming-conventions.md for naming rules


Phase 4: Deployment

Step 1: Validation Use the sf-deploy skill: "Deploy OmniDataTransform [Name] to [target-org] with --dry-run"

Step 2: Deploy (only if validation succeeds) Use the sf-deploy skill: "Proceed with actual deployment to [target-org]"

Post-Deploy: Activate the Data Mapper in the target org. Verify it appears in OmniStudio Designer.


Phase 5: Testing & Documentation

Completion Summary:

Data Mapper Complete: [Name]
  Type: [Extract|Transform|Load|Turbo Extract]
  Target Object(s): [Object1, Object2]
  Field Count: [N mapped fields]
  Validation: PASSED (Score: XX/100)

Next Steps: Test in Integration Procedure, verify data output, monitor performance

Testing Checklist:

  • Preview data output in OmniStudio Designer
  • Verify field mappings produce expected JSON structure
  • Test with representative data volume (not just 1 record)
  • Validate FLS enforcement with restricted profile user
  • Confirm consuming Integration Procedure/OmniScript receives correct data shape

Best Practices (100-Point Scoring)

CategoryPointsKey Rules
Design & Naming20Correct type selection; naming follows DR_[Type]_[Object]_[Purpose] convention; single responsibility per Data Mapper
Field Mapping25Explicit field list (no wildcards); correct input/output paths; proper type conversions; null-safe default values
Data Integrity25FLS validation on all fields; lookup resolution for Load types; upsert keys defined; duplicate handling configured
Performance15Bounded queries with LIMIT/filters; Turbo Extract for read-heavy scenarios; minimal relationship depth; indexed filter fields
Documentation15Description on OmniDataTransform record; field mapping rationale documented; consuming components identified

Thresholds: ✅ 90+ (Deploy) | ⚠️ 67-89 (Review) | ❌ <67 (Block - fix required)


CLI Commands

Query Existing Data Mappers

sf data query -q "SELECT Id,Name,Type FROM OmniDataTransform" -o <org>

Query Data Mapper Field Mappings

sf data query -q "SELECT Id,Name,InputObjectName,OutputObjectName,LookupObjectName FROM OmniDataTransformItem WHERE OmniDataTransformationId='<id>'" -o <org>

Retrieve Data Mapper Metadata

sf project retrieve start -m OmniDataTransform:<Name> -o <org>

Deploy Data Mapper Metadata

sf project deploy start -m OmniDataTransform:<Name> -o <org>

Cross-Skill Integration

From SkillTo sf-industry-commoncore-datamapperWhen
sf-industry-commoncore-omnistudio-analyze-> sf-industry-commoncore-datamapper"Analyze dependencies before creating Data Mapper"
sf-metadata-> sf-industry-commoncore-datamapper"Describe target object fields before mapping"
sf-soql-> sf-industry-commoncore-datamapper"Validate Extract query logic"
From sf-industry-commoncore-datamapperTo SkillWhen
sf-industry-commoncore-datamapper-> sf-industry-commoncore-integration-procedure"Create Integration Procedure that calls this Data Mapper"
sf-industry-commoncore-datamapper-> sf-deploy"Deploy Data Mapper to target org"
sf-industry-commoncore-datamapper-> sf-industry-commoncore-omniscript"Wire Data Mapper output into OmniScript"
sf-industry-commoncore-datamapper-> sf-industry-commoncore-flexcard"Display Data Mapper Extract results in FlexCard"

Edge Cases

ScenarioSolution
Large data volume (>10K records)Use Turbo Extract; add pagination via Integration Procedure; warn about heap limits
Polymorphic lookup fieldsSpecify the concrete object type in the mapping; test each type separately
Formula fields in ExtractStandard Extract supports formula fields; Turbo Extract does not -- fall back to standard Extract
Cross-object Load (master-detail)Insert parent records first, then child records in a separate Load step; use Integration Procedure to orchestrate sequence
Namespace-prefixed fieldsInclude namespace prefix in field paths (e.g., ns__Field__c); verify prefix matches target org
Multi-currency orgsMap CurrencyIsoCode explicitly; do not rely on default currency assumption
RecordType-dependent mappingsFilter by RecordType in Extract; set RecordTypeId in Load; document which RecordTypes are supported

Notes

  • Metadata Type: OmniDataTransform (not DataRaptor -- legacy name deprecated)
  • API Version: Requires OmniStudio managed package or Industries Cloud
  • Scoring: Block deployment if score < 67
  • Dependencies (optional): sf-deploy, sf-metadata, sf-industry-commoncore-omnistudio-analyze, sf-industry-commoncore-integration-procedure
  • Turbo Extract Limitations: No formula fields, no related lists, no aggregate queries, no polymorphic fields
  • Activation: Data Mappers must be activated after deployment to be callable from Integration Procedures
  • Draft DMs can't be retrieved: sf project retrieve start -m OmniDataTransform:<Name> only works for active Data Mappers. Draft DMs return "Entity cannot be found".
  • Creating via Data API: Use sf api request rest --method POST --body @file.json to create OmniDataTransform and OmniDataTransformItem records. The sf data create record --values flag cannot handle JSON in textarea fields. Write the JSON body to a temp file first.
  • Foreign key field name: The parent lookup on OmniDataTransformItem is OmniDataTransformationId (full word "Transformation"), not OmniDataTransformId.

License

MIT License. Copyright (c) 2026 David Ryan (weytani)

平台分布

Codex

35.92%
按下载量换算2,107

Claude

29.93%
按下载量换算1,756

Cursor

20%
按下载量换算1,173

Gemini CLI

8.43%
按下载量换算495

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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