sf-datacloud-harmonize

sf-datacloud-harmonize is a skill for Claude Code, Codex from Jaganpro/sf-skills. It costs 110 tokens per session (1,318 once invoked), scanned A, original, MIT.

Salesforce Data Cloud data-modeling and unification work that turns sources into consistent customer or business records. DMOs are Data Model Objects, and identity resolution matches records that refer to the same real-world person or entity.

In plain words
What is it for?
Creating DMOs, mapping fields, defining relationships, configuring identity-resolution rules, building data graphs, managing unified profiles, and looking up universal IDs.
Why use it?
It separates data modeling and record matching from ingestion, audience creation, and querying. This helps build unified profiles, relationships, data graphs, and reliable identifiers.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: reads .claude/ paths.

Good fit Creating DMOs, mapping fields, defining relationships, configuring identity-resolution rules, building data graphs, managing unified profiles, and looking up universal IDs.

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Install with agentmods
npx agentmods add skills/jaganpro/sf-skills/sf-datacloud-harmonize
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-datacloud-harmonize
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-datacloud-harmonize

README.md
[![agentmods](https://agentmods.dev/badge/skills/jaganpro/sf-skills/sf-datacloud-harmonize/github.svg)](https://agentmods.dev/skills/jaganpro/sf-skills/sf-datacloud-harmonize)
Your own site
<a href="https://agentmods.dev/skills/jaganpro/sf-skills/sf-datacloud-harmonize"><img src="https://agentmods.dev/badge/skills/jaganpro/sf-skills/sf-datacloud-harmonize/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-datacloud-harmonize

Your own site · 80×15
<a href="https://agentmods.dev/skills/jaganpro/sf-skills/sf-datacloud-harmonize"><img src="https://agentmods.dev/badge/skills/jaganpro/sf-skills/sf-datacloud-harmonize.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 110 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,318 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 $0.00110 $0.01318
Opus 5 $0.00055 $0.00659
Sonnet 5 $0.00022 $0.00264
Haiku 4.5 $0.00011 $0.00132

Measured 10d ago against content hash 6e8df7b990e8, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

sf-datacloud-harmonize 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 10d 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-datacloud-harmonize/SKILL.md · 126 lines

How it starts

The opening of the file, as written. The whole thing — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.

sf-datacloud-harmonize: Data Cloud Harmonize Phase

Use this skill when the user needs schema harmonization and unification work: DMOs, field mappings, relationships, identity resolution, unified profiles, data graphs, or universal ID lookup.

When This Skill Owns the Task

Use sf-datacloud-harmonize when the work involves:

  • sf data360 dmo *
  • sf data360 identity-resolution *
  • sf data360 data-graph *
  • sf data360 profile *
  • sf data360 universal-id lookup

Delegate elsewhere when the user is:


Required Context to Gather First

Ask for or infer:

  • source DLO and target DMO names
  • whether the task is schema creation, mapping, IR, or graph-related
  • target org alias
  • whether a ruleset already exists
  • the user’s desired unified entity model

Core Operating Rules

  • Inspect DMO schema before creating mappings.
  • Run the shared readiness classifier before mutating harmonization assets: node ~/.claude/skills/sf-datacloud/scripts/diagnose-org.mjs -o <org> --phase harmonize --json.
  • Prefer dmo list --all when browsing the catalog, but use first-page dmo list for fast readiness checks.
  • Use query describe or dmo get --json instead of inventing unsupported describe flows.
  • Treat identity resolution runs as asynchronous and verify results after execution.
  • Keep unified-profile work separate from STDM/session tracing work.

1. Classify readiness for harmonize work

node ~/.claude/skills/sf-datacloud/scripts/diagnose-org.mjs -o <org> --phase harmonize --json

2. Inspect the catalog

sf data360 dmo list --all -o <org> 2>/dev/null
sf data360 identity-resolution list -o <org> 2>/dev/null

Read the full file on GitHub · 126 lines

Files

What ships with it

3 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. 10d ago First seen · 126 lines · 110 tokens per session scan A 6e8df7b990e8

Subscribe to this mod's changes

sf-datacloud-harmonize is a skill published in the GitHub repository Jaganpro/sf-skills (423 stars, last pushed 4mo ago), licensed MIT. It adds 110 tokens to every session and 1,318 once invoked, about $0.0006 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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