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-datacloud-harmonizegit 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-datacloud-harmonize)<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.
<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>- 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 | $0.00110 | $0.01318 |
| Opus 5 | $0.00055 | $0.00659 |
| Sonnet 5 | $0.00022 | $0.00264 |
| Haiku 4.5 | $0.00011 | $0.00132 |
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.
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:
- still ingesting streams or building DLOs → sf-datacloud-prepare
- working on segment logic or calculated insights → sf-datacloud-segment
- running SQL, describe, or search-index workflows → sf-datacloud-retrieve
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 --allwhen browsing the catalog, but use first-pagedmo listfor fast readiness checks. - Use
query describeordmo get --jsoninstead 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.
Recommended Workflow
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
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.
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.
- 10d ago First seen · 126 lines · 110 tokens per session scan A 6e8df7b990e8
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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