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-segmentgit 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-segment)<a href="https://agentmods.dev/skills/jaganpro/sf-skills/sf-datacloud-segment"><img src="https://agentmods.dev/badge/skills/jaganpro/sf-skills/sf-datacloud-segment/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-segment"><img src="https://agentmods.dev/badge/skills/jaganpro/sf-skills/sf-datacloud-segment.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 | $0.00114 | $0.01172 |
| Opus 5 | $0.00057 | $0.00586 |
| Sonnet 5 | $0.00023 | $0.00234 |
| Haiku 4.5 | $0.00011 | $0.00117 |
Grade A, and why
sf-datacloud-segment 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 — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.
sf-datacloud-segment: Data Cloud Segment Phase
Use this skill when the user needs audience and insight work: segments, calculated insights, publish workflows, member counts, or troubleshooting Data Cloud segment SQL.
When This Skill Owns the Task
Use sf-datacloud-segment when the work involves:
sf data360 segment *sf data360 calculated-insight *- segment publish workflows
- member counts and segment troubleshooting
- calculated insight execution and verification
Delegate elsewhere when the user is:
- still building DMOs, mappings, or identity resolution → sf-datacloud-harmonize
- activating a segment downstream → sf-datacloud-act
- writing read-only SQL or search-index queries → sf-datacloud-retrieve
Required Context to Gather First
Ask for or infer:
- target org alias
- unified DMO or base entity name
- whether the user wants create, publish, inspect, or troubleshoot
- whether the asset is a segment or calculated insight
- expected success metric: member count, aggregate value, or publish status
Core Operating Rules
- Treat Data Cloud segment SQL as distinct from CRM SOQL.
- Run the shared readiness classifier before mutating audience assets:
node ~/.claude/skills/sf-datacloud/scripts/diagnose-org.mjs -o <org> --phase segment --json. - Prefer reusable JSON definitions for repeatable segment and CI creation.
- Use
--api-version 64.0when segment creation behavior is unstable on newer defaults. - Verify with counts or SQL after publish/run steps instead of assuming success.
- Use SQL joins rather than
segment memberswhen readable member details are needed.
Recommended Workflow
1. Classify readiness for segment work
node ~/.claude/skills/sf-datacloud/scripts/diagnose-org.mjs -o <org> --phase segment --json
2. Inspect current state
sf data360 segment list -o <org> 2>/dev/null
sf data360 calculated-insight 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 · 124 lines · 114 tokens per session scan A a69cbe8e1aef
sf-datacloud-segment is a skill published in the GitHub repository Jaganpro/sf-skills (423 stars, last pushed 4mo ago), licensed MIT. It adds 114 tokens to every session and 1,172 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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