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-preparegit 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-prepare)<a href="https://agentmods.dev/skills/jaganpro/sf-skills/sf-datacloud-prepare"><img src="https://agentmods.dev/badge/skills/jaganpro/sf-skills/sf-datacloud-prepare/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-prepare"><img src="https://agentmods.dev/badge/skills/jaganpro/sf-skills/sf-datacloud-prepare.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket warn
- 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 | $0.00098 | $0.02031 |
| Opus 5 | $0.00049 | $0.01015 |
| Sonnet 5 | $0.00020 | $0.00406 |
| Haiku 4.5 | $0.00010 | $0.00203 |
Grade A, and why
sf-datacloud-prepare 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 12d 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 — 199 lines — stays where its author put it; the contents beside it link to each section on GitHub.
sf-datacloud-prepare: Data Cloud Prepare Phase
Use this skill when the user needs ingestion and lake preparation work: data streams, Data Lake Objects (DLOs), transforms, Document AI, unstructured ingestion, or the handoff from connector setup into a live stream.
When This Skill Owns the Task
Use sf-datacloud-prepare when the work involves:
sf data360 data-stream *sf data360 dlo *sf data360 transform *sf data360 docai *- choosing how data should enter Data Cloud
- rerunning or rescanning ingestion after a source update
- preparing Ingestion API-backed streams after connector setup is complete
Delegate elsewhere when the user is:
- still creating/testing source connections → sf-datacloud-connect
- mapping to DMOs or designing IR/data graphs → sf-datacloud-harmonize
- querying ingested data → sf-datacloud-retrieve
Required Context to Gather First
Ask for or infer:
- target org alias
- source connection name
- source object / dataset / document source
- desired stream type
- DLO naming expectations
- whether the user is creating, updating, running, or deleting a stream
- whether the source is CRM, a database connector, an unstructured file source, or an Ingestion API feed
Core Operating Rules
- Verify the external plugin runtime before running Data Cloud commands.
- Run the shared readiness classifier before mutating ingestion assets:
node ~/.claude/skills/sf-datacloud/scripts/diagnose-org.mjs -o <org> --phase prepare --json. - Prefer inspecting existing streams and DLOs before creating new ingestion assets.
- Suppress linked-plugin warning noise with
2>/dev/nullfor normal usage. - Treat DLO naming and field naming as Data Cloud-specific, not CRM-native.
- Confirm whether each dataset should be treated as
Profile,Engagement, orOtherbefore creating the stream. - Distinguish stream-level refresh from connection-level reruns when working with unstructured sources.
- Use UI setup intentionally when initial stream or unstructured asset creation is platform-gated.
- Hand off to Harmonize only after ingestion assets are clearly healthy.
What ships with it
6 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.
- 12d ago First seen · 199 lines · 98 tokens per session scan A 7961833585c5
sf-datacloud-prepare is a skill published in the GitHub repository Jaganpro/sf-skills (423 stars, last pushed 4mo ago), licensed MIT. It adds 98 tokens to every session and 2,031 once invoked, about $0.0005 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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