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-actgit 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-act)<a href="https://agentmods.dev/skills/jaganpro/sf-skills/sf-datacloud-act"><img src="https://agentmods.dev/badge/skills/jaganpro/sf-skills/sf-datacloud-act/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-act"><img src="https://agentmods.dev/badge/skills/jaganpro/sf-skills/sf-datacloud-act.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 | $0.00089 | $0.01227 |
| Opus 5 | $0.00044 | $0.00613 |
| Sonnet 5 | $0.00018 | $0.00245 |
| Haiku 4.5 | $0.00009 | $0.00123 |
Grade A, and why
sf-datacloud-act 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 — 127 lines — stays where its author put it; the contents beside it link to each section on GitHub.
sf-datacloud-act: Data Cloud Act Phase
Use this skill when the user needs downstream delivery work: activations, activation targets, data actions, or pushing Data Cloud outputs into other systems.
When This Skill Owns the Task
Use sf-datacloud-act when the work involves:
sf data360 activation *sf data360 activation-target *sf data360 data-action *sf data360 data-action-target *- verifying downstream delivery setup
Delegate elsewhere when the user is:
- still building the audience or insight → sf-datacloud-segment
- exploring query/search or search indexes → sf-datacloud-retrieve
- setting up base connections or ingestion → sf-datacloud-connect, sf-datacloud-prepare
Required Context to Gather First
Ask for or infer:
- target org alias
- destination platform or downstream system
- whether the segment already exists and is published
- whether the user needs create, inspect, update, or delete
- whether the task is activation-focused or data-action-focused
Core Operating Rules
- Verify the upstream segment or insight is healthy before creating downstream delivery assets.
- Run the shared readiness classifier before mutating activation assets:
node ~/.claude/skills/sf-datacloud/scripts/diagnose-org.mjs -o <org> --phase act --json. - Inspect available platforms and targets before mutating activation setup.
- Keep destination definitions deterministic and reusable where possible.
- Treat downstream credential and platform constraints as separate validation concerns.
- Prefer read-only inspection first when the destination state is unclear.
Recommended Workflow
1. Classify readiness for act work
node ~/.claude/skills/sf-datacloud/scripts/diagnose-org.mjs -o <org> --phase act --json
2. Inspect destinations first
sf data360 activation platforms -o <org> 2>/dev/null
sf data360 activation-target list -o <org> 2>/dev/null
sf data360 data-action-target 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.
- 12d ago First seen · 127 lines · 89 tokens per session scan A 29486c24969d
sf-datacloud-act is a skill published in the GitHub repository Jaganpro/sf-skills (423 stars, last pushed 4mo ago), licensed MIT. It adds 89 tokens to every session and 1,227 once invoked, about $0.0004 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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