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 divingsbysangam/salesforce-compound-engineering-plugin --skill validation-rule-generategit clone --depth 1 https://github.com/divingsbysangam/salesforce-compound-engineering-pluginWrote 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/divingsbysangam/salesforce-compound-engineering-plugin/validation-rule-generate)<a href="https://agentmods.dev/skills/divingsbysangam/salesforce-compound-engineering-plugin/validation-rule-generate"><img src="https://agentmods.dev/badge/skills/divingsbysangam/salesforce-compound-engineering-plugin/validation-rule-generate.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00145 | $0.01330 |
| Opus 5 | $0.00072 | $0.00665 |
| Sonnet 5 | $0.00029 | $0.00266 |
| Haiku 4.5 | $0.00015 | $0.00133 |
Grade A, and why
validation-rule-generate 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 4d 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.
This is a copy
97% identical to agentforce-test — 59 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 50 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/validation-rule-generate
Principles enforced: 1 (preserve the quality ceiling), 5 (taste over typing). See
PRINCIPLES.md.
Required reads
Procedure lives in sibling files, not only in this orchestrator:
-
When to use — read
references/when-to-use.mdbefore acting on this section. -
Required properties — read
references/required-properties.mdbefore acting on this section. -
Hard constraints — read
references/hard-constraints.mdbefore acting on this section. -
Step 0: Research (Principle 7) — read
references/step-0-research-principle-7.mdbefore acting on this section. -
Workflow — read
references/workflow.mdbefore acting on this section.
What ships with it
7 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.
- 4d ago First seen · 50 lines · 145 tokens per session scan A e3dc0a8e41c3
validation-rule-generate is a skill published in the GitHub repository divingsbysangam/salesforce-compound-engineering-plugin (10 stars, last pushed 3d ago), licensed MIT. It adds 145 tokens to every session and 1,330 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. It is 97% identical to agentforce-test, differing in 59 lines, and is treated as a copy.
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