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 sf-pr-descriptiongit 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/sf-pr-description)<a href="https://agentmods.dev/skills/divingsbysangam/salesforce-compound-engineering-plugin/sf-pr-description"><img src="https://agentmods.dev/badge/skills/divingsbysangam/salesforce-compound-engineering-plugin/sf-pr-description/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/divingsbysangam/salesforce-compound-engineering-plugin/sf-pr-description"><img src="https://agentmods.dev/badge/skills/divingsbysangam/salesforce-compound-engineering-plugin/sf-pr-description.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00085 | $0.00640 |
| Opus 5 | $0.00043 | $0.00320 |
| Sonnet 5 | $0.00017 | $0.00128 |
| Haiku 4.5 | $0.00009 | $0.00064 |
Grade A, and why
sf-pr-description scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- sf-session-inventory — 86% identical, 15 lines differ
How it starts
The opening of the file, as written. The whole thing — 40 lines — stays where its author put it; the contents beside it link to each section on GitHub.
sf-pr-description
Generate a Salesforce-aware pull request description that scales with change complexity: a focused trigger fix gets a short body; a metadata bundle plus Apex plus LWC plus permission changes gets sectioned with deploy plan and post-deploy verification.
<feature_description> #$ARGUMENTS </feature_description>
Salesforce Angle
- Scope summary: explicit list of file types touched (
force-app/main/default/classes/,lwc/,flows/,objects/,permissionsets/,profiles/). - Deploy plan: target environment(s), validate vs. quick-deploy, dependencies on prior deploys, package.xml or destructive changes.
- Test plan: Apex test classes affected, coverage delta, sharing scenarios exercised, mock callouts, manual UI verification needs.
- Risk callouts: governor-limit headroom changes, sharing/visibility changes, integration contract changes, permission set delta.
Interaction Method
When asking the user a question, use the platform's blocking question tool (AskUserQuestion in Claude Code, request_user_input in Codex, ask_user in Gemini). Fall back to numbered options in chat when no blocking tool is available. Ask one question at a time. Prefer concise single-select choices when natural options exist.
Procedure
This skill follows the standard sf-compound-engineering execution discipline:
- Understand the input — read the
<feature_description>block above and any referenced files, plans, or issues. - Plan a small set of phases — break the work into 2-5 ordered steps that an implementer (or another skill) can verify.
- Apply the Salesforce Angle notes above — these encode the platform-specific considerations (governor limits, sharing context, deploy ordering, FLS, metadata semantics) that distinguish this skill from generic counterparts.
- Use Salesforce-aware contexts and commands — file paths under
force-app/main/default/..., test commands likesf apex run test, deploy commands likesf project deploy validateandsf project deploy start, query the org withsf data querywhen state inspection is needed. - Surface decisions back to the user — when a step requires a choice that materially affects scope or risk, ask using the platform's blocking question tool rather than guessing.
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.
- 8d ago First seen · 40 lines · 85 tokens per session scan A c889bb2d3e13
sf-pr-description is a skill published in the GitHub repository divingsbysangam/salesforce-compound-engineering-plugin (10 stars, last pushed 4d ago), licensed MIT. It adds 85 tokens to every session and 640 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 2026-08-31.
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