Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/divingsbysangam/salesforce-compound-engineering-pluginnpx agentmods add skills/divingsbysangam/salesforce-compound-engineering-plugin/sf-deepenWrote 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-deepen)<a href="https://agentmods.dev/skills/divingsbysangam/salesforce-compound-engineering-plugin/sf-deepen"><img src="https://agentmods.dev/badge/skills/divingsbysangam/salesforce-compound-engineering-plugin/sf-deepen/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-deepen"><img src="https://agentmods.dev/badge/skills/divingsbysangam/salesforce-compound-engineering-plugin/sf-deepen.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector 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 | $0.00080 | $0.01196 |
| Opus 5 | $0.00040 | $0.00598 |
| Sonnet 5 | $0.00016 | $0.00239 |
| Haiku 4.5 | $0.00008 | $0.00120 |
Grade A, and why
sf-deepen 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.
How it starts
The opening of the file, as written. The whole thing — 166 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/sf-deepen
Persona dispatch. This skill dispatches personas as isolated subagents — see the
dispatching-parallel-personasskill for the mechanics (isolated subagents, same-response parallelism, same-file-conflict check). This skill's research personas are referenced from../sf-plan/references/personas/.
Principles enforced: 4 (spec is the artifact), 2 (verifiability). See
PRINCIPLES.md.
Copy-paste-to-agent
Tighten an existing Salesforce plan into a load-bearing spec. This is NOT a research dump
— it is a spec-tightening pass. For each major plan section, dispatch the appropriate
research agent in parallel, then merge findings into the plan AS spec constraints (governor
caps, sharing-model implications, order-of-execution placement, API-version dependencies),
not as appended prose. If the plan's Verification Strategy from /sf-plan is incomplete or
hand-waved, fill the five fields concretely before returning.
Relationship to /sf-plan
/sf-plan produces the spec. /sf-deepen tightens it. The split is:
-
/sf-plan— runs once, produces the initial spec/plan/verification/tasks artifacts via parallel research. -
/sf-deepen— runs against an existing plan file, adds Salesforce-specific depth (governor analysis, sharing impact, order-of-execution placement, known issues) and concretes any vague verification fields.
If you just created a plan with /sf-plan and want it deeper, run /sf-deepen <plan_path>. Don't run /sf-plan twice on the same feature.
<feature_description> #$ARGUMENTS </feature_description>
Interaction Method
When asking the user a question, use the platform's blocking question tool: AskUserQuestion in Claude Code (call ToolSearch with select:AskUserQuestion first if its schema isn't loaded), request_user_input in Codex, ask_user in Gemini. Fall back to numbered options in chat only when no blocking tool exists in the harness or the call errors. Never silently skip the question.
Ask one question at a time. Prefer a concise single-select choice when natural options exist.
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 · 166 lines · 80 tokens per session scan A 4c9b086d4cad
sf-deepen is a skill published in the GitHub repository divingsbysangam/salesforce-compound-engineering-plugin (10 stars, last pushed 4d ago), licensed MIT. It adds 80 tokens to every session and 1,196 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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