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-agent-native-architecturegit 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-agent-native-architecture)<a href="https://agentmods.dev/skills/divingsbysangam/salesforce-compound-engineering-plugin/sf-agent-native-architecture"><img src="https://agentmods.dev/badge/skills/divingsbysangam/salesforce-compound-engineering-plugin/sf-agent-native-architecture/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-agent-native-architecture"><img src="https://agentmods.dev/badge/skills/divingsbysangam/salesforce-compound-engineering-plugin/sf-agent-native-architecture.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 | $0.00075 | $0.00534 |
| Opus 5 | $0.00037 | $0.00267 |
| Sonnet 5 | $0.00015 | $0.00107 |
| Haiku 4.5 | $0.00007 | $0.00053 |
Grade A, and why
sf-agent-native-architecture scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 40 lines — stays where its author put it; the contents beside it link to each section on GitHub.
sf-agent-native-architecture
Apply agent-native design principles to Salesforce features: every human-affordance has an agent-affordance.
<feature_description> #$ARGUMENTS </feature_description>
Salesforce Angle
- Setup actions → Metadata API + Tooling API + sf CLI equivalents.
- UI buttons → Flow
Actionor Apex@InvocableMethodexposed via Hosted MCP server. - Custom field changes → Tooling API CustomField operations.
- Reports → Reports REST API.
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.
Related
- Salesforce knowledge:
docs/solutions/(search via thesf-learnings-researcheragent). - Plugin conventions: see
CLAUDE.mdfor frontmatter, naming, and protected-artifact rules.
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.
- 9d ago First seen · 40 lines · 75 tokens per session scan A 4516fb77a4d4
sf-agent-native-architecture is a skill published in the GitHub repository divingsbysangam/salesforce-compound-engineering-plugin (10 stars, last pushed 5d ago), licensed MIT. It adds 75 tokens to every session and 534 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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