sf-optimize

sf-optimize is a skill for Claude Code, Codex from divingsbysangam/salesforce-compound-engineering-plugin. It costs 80 tokens per session (553 once invoked), scanned A, original, MIT.

A measurement-based workflow for improving Salesforce performance, where Salesforce is a cloud platform for business applications. It repeatedly measures a target, tests possible changes, and keeps improvements that meet the goal.

In plain words
What is it for?
Use it to reduce Apex CPU time, SOQL or DML counts, improve query selectivity, increase limit headroom, shrink LWC bundles, or improve page-load performance.
Why use it?
It replaces guesswork with evidence when code is slow, uses too many Salesforce limits, or produces an oversized user-interface bundle.

Skill for Claude CodeCodex

Part of the sf-compound-engineering plugin — 68 skills, 2 agents, 1 hook, 2 MCP servers shipped together

Install

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.

agentmods
npx agentmods add skills/divingsbysangam/salesforce-compound-engineering-plugin/sf-optimize
Any agent
npx skills add divingsbysangam/salesforce-compound-engineering-plugin --skill sf-optimize
Clone the repo
git clone --depth 1 https://github.com/divingsbysangam/salesforce-compound-engineering-plugin

Made for: Claude Code, Codex.

Or install sf-compound-engineering, the plugin that ships this one along with the rest of its 68 skills, 2 agents, 1 hook, 2 MCP servers.

Wrote 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.

agentmods badge for sf-optimize

README.md
[![agentmods](https://agentmods.dev/badge/skills/divingsbysangam/salesforce-compound-engineering-plugin/sf-optimize.svg)](https://agentmods.dev/skills/divingsbysangam/salesforce-compound-engineering-plugin/sf-optimize)
Your own site
<a href="https://agentmods.dev/skills/divingsbysangam/salesforce-compound-engineering-plugin/sf-optimize"><img src="https://agentmods.dev/badge/skills/divingsbysangam/salesforce-compound-engineering-plugin/sf-optimize.svg" alt="Measured on agentmods" height="20"></a>
Per session 80 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 553 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00080 $0.00553
Opus 5 $0.00040 $0.00277
Sonnet 5 $0.00016 $0.00111
Haiku 4.5 $0.00008 $0.00055

Measured 5d ago against content hash 40ed810d6906, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

sf-optimize 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 5d 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.

skills/sf-optimize/SKILL.md · 38 lines

How it starts

The opening of the file, as written. The whole thing — 38 lines — stays where its author put it; the contents beside it link to each section on GitHub.

sf-optimize

Systematic experimentation loop: measure → propose → run experiments in parallel → keep improvements → converge.

<feature_description> #$ARGUMENTS </feature_description>

Salesforce Angle

  • Salesforce metrics: Apex CPU time, heap size, SOQL count, DML count, query selectivity (filterable indexes hit), LWC bundle size, p95 page load.
  • Measurement scaffolding uses Limits.getCpuTime(), Limits.getQueries(), debug logs, the SOQL Query Plan tool, and the LWC Lighthouse profile.

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:

  1. Understand the input — read the <feature_description> block above and any referenced files, plans, or issues.
  2. Plan a small set of phases — break the work into 2-5 ordered steps that an implementer (or another skill) can verify.
  3. 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.
  4. Use Salesforce-aware contexts and commands — file paths under force-app/main/default/..., test commands like sf apex run test, deploy commands like sf project deploy validate and sf project deploy start, query the org with sf data query when state inspection is needed.
  5. 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.
  • Salesforce knowledge: docs/solutions/ (search via the sf-learnings-researcher agent).
  • Plugin conventions: see CLAUDE.md for frontmatter, naming, and protected-artifact rules.

Read the full file on GitHub · 38 lines

Changes

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.

  1. 5d ago First seen · 38 lines · 80 tokens per session scan A 40ed810d6906

Subscribe to this mod's changes

sf-optimize is a skill published in the GitHub repository divingsbysangam/salesforce-compound-engineering-plugin (10 stars, last pushed yesterday), licensed MIT. It adds 80 tokens to every session and 553 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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