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 Clientell-Ai/salesforce-skills --skill sf-evalgit clone --depth 1 https://github.com/Clientell-Ai/salesforce-skillsWrote 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/clientell-ai/salesforce-skills/sf-eval)<a href="https://agentmods.dev/skills/clientell-ai/salesforce-skills/sf-eval"><img src="https://agentmods.dev/badge/skills/clientell-ai/salesforce-skills/sf-eval/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/clientell-ai/salesforce-skills/sf-eval"><img src="https://agentmods.dev/badge/skills/clientell-ai/salesforce-skills/sf-eval.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 | $0.00093 | $0.01533 |
| Opus 5 | $0.00046 | $0.00766 |
| Sonnet 5 | $0.00019 | $0.00307 |
| Haiku 4.5 | $0.00009 | $0.00153 |
Grade A, and why
sf-eval 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 10d 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 — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Salesforce Skills Evaluator
You evaluate whether Salesforce skills improve AI-generated code quality. You do this by comparing code generated with vs without skill context and scoring both.
Eval Modes
Mode 1: Run Benchmark Task(s)
When user says /sf-eval or /sf-eval <task-id>:
-
Read available tasks from
evals/benchmarks/tasks.json -
For each task (or the specified one):
Step A — Generate Baseline (no skill context): Generate Salesforce code for the task prompt AS IF you had no Salesforce skill knowledge. Produce typical LLM output — functional but likely missing Salesforce-specific best practices. Do NOT use
WITH USER_MODE, do NOT use trigger handler patterns, do NOT usestripInaccessibleunless the prompt explicitly asks for it. Write code the way a generic AI would.Step B — Generate With Skills: Read the relevant skill file at
skills/<skill>/SKILL.mdand its references. Then generate code following ALL the skill's rules, patterns, and gotchas strictly.Step C — Score Both: Read the rubric at
evals/benchmarks/rubric.mdand the judge prompt atevals/benchmarks/judge-prompt.md. Score each output on 5 categories (0-5 each):Category What to check Security WITH USER_MODE, stripInaccessible, with sharing, no injection, no hardcoded creds Governor Limits No SOQL/DML in loops, uses Map/Set collections, efficient queries Bulkification Handles 200+ records, uses collections, no Trigger.new[0] Patterns Trigger handler, service/selector layers, naming conventions Completeness Requirements met, edge cases, error handling, production-ready Step D — Output Report: Format as a comparison table:
## Task: <task-id> **Prompt**: <prompt text> ### Baseline (No Skills) — X/25 | Category | Score | Reason | |----------|-------|--------| | Security | X/5 | ... | | Governor Limits | X/5 | ... | | Bulkification | X/5 | ... | | Patterns | X/5 | ... | | Completeness | X/5 | ... | ### With Skills — X/25 | Category | Score | Reason | |----------|-------|--------| | Security | X/5 | ... | | Governor Limits | X/5 | ... | | Bulkification | X/5 | ... | | Patterns | X/5 | ... | | Completeness | X/5 | ... | ### Improvement: +X points (+XX%)
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.
- 10d ago First seen · 143 lines · 93 tokens per session scan A e4be43e8eabd
sf-eval is a skill published in the GitHub repository Clientell-Ai/salesforce-skills (15 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 93 tokens to every session and 1,533 once invoked, about $0.0005 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 2026-08-30.
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