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 BanibrataChatterjee/AwesomeSalesforceSkills --skill ai-ethics-and-governance-requirementsgit clone --depth 1 https://github.com/BanibrataChatterjee/AwesomeSalesforceSkillsWrote 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/banibratachatterjee/awesomesalesforceskills/ai-ethics-and-governance-requirements)<a href="https://agentmods.dev/skills/banibratachatterjee/awesomesalesforceskills/ai-ethics-and-governance-requirements"><img src="https://agentmods.dev/badge/skills/banibratachatterjee/awesomesalesforceskills/ai-ethics-and-governance-requirements/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/banibratachatterjee/awesomesalesforceskills/ai-ethics-and-governance-requirements"><img src="https://agentmods.dev/badge/skills/banibratachatterjee/awesomesalesforceskills/ai-ethics-and-governance-requirements.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 | $0.00089 | $0.02964 |
| Opus 5 | $0.00044 | $0.01482 |
| Sonnet 5 | $0.00018 | $0.00593 |
| Haiku 4.5 | $0.00009 | $0.00296 |
Grade A, and why
ai-ethics-and-governance-requirements 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 — 199 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Ethics and Governance Requirements
Use this skill when a Salesforce implementation needs governance structure around AI features — not just configuration, but the policies, human oversight rules, risk inventory, bias controls, and transparency disclosures that sit above Trust Layer settings. Activates when stakeholders ask how to make AI use responsible, auditable, and explainable rather than how to configure a specific platform feature.
Before Starting
Gather this context before working on anything in this domain:
- Which AI surface is in scope: Einstein Prediction Builder, Next Best Action (NBA), Agentforce agents, Einstein Copilot, Einstein Discovery, or a third-party LLM integrated through the Trust Layer.
- Whether the org is subject to sector-specific regulation (healthcare HIPAA, financial FINRA, EU AI Act high-risk classification, California CPRA).
- Whether AI-generated output is customer-facing or internal-only — disclosure requirements differ materially.
- Who owns the AI governance function in the customer org: CTO, legal/compliance, data governance council, or a dedicated AI Ethics board.
- The most common wrong assumption: that enabling the Einstein Trust Layer is sufficient governance. Trust Layer covers data protection during LLM calls; governance covers policy, human accountability, bias controls, and ongoing audit.
Core Concepts
1. Salesforce Responsible AI Pillars
Salesforce's responsible AI framework rests on five published pillars:
- Accuracy — AI outputs must be grounded, traceable, and evaluated against ground truth.
- Safety — Harmful, toxic, or dangerous content must be prevented before it reaches users. The Einstein Trust Layer's toxicity scoring and prompt defense mechanisms are safety controls, but the policy decision on what constitutes "harmful" is a governance requirement.
- Honesty — AI-generated content must be disclosed as such. Salesforce requires that features surfacing generated text display a disclosure marker. Orgs must define where and how those markers appear.
- Empowerment — Users and customers must be able to override, reject, or escalate AI decisions. Human-in-the-loop gates are a governance design concern, not a platform default.
- Sustainability — AI resource consumption and environmental impact must be tracked as part of responsible use.
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 199 lines · 89 tokens per session scan A 93172a22f783
ai-ethics-and-governance-requirements is a skill published in the GitHub repository BanibrataChatterjee/AwesomeSalesforceSkills (3 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 89 tokens to every session and 2,964 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 2026-09-03.
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