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-use-case-assessmentgit 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-use-case-assessment)<a href="https://agentmods.dev/skills/banibratachatterjee/awesomesalesforceskills/ai-use-case-assessment"><img src="https://agentmods.dev/badge/skills/banibratachatterjee/awesomesalesforceskills/ai-use-case-assessment/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-use-case-assessment"><img src="https://agentmods.dev/badge/skills/banibratachatterjee/awesomesalesforceskills/ai-use-case-assessment.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 | $0.00066 | $0.03185 |
| Opus 5 | $0.00033 | $0.01592 |
| Sonnet 5 | $0.00013 | $0.00637 |
| Haiku 4.5 | $0.00007 | $0.00318 |
Grade A, and why
ai-use-case-assessment 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 — 195 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Use Case Assessment
This skill activates when a practitioner or organization needs to decide which AI use cases to pursue on Salesforce, in what order, and whether they are feasible — before any implementation work begins. It produces a structured prioritization and feasibility output grounded in the Salesforce AI Use Case Identification framework.
Before Starting
Gather this context before working on anything in this domain:
- License and edition confirmation: Many Einstein features require specific add-ons. Einstein for Service generative features (Work Summaries, Service Replies) require the Einstein for Service add-on license; Agentforce requires the Agentforce platform license. Confirm what is purchased before scoring technical feasibility.
- Data posture: The single most common failure mode is assuming that CRM data is AI-ready. Verify whether Data Cloud is licensed and populated, whether key objects (Case, Lead, Opportunity) have sufficient record volume and field completeness for the features being assessed.
- Stakeholder alignment on scope: Assessment work frequently expands into implementation scoping mid-session. Stay inside the assessment boundary — the outputs of this skill feed an implementation project, not replace one.
Core Concepts
1. Impact-Effort Matrix
The Salesforce AI use case framework organizes candidate use cases into four quadrants based on two axes: business impact (revenue, cost reduction, customer satisfaction, risk mitigation) and implementation effort (technical complexity, data readiness requirements, change management burden).
| Quadrant | Impact | Effort | Action |
|---|---|---|---|
| Quick Wins | High | Low | Prioritize first |
| Big Bets | High | High | Plan for later; needs groundwork |
| Low-Hanging Fruit | Low | Low | Fill gaps; good for momentum |
| Money Pits | Low | High | Avoid or defer indefinitely |
Scoring is qualitative and consensus-driven. A facilitated workshop with business and IT stakeholders is the standard approach. Use a 1–3 scale per dimension, sum the axis scores, and plot the result. Do not use the matrix as a rigid formula — it is a facilitation and alignment tool.
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.
- 9d ago First seen · 195 lines · 66 tokens per session scan A 0d2f8bf0dce7
ai-use-case-assessment is a skill published in the GitHub repository BanibrataChatterjee/AwesomeSalesforceSkills (3 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 66 tokens to every session and 3,185 once invoked, about $0.0003 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.
Other skills, from other repositories
ai-use-case-assessment
Identify, score, and prioritize Salesforce AI use cases before any implementation begins — opportunity identification, Impact-Effort matrix scoring, feasibility evaluation across Technical/Operational/Data/Risk dimensions, and ROI framing. NOT for the license checks and enablement steps once a use case is approved …
ai-adoption-change-management
Use this skill when planning or executing the human side of an Agentforce or Einstein AI feature rollout — user trust-building, AI-specific training, structured feedback collection via the Feedback API, and adoption measurement via Agentforce Analytics. NOT for turning the Einstein for Sales features on — use…
ai-ethics-and-governance-requirements
Use this skill when defining AI governance policies, designing human oversight workflows, documenting bias mitigation strategies, or meeting regulatory transparency requirements for Salesforce AI features. Trigger keywords: responsible AI, AI bias, AI audit trail, AI transparency, human-in-the-loop, AI risk inventory…
analytics-adoption-strategy
Use this skill when driving adoption of CRM Analytics (Einstein Analytics) across an org — the Analytics Adoption App for measuring who uses which dashboards, embedding analytics into Lightning pages, pinning dashboards to the Analytics home page, self-service personas, and analytics success metrics. Triggers…
case-feed-send-email-action
Use when configuring the outbound Send Email quick action in Lightning Case Feed — creating the action in Setup on Case (Action Type = Send Email), attaching a default Custom email template, setting predefined To/CC/BCC values, wiring QuickAction.QuickActionDefaultsHandler Apex defaults, respecting the Lightning…
collaborative-forecasts
Set up, configure or troubleshoot Salesforce Collaborative Forecasts: forecast types, forecast categories, rollup methods, quota management, forecast hierarchy, manager adjustments, pipeline inspection integration. Trigger keywords: forecast type, forecast category, cumulative rollup, individual rollup, quota…