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 hewi333/Mom-n-Pop-Skills --skill small-business-ai-transformationgit clone --depth 1 https://github.com/hewi333/Mom-n-Pop-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/hewi333/mom-n-pop-skills/small-business-ai-transformation)<a href="https://agentmods.dev/skills/hewi333/mom-n-pop-skills/small-business-ai-transformation"><img src="https://agentmods.dev/badge/skills/hewi333/mom-n-pop-skills/small-business-ai-transformation/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/hewi333/mom-n-pop-skills/small-business-ai-transformation"><img src="https://agentmods.dev/badge/skills/hewi333/mom-n-pop-skills/small-business-ai-transformation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00053 | $0.03408 |
| Opus 5 | $0.00026 | $0.01704 |
| Sonnet 5 | $0.00011 | $0.00682 |
| Haiku 4.5 | $0.00005 | $0.00341 |
Grade A, and why
small-business-ai-transformation 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 12d 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 — 211 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Small Business AI Transformation
When to Use
- User wants to help a real small service business with AI (family business, friend's business, etc.)
- Owners are non-technical — phone-driven, no CRM, no dashboards
- Goal: build an AI "front-office employee" that finds leads, qualifies them, estimates jobs, books, bills, and reports
- Also applies when a hackathon concept needs a real-world customer story (not AI-for-AI-people)
Core Principle: The Agent is an Employee, Not a Tool
The framing matters for both the business owners AND for hackathon judges. A tool requires someone to operate it. An employee works 24/7 and reports by text. The owners don't log into anything. They text. The agent handles the rest.
The demo moment that wins: "My 62-year-old mom texts an AI agent to run her business. She doesn't know what an API is."
Methodology: 6 Steps
Step 1: Website Audit (Browser Tools)
Navigate the full site systematically: Home → Services → Testimonials → FAQ → Blog → Contact. For each page, note:
- Broken pages (404s, dead links) — common in old WordPress sites built by contractors
- Stale content (blog last updated >1 year ago, copyright date)
- Missing features (no online estimator, no chat, no booking, no online payment)
- Mobile experience (does it render on phone?)
- SEO gaps (no geo-targeted content for service areas, no fresh content)
- Contact form (does it actually work? does it have reCAPTCHA? what fields does it collect?)
Record findings in the North Star wiki page (Step 5).
Step 2: Business Model Discovery (Ask the User OR Analyze Their Data)
Option A — Ask the user (when no financial data is available):
Ask these questions to the user (who talks to the business owners). Get rough numbers — precision isn't needed for concept development:
- Who runs it? Owners, roles, who does what (ops vs marketing vs field)
- Volume & pricing — Jobs per month, average job cost, pricing model (sqft? flat rate? tiers?)
- Lead sources — Where do customers come from today? Rough percentage split (realtors, Google, referrals, insurance, etc.)
- Revenue channel risk — Is 80% of revenue from one source? What happens when that source slows?
- Customer flow — Step by step: how does someone go from "interested" to "job completed and paid"?
- What systems exist? CRM? Calendar? Email list? QuickBooks? Stripe? Or is it all phone + memory?
- Tech maturity — Would the owners text a bot? Would they read a weekly SMS summary? What's their comfort level?
- Existing contacts — Do they have a list of past clients, referral partners (realtors, insurance adjusters) that's dormant?
What ships with it
1 file 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.
- 12d ago First seen · 211 lines · 53 tokens per session scan A 8712f2ac3430
small-business-ai-transformation is a skill published in the GitHub repository hewi333/Mom-n-Pop-Skills (120 stars, last pushed yesterday), licensed MIT. It adds 53 tokens to every session and 3,408 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 2026-08-30.
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