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 dublyo/saas-skills --skill moatgit clone --depth 1 https://github.com/dublyo/saas-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/dublyo/saas-skills/moat)<a href="https://agentmods.dev/skills/dublyo/saas-skills/moat"><img src="https://agentmods.dev/badge/skills/dublyo/saas-skills/moat/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/dublyo/saas-skills/moat"><img src="https://agentmods.dev/badge/skills/dublyo/saas-skills/moat.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 | $0.00117 | $0.01059 |
| Opus 5 | $0.00059 | $0.00530 |
| Sonnet 5 | $0.00023 | $0.00212 |
| Haiku 4.5 | $0.00012 | $0.00106 |
Grade A, and why
moat 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 — 129 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Moat
Core Rule
A SaaS moat is not a feature, a UI, a model wrapper, or "we will move fast."
A real moat must pass both tests:
- Benefit: it creates higher customer value, lower cost, better retention, faster distribution, or better margins.
- Barrier: competitors cannot cheaply copy, neutralize, or bypass it.
For SaaS, judge moats by their effect on profitability:
- Higher willingness to pay.
- Lower churn and higher gross revenue retention.
- Expansion revenue and stronger net revenue retention.
- Lower CAC or faster CAC payback.
- Higher gross margin or lower cost-to-serve.
- Stronger pricing power.
- Lower support/onboarding drag over time.
- More durable distribution.
Operating Workflow
- Identify the SaaS idea, customer, buyer, and workflow.
- Identify the profit engine:
- Pricing model.
- Gross margin drivers.
- Cost-to-serve.
- Acquisition channel.
- Retention and expansion mechanism.
- Identify candidate moat types:
- Switching costs.
- Workflow/system-of-record depth.
- Network effects.
- Data advantage.
- Integration depth.
- Distribution advantage.
- Brand/trust.
- Scale or cost advantage.
- Process power.
- Ecosystem, marketplace, partner, reseller, or white-label channel.
- Compliance, regulatory, procurement, or operational trust.
- Test each candidate moat:
- What customer behavior proves it?
- What product function creates it?
- What business metric should improve?
- What must compound over time?
- How would a competitor attack it?
- Is it a real barrier or only temporary differentiation?
- Convert the moat into product requirements:
- Data model.
- Dashboard/workflow.
- Integrations.
- Collaboration.
- Permissions.
- Audit/history.
- Automation.
- Sharing.
- Billing/entitlements.
- Analytics.
- Output a moat plan with proof metrics and build sequence.
Default Stance
Be skeptical.
Treat these as weak moat claims unless proven:
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 · 129 lines · 117 tokens per session scan A 6b9006a0ef28
moat is a skill published in the GitHub repository dublyo/saas-skills (9 stars, last pushed 2mo ago), licensed MIT. It adds 117 tokens to every session and 1,059 once invoked, about $0.0006 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.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…