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.
git clone --depth 1 https://github.com/GktuOktay/ai-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/rules/gktuoktay/ai-skills/prototype)<a href="https://agentmods.dev/rules/gktuoktay/ai-skills/prototype"><img src="https://agentmods.dev/badge/rules/gktuoktay/ai-skills/prototype/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/rules/gktuoktay/ai-skills/prototype"><img src="https://agentmods.dev/badge/rules/gktuoktay/ai-skills/prototype.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 | $0.00000 | $0.00971 |
| Opus 5 | $0.00000 | $0.00485 |
| Sonnet 5 | $0.00000 | $0.00194 |
| Haiku 4.5 | $0.00000 | $0.00097 |
Grade A, and why
prototype 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 — 59 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Rapid Prototyping and MVP Development
Prototyping is the process of creating a preliminary model of an application to validate concepts, flows, and technical feasibility before committing to full-scale development.
1. Choosing the Right Fidelity
Prototypes range from low to high fidelity. Choosing the right level is essential for efficiency.
| Fidelity Level | Format | Purpose | Speed | Tools |
|---|---|---|---|---|
| Low (Lo-Fi) | Paper Sketches, Whiteboard | Brainstorming, defining core layout, early validation of user flows. | Very Fast | Pen & Paper, Excalidraw, Balsamiq |
| Medium (Mid-Fi) | Wireframes, Clickable Screens | Defining structure, navigation, and content hierarchy without visual distraction. | Fast | Figma, Sketch, Whimsical |
| High (Hi-Fi) | Interactive Mockups | Finalizing visual design (colors, typography), micro-interactions, realistic user testing. | Slow | Figma, Framer, Protopie |
| Coded (Code-Fi) | Functional MVP (HTML/CSS/JS) | Validating technical feasibility, testing with real data, actual user interaction on devices. | Very Slow | CodeSandbox, Vercel, HTML/CSS, Tailwind |
2. Tool Selection
- Figma: The industry standard for Mid-Fi and Hi-Fi prototyping. Excellent for collaboration, creating clickable flows, and sharing with stakeholders.
- Code-based Prototyping: Tools like Framer (which bridges design and code) or jumping straight into React/Tailwind. Best when interactions are too complex for Figma or when you intend to iterate the prototype directly into production.
- No-Code/Low-Code: Tools like Webflow, Bubble, or Retool. Excellent for functional MVPs without requiring a full engineering team.
3. Speed vs. Quality Tradeoffs
A prototype is a disposable artifact used for learning, not a production application.
- Accept Technical Debt: Hardcode data, skip error handling, ignore edge cases.
- Fake It 'Til You Make It: Use "Wizard of Oz" techniques (humans manually doing tasks behind the scenes) instead of building complex backend logic.
- Focus on the Core Loop: Only build the features necessary to test the specific hypothesis.
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 · 59 lines · 971 tokens per session scan A 6973460c2f21
prototype is a cursor rule published in the GitHub repository GktuOktay/ai-skills (2 stars, last pushed 8d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 971 tokens. 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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