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 Hades-HY-LI/ai-native-founder-playbook-skills --skill ai-native-mvp-stagegit clone --depth 1 https://github.com/Hades-HY-LI/ai-native-founder-playbook-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/hades-hy-li/ai-native-founder-playbook-skills/ai-native-mvp-stage)<a href="https://agentmods.dev/skills/hades-hy-li/ai-native-founder-playbook-skills/ai-native-mvp-stage"><img src="https://agentmods.dev/badge/skills/hades-hy-li/ai-native-founder-playbook-skills/ai-native-mvp-stage/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/hades-hy-li/ai-native-founder-playbook-skills/ai-native-mvp-stage"><img src="https://agentmods.dev/badge/skills/hades-hy-li/ai-native-founder-playbook-skills/ai-native-mvp-stage.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 | $0.00060 | $0.00654 |
| Opus 5 | $0.00030 | $0.00327 |
| Sonnet 5 | $0.00012 | $0.00131 |
| Haiku 4.5 | $0.00006 | $0.00065 |
Grade A, and why
ai-native-mvp-stage 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 — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI-Native MVP Stage
Goal
Help founders ship the smallest product that proves the core customer outcome while keeping AI-generated work testable, secure, and maintainable.
Required Inputs
If the founder provides a structured brief, use these inputs:
Validated customer/problem:
MVP outcome to prove:
Current product status:
Technical stack:
Data/security constraints:
Available builders/tools:
Deadline:
Desired output:
Guided Intake
Do not require the founder to know all implementation details upfront. If the request is thin, ask up to five questions first:
1. What customer problem and user outcome has already been validated?
2. What is the smallest workflow the MVP must prove?
3. What exists today: mockup, prototype, manual workflow, or no product?
4. What technical or data constraints matter most?
5. What do you want next: MVP scope, architecture, coding-agent task plan, eval plan, or milestones?
After the user answers, infer reasonable defaults, mark unknowns explicitly, and produce a build recommendation. Do not block on stack details unless the requested output is technical architecture.
Workflow
- Define the MVP proof target: the user outcome that must become measurably easier, faster, cheaper, or better.
- Cut scope to the smallest workflow that proves that target.
- Use
references/mvp-scope.mdto separate must-have proof from distracting surface area. - Use
references/technical-architecture.mdfor architecture, coding-agent guardrails, security, and technical debt prevention. - Use
references/evals-and-feedback.mdto define evaluation, telemetry, bug intake, and customer feedback loops. - Return a build plan with milestones, risks, evals, and acceptance criteria.
AI-Native Workflows
Use generic AI roles:
- Coding agent: implement bounded tasks with tests and explicit file ownership.
- Architecture critic: review data flow, security, and maintainability.
- Evaluation assistant: create test cases, golden examples, and failure taxonomies.
- User-research assistant: convert feedback into product decisions.
What ships with it
4 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.
- 12d ago First seen · 86 lines · 60 tokens per session scan A e6059b4adfec
ai-native-mvp-stage is a skill published in the GitHub repository Hades-HY-LI/ai-native-founder-playbook-skills (5 stars, last pushed 3mo ago), licensed MIT. It adds 60 tokens to every session and 654 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 2026-08-31.
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