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-launch-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-launch-stage)<a href="https://agentmods.dev/skills/hades-hy-li/ai-native-founder-playbook-skills/ai-native-launch-stage"><img src="https://agentmods.dev/badge/skills/hades-hy-li/ai-native-founder-playbook-skills/ai-native-launch-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-launch-stage"><img src="https://agentmods.dev/badge/skills/hades-hy-li/ai-native-founder-playbook-skills/ai-native-launch-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-launch-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 — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI-Native Launch Stage
Goal
Help founders turn a working MVP into a focused launch motion that creates customers, feedback, revenue signals, and repeatable learning.
Required Inputs
If the founder provides a structured brief, use these inputs:
Product/MVP status:
Ideal customer profile:
Positioning hypothesis:
Current users or pipeline:
Pricing hypothesis:
Onboarding flow:
Acquisition channel:
Feedback signals:
Desired output:
Guided Intake
Do not require the founder to provide a full launch brief. If the request is vague, ask up to five questions first:
1. What product or MVP can users try now?
2. Who is the narrowest ideal customer for the first launch motion?
3. How are you currently reaching users or buyers?
4. What launch problem is most urgent: positioning, sales, pricing, onboarding, or feedback?
5. What evidence do you have from users, demos, pilots, or sales conversations?
After the user answers, choose the narrowest useful launch artifact and state assumptions. Ask follow-ups only if the launch audience or available product is still impossible to infer.
Workflow
- Clarify the launch audience and buying/user trigger.
- Use
references/positioning.mdto convert product capability into customer value. - Use
references/founder-led-sales.mdto plan outreach, discovery, demos, follow-up, and close/learn loops. - Use
references/pricing-and-onboarding.mdto design a simple offer and activation path. - Use
references/launch-operating-system.mdto replace repetitive founder attention with AI-assisted workflows. - Return a launch plan with messaging, target list, sales motion, onboarding, feedback loops, and decision points.
AI-Native Workflows
Use generic AI roles:
- Research assistant: build account lists and summarize buyer context.
- Sales copilot: draft outreach, discovery guides, demo scripts, and follow-ups.
- Customer-success assistant: create onboarding, FAQs, and activation nudges.
- Feedback analyst: cluster objections, usage notes, churn risks, and feature requests.
- Workflow agent: automate repeatable launch operations with human review.
What ships with it
5 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 · 88 lines · 60 tokens per session scan A 6b7b5d67e538
ai-native-launch-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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