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-scale-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-scale-stage)<a href="https://agentmods.dev/skills/hades-hy-li/ai-native-founder-playbook-skills/ai-native-scale-stage"><img src="https://agentmods.dev/badge/skills/hades-hy-li/ai-native-founder-playbook-skills/ai-native-scale-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-scale-stage"><img src="https://agentmods.dev/badge/skills/hades-hy-li/ai-native-founder-playbook-skills/ai-native-scale-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 | $0.00699 |
| Opus 5 | $0.00030 | $0.00349 |
| Sonnet 5 | $0.00012 | $0.00140 |
| Haiku 4.5 | $0.00006 | $0.00070 |
Grade A, and why
ai-native-scale-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 Scale Stage
Goal
Help founders convert early traction into repeatable growth, operating discipline, team leverage, and defensibility without losing customer learning.
Required Inputs
If the founder provides a structured brief, use these inputs:
Traction:
Retention/usage:
Revenue or pipeline:
Growth channel:
Team and hiring needs:
Operating bottlenecks:
Key metrics:
Funding context:
Desired output:
Guided Intake
Do not require the founder to provide a complete metrics package. If the request is broad, ask up to five questions first:
1. What traction do you have: users, revenue, pilots, retention, or pipeline?
2. Where does growth or delivery feel most constrained right now?
3. Which metric do you trust most, and which metric feels unclear or misleading?
4. What is the next major milestone: revenue, retention, hiring, fundraising, reliability, or GTM repeatability?
5. What output would help most: bottleneck diagnosis, metrics dashboard, hiring plan, operating cadence, or fundraising memo?
After the user answers, identify the likely bottleneck and produce the requested plan. Mark missing metrics as assumptions instead of stalling unless the user asks for a precise dashboard.
Workflow
- Identify the real scale bottleneck: demand, activation, retention, delivery, data, reliability, hiring, or capital.
- Use
references/metrics.mdto separate product-market fit signals from early hype. - Use
references/operating-cadence.mdto design weekly reviews, dashboards, decision rituals, and AI-assisted workflows. - Use
references/hiring.mdto decide what to hire, automate, outsource, or defer. - Use
references/fundraising.mdwhen the user needs a funding narrative, milestone plan, or investor memo. - Return a scale plan with priorities, owners, metrics, operating rhythm, and risk controls.
AI-Native Workflows
Use generic AI roles:
- Metrics analyst: monitor acquisition, activation, retention, revenue, and qualitative signals.
- GTM assistant: research accounts, draft campaigns, and maintain sales/customer-success artifacts.
- Ops agent: run recurring internal workflows with human approval points.
- Hiring assistant: draft scorecards, interview loops, and onboarding plans.
- Fundraising copilot: assemble narrative, milestones, and diligence materials.
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 5f4255cbcb48
ai-native-scale-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 699 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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