Borrowing it
Nothing to install: this file belongs to lglucas/ai-dev-operating-system. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/lglucas/ai-dev-operating-system/main/.claude/skills/first-100-users/SKILL.mdgit clone --depth 1 https://github.com/lglucas/ai-dev-operating-systemWrote 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/lglucas/ai-dev-operating-system/first-100-users)<a href="https://agentmods.dev/skills/lglucas/ai-dev-operating-system/first-100-users"><img src="https://agentmods.dev/badge/skills/lglucas/ai-dev-operating-system/first-100-users.svg" alt="Measured on agentmods" 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 | $0.00106 | $0.00937 |
| Opus 5 | $0.00053 | $0.00468 |
| Sonnet 5 | $0.00021 | $0.00187 |
| Haiku 4.5 | $0.00011 | $0.00094 |
Grade A, and why
first-100-users 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 6d 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 — 99 lines — stays where its author put it; the contents beside it link to each section on GitHub.
First 100 Users
When to run this skill
- After
launch-agentTrack A (soft launch). - User says "ninguém tá usando" / "como divulgar" / "como faço marketing sem dinheiro".
- 7+ days post-launch with < 10 active users.
Mindset
Os primeiros 100 usuários NÃO vêm de ads. Vêm de você falando uma a uma com pessoas que têm o problema. Cada conversa vai te ensinar mais sobre o produto que qualquer dashboard.
The 100-user funnel
~1000 conversas iniciadas
↓ ~30% topa testar
~300 testaram (sign-up)
↓ ~33% completam o aha-moment
~100 usuários ativos
Plan for ~10x the conversation volume vs the active-user goal.
Sources, in order of efficiency
Tier 1 — Pessoas que você conhece (week 1)
- WhatsApp pessoais — pessoas específicas, não broadcast.
- LinkedIn 1st-degree — DMs pra 20 contatos relevantes.
- Ex-colegas, ex-clientes, ex-fornecedores que se encaixam na persona.
Tier 2 — Comunidades onde sua persona já está (week 2-3)
- Discord servers / WhatsApp groups do nicho.
- Subreddits relevantes — só onde você tem 30+ dias de comentários úteis.
- LinkedIn / X — postar sobre o problema (não o produto).
- Indie Hackers, Product Hunt makers.
- BlueSky niche feeds.
Regra: não chega vendendo. Chega contribuindo. Mencione o produto só quando a conversa pedir.
Tier 3 — Cold outreach (week 3-4)
- Listas de email do público-alvo (LinkedIn Sales Nav, Apollo).
- DMs em X/LinkedIn pra perfis que se encaixam.
- 5-10 por dia, NUNCA copy-paste — sempre referência específica.
Tier 4 — Conteúdo orgânico (mês 2+)
- Blog post / thread por semana sobre o problema.
- 1 case real (com permissão) por mês.
- SEO de cauda longa.
Quality bar (avoid acquisition theater)
- ❌ Não conta usuário cadastrado que nunca usou.
- ❌ Não conta amigo que entrou só pra te ajudar.
- ✅ Conta usuário que: (a) completou o aha-moment, (b) voltou em 7 dias, (c) mandou feedback espontâneo.
Retention check-in cadence
- Day 1: thank-you email + 1 specific question ("o que mais te incomodou?").
- Day 7: "ainda usa? o que falta?".
- Day 30: invite for 15-min call.
- Use
usage-monitorpra ver quem voltou.
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.
- 6d ago First seen · 99 lines · 106 tokens per session scan A 659ccab85bf2
first-100-users is a skill published in the GitHub repository lglucas/ai-dev-operating-system (11 stars, last pushed 28d ago), licensed MIT. It adds 106 tokens to every session and 937 once invoked, about $0.0005 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 2026-08-30.
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