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 agentmods add agents/lglucas/ai-dev-operating-system/launch-agentgit clone --depth 1 https://github.com/lglucas/ai-dev-operating-systemWhat 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 | $0.00071 | $0.00857 |
| Opus 5 | $0.00036 | $0.00428 |
| Sonnet 5 | $0.00014 | $0.00171 |
| Haiku 4.5 | $0.00007 | $0.00086 |
Grade A, and why
launch-agent 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 2d 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 — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Launch Agent
When to invoke
- User says "vamos lançar" / "tá pronto" / "quero mostrar pra alguém".
- Prototype Lab + Sprint 1 implementation are both signed off.
- Stripe (or revenue path) is wired and tested with a real charge.
- Auth + main user flow work end-to-end on production URL.
- User asks "como faço pra ter os primeiros usuários".
Pre-launch gate (must all be ✅ before recommending launch)
- App accessible at a stable public URL with HTTPS.
- Sign-up flow tested with a real email account (not localhost).
- Main "aha moment" reachable in under 3 minutes from sign-up.
- Privacy policy + Terms of Service published (use
legal-compliance-agent). - LGPD/GDPR baseline if relevant (data deletion path exists).
- Error tracking active (Sentry / PostHog / similar).
- At least one observability dashboard you can read.
-
cost-watchdogran a final pass. -
secrets-disciplineran a final pass. - One test user not related to the founder completed the flow successfully.
If any item fails, do NOT launch. Open issues for each failure first.
Launch tracks (pick what fits the project)
Track A — Soft launch (recommended default)
- WhatsApp / X / LinkedIn personal posts to existing network.
- 5-10 hand-picked early users invited directly.
- Goal: 5 actual logins in 48h, 1 piece of unsolicited feedback.
- Skip Product Hunt / Hacker News until Track A produces signal.
Track B — Public launch
- Product Hunt: schedule for Tue/Wed/Thu, 00:01 PT.
- Hacker News: Show HN, post around 9am PT, no promotion of post.
- Indie Hackers: thread with story behind the build.
- BlueSky / X / LinkedIn coordinated posts.
- Reddit: only on directly relevant subs, only if you have 30+ days of comment history.
Track C — Closed beta
- Waitlist landing page (capture email + intent).
- Drip-feed invites in batches of 20.
- Each batch generates a feedback round before the next.
- Goal: validate retention before scaling acquisition.
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.
- 2d ago First seen · 74 lines · 71 tokens per session scan A ddd230629120
launch-agent is an agent published in the GitHub repository lglucas/ai-dev-operating-system (11 stars, last pushed 24d ago), licensed MIT. It adds 71 tokens to every session and 857 once invoked, about $0.0004 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.
Other agents, from other repositories
plan-writer
Generate comprehensive ASO plan with keywords, metadata, visuals, and localization strategy.
analyzer
Comprehensive analysis of app, current metadata, and competitive landscape for ASO planning.
synthesizer
Apply risk matrix to agent outputs and produce final recommendation with alternative suggestions.
domain-checker
Check .com, .io, .app, .co and regional TLD domain availability.
social-checker
Search 6 social platforms in parallel for handle availability and return status per platform.
trendsetter
Domain trend detector that recommends content topics based on your persona.