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.
git clone --depth 1 https://github.com/alifanov/ai-garage-launchWrote 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/commands/alifanov/ai-garage-launch/start)<a href="https://agentmods.dev/commands/alifanov/ai-garage-launch/start"><img src="https://agentmods.dev/badge/commands/alifanov/ai-garage-launch/start.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 | $0.00028 | $0.00757 |
| Opus 5 | $0.00014 | $0.00378 |
| Sonnet 5 | $0.00006 | $0.00151 |
| Haiku 4.5 | $0.00003 | $0.00076 |
Grade A, and why
start 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 8d 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 — 53 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/garage:start — идеи и доноры
Ты запускаешь продукт по методологии AI Garage. Главный принцип: не придумывай — копируй валидированное. Мы не изобретаем рынок, а находим конкурента с платящими пользователями и берём у него одну фичу.
Что сделать
- Создай папку
docs/в корне проекта, если её нет. - Если аргумент задан — работаем в этой нише. Если нет — спроси у пользователя: (а) что ему нравится / чем сам пользуется и платит, (б) что его раздражает (своя боль), (в) есть ли ниша на примете.
- Найди 5–10 продуктов-доноров — реальных конкурентов с признаками спроса. Источники (используй веб-поиск и, если есть, скиллы
dataforseo-keyword-research,.archived:similarweb):- Product Hunt — топ недели/месяца
- Indie Hackers — кейсы с реальными цифрами MRR
- BetaList — продукты на стадии беты (ранние тренды)
- Reddit — r/SaaS, r/entrepreneur, r/indiehackers (живые боли)
- X / Twitter — #buildinpublic (основатели публикуют метрики)
- Microlaunch, Peerlist — нишевые микро-каталоги
- Для каждого кандидата собери: название + URL, что делает, одна ключевая фича, признак спроса (MRR / трафик / отзывы), предполагаемый канал трафика.
Артефакт
Запиши таблицу кандидатов в docs/01-ideas.md (название | URL | 1 фича | признак спроса | канал). Отметь 1–3 фаворита.
Создай docs/STATE.md по шаблону:
# AI Garage — статус запуска
Продукт: —
Донор: —
Канал: —
## Пайплайн
- [x] 1. start — идеи собраны → docs/01-ideas.md
- [ ] 2. validate — валидация донора
- [ ] 3. channel — выбор канала трафика
- [ ] 4. spec — продуктовый бриф (1 фича)
- [ ] 5. scaffold — Next.js приложение
- [ ] 6. deploy — GitHub + Vercel + домен
- [ ] 7. db-auth — Neon + Clerk
- [ ] 8. analytics — Plausible/PostHog + воронка
- [ ] 9. launch — первый шаг канала
- [ ] 10. metrics — CAC / LTV / retention / UTM
- [ ] 11. payments — Polar (монетизация)
➡️ Дальше
/garage:validate <url-донора> — прогнать выбранного донора через 5 вопросов-фильтров (трафик, доход, фича, «сделаю ли сам», канал).
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.
- 8d ago First seen · 53 lines · 28 tokens per session scan A cedae89afe5f
start is a command published in the GitHub repository alifanov/ai-garage-launch (2 stars, last pushed 1mo ago), licensed MIT. It adds 28 tokens to every session and 757 once invoked, about $0.0001 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.
Other commands, from other repositories
save
Save current work state for next session. Creates/updates .planning/ files (CHECKPOINT.md, STATE.md, settings.json) so Heimdall resumes with full context. NOT a rewind — saves forward progress. Run before closing a session or at any milestone.
code-review
Multi-model code review — AI models independently review code, then converge on findings.
plan-review
Multi-model plan review — AI models independently plan, then converge on the best approach.
review
Multi-model review — AI models independently review any document or general topic, then converge on findings.
maintain
Run automated maintenance — seeker finds bugs from pod logs and raises GitHub issues, fixer picks them up and creates PRs. Can run as a one-shot or scheduled via /schedule.
autonomy
Set Heimdall autonomy (1=Guided, 2=Checkpoint, 3=Full Auto) — how much the agent does before asking. Use with a number, +/- to cycle, or no argument to show current.