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/metrics)<a href="https://agentmods.dev/commands/alifanov/ai-garage-launch/metrics"><img src="https://agentmods.dev/badge/commands/alifanov/ai-garage-launch/metrics/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/commands/alifanov/ai-garage-launch/metrics"><img src="https://agentmods.dev/badge/commands/alifanov/ai-garage-launch/metrics.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 | $0.00032 | $0.00420 |
| Opus 5 | $0.00016 | $0.00210 |
| Sonnet 5 | $0.00006 | $0.00084 |
| Haiku 4.5 | $0.00003 | $0.00042 |
Grade A, and why
metrics 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 10d 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.
What it actually says
/garage:metrics — воронка и экономика
Предусловия
Прочитай docs/08-analytics.md и docs/09-launch.md (есть трафик). Нет запуска — скажи запустить /garage:launch.
Что сделать
Опирайся на скилл unit-economics. Собери из аналитики (Plausible/PostHog) и посчитай:
- Воронка — visitors → users → buyers: где именно проседает? Работаем над самым узким местом.
- CAC — стоимость привлечения = потрачено на канал / число клиентов.
- LTV — сколько принесёт клиент = средний чек × срок жизни.
- LTV/CAC — главный коэффициент. Ориентир: ≥ 3 — модель здоровая; < 1 — теряешь на каждом клиенте.
- Retention — DAU/WAU/MAU, когорты, retention curve: возвращаются ли люди.
- UTM — какие источники/кампании реально приносят конверсии.
Вывод: 1–3 конкретных действия (починить узкое место воронки / убить неэффективную кампанию / усилить работающий источник).
Артефакт
Запиши в docs/10-metrics.md: цифры воронки, CAC, LTV, LTV/CAC, retention, разбивка по UTM, выводы и действия. Обнови docs/STATE.md: шаг 10 [x].
➡️ Дальше
/garage:payments — подключить монетизацию через Polar (когда воронка доводит людей до ценности).
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.
- 10d ago First seen · 31 lines · 0 tokens per session scan A 37fee663dbcb
metrics is a command published in the GitHub repository alifanov/ai-garage-launch (2 stars, last pushed 1mo ago), licensed MIT. It adds 32 tokens to every session and 420 once invoked, about $0.0002 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.
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.
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.
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.