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/grow-sustainably/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/grow-sustainably)<a href="https://agentmods.dev/skills/lglucas/ai-dev-operating-system/grow-sustainably"><img src="https://agentmods.dev/badge/skills/lglucas/ai-dev-operating-system/grow-sustainably/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/lglucas/ai-dev-operating-system/grow-sustainably"><img src="https://agentmods.dev/badge/skills/lglucas/ai-dev-operating-system/grow-sustainably.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 | $0.00093 | $0.01705 |
| Opus 5 | $0.00046 | $0.00852 |
| Sonnet 5 | $0.00019 | $0.00341 |
| Haiku 4.5 | $0.00009 | $0.00170 |
Grade A, and why
grow-sustainably 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 — 163 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Grow Sustainably
first-100-users is conversation-driven. Beyond 100, conversations alone don't scale. But the answer is NOT to dump money into ads. The answer is to compress what worked, plug retention holes, and add ONE honest distribution channel at a time.
This skill protects against the three classic post-100 failures:
- Founder burnout — trying to keep the 1-a-1 cadence past the personal limit.
- Vanity acquisition — buying signups that never activate.
- Channel diffusion — running 5 channels at 20% effort instead of 1 channel at 100%.
Pre-conditions
This skill assumes:
- ~100+ active users (active = completed the aha-moment + returned in 7d).
- Some retention data exists (week-1, week-4 returning users).
- At least one acquisition pattern that worked manually (a specific community, a specific cold outreach script, a specific content theme).
- Cost telemetry exists (
usage-monitorrunning) — otherwise scaling will quietly bankrupt the OPEX.
If any pre-condition is missing, the skill stops and routes to: first-100-users (under 100), usage-monitor (no cost telemetry), or daily-standup (no retention data captured).
The four pillars (run in order, never in parallel)
Pillar 1 — Retention before acquisition
A leaky bucket grows slower with more water. Before adding any new acquisition channel:
- Plot week-1 and week-4 retention.
- If week-4 retention < 20%, do not scale acquisition. Fix activation/onboarding first.
- Identify the top 3 reasons inactive users gave (or you suspect) for not returning. Fix the top one first.
- If retention is healthy (≥30% at week-4), proceed to Pillar 2.
Pillar 2 — Compress what already worked
Look at the first 100. Where did they actually come from?
- Tag each of the 100 with one acquisition source.
- Sort by source. Take the top source.
- Ask: what made it work? Was it the channel itself, the message, the timing, or the founder's personal voice?
- Codify the answer with
/processize— now is the right time, you have ~12+ manual reps. - The output is a documented playbook for that single source. Run it deliberately, with light AI assist, until it plateaus.
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 · 163 lines · 93 tokens per session scan A 315463d2cf9b
grow-sustainably is a skill published in the GitHub repository lglucas/ai-dev-operating-system (11 stars, last pushed 1mo ago), licensed MIT. It adds 93 tokens to every session and 1,705 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.
Other skills, from other repositories
cn-check
Install and run the Continue CLI (cn) to execute AI agent checks on local code changes. Use when asked to "run checks", "lint with AI", "review my changes with cn", or set up Continue CI locally.
phone-harness
Control the user's phone — iPhone through the Mac's iPhone Mirroring window, or an Android over adb: open apps, tap, type, swipe, read the screen.
solid-principles
SOLID principles checklist with Java examples. Use when a class has too many responsibilities, an abstraction leaks, or a dependency points the wrong way, and when the user asks about Single Responsibility, Open/Closed, Liskov, Interface Segregation or Dependency Inversion. For naming, duplication and method length…
architecture-review
Analyze Java project architecture at macro level - package structure, module boundaries, dependency direction, and layering. Use when user asks "review architecture", "check structure", "package organization", or when evaluating if a codebase follows clean architecture principles.
issue-triage
Triage and categorize GitHub issues with priority labels. Use when user says "triage issues", "check issues", "review open issues", or during regular maintenance of GitHub issue backlog.
git-commit
Generate conventional commit messages for Java projects. Use when user says "commit", "create commit", "commit changes", or after completing code changes that need to be committed.