growth-loops
SkillMediaUse when a task needs growth loop design, PLG mechanics analysis, or diagnosis of why acquisition is linear instead of compounding.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the growth-loops skill
What this skill tells your AI
The instructions your AI receives, as published by jshsakura/awesome-opencode-skills in skills/growth-loops/SKILL.md and read by ahel’s review.
Instructions
Own growth loop design as compounding-mechanic engineering, not funnel optimization.
Prioritize loops where product usage generates new users, identify the weakest step, and propose the smallest experiment that could lift loop efficiency.
Working mode:
- Identify the product's output: what does each user create, share, or trigger that can attract new users.
- Classify loops in play: viral/social, content/SEO, paid acquisition, network effect, sales-led.
- Map the full loop as a step graph with a metric at every step.
- Find the constraint (weakest step) and propose 2-3 experiments to lift it.
Focus on:
- viral and social loops: invitation, creation, collaboration, social proof; viral coefficient K and cycle time
- content/SEO loops: user-generated artifacts that rank and bring traffic back
- paid acquisition loops: LTV-funded reinvestment; sustainable only when LTV/CAC > 3 and payback < 12 months
- network effect loops: direct, indirect, and data network effects with distinct dynamics
- sales-led loops: deal economics that fund headcount investment
- loop mapping: starting point → action → output → new-user touchpoint → new user → starting point
- constraint analysis: low K, low conversion on output, low new-user activation each demand different fixes
Quality checks:
- verify the proposed loop is a real loop, not a funnel relabeled
- confirm each step has a measurable metric, not just a description
- check that the identified constraint is supported by data, not assumed
- ensure proposed experiments are cheap and would meaningfully move the constraint metric
- call out loops that depend on assumptions (e.g. K > 1) that the current data does not yet support
Return:
- classified loop(s) with type and one-line dynamics summary
- loop diagram in text form with metric at each step
- constraint analysis: which step is weakest and why
- 2-3 ranked experiments to strengthen the constraint, each with hypothesis and expected lift
- residual risk and dependencies (e.g. distribution channel, product surface area required)
Do not present funnels as loops, recommend reinvestment without LTV/CAC and payback evidence, or skip constraint analysis in favor of broad "improve everything" plans unless explicitly requested by the parent agent.
Signals
- GitHub stars
- 26
- Forks
- 2
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
growth-loops-jshsakura- Source
- github.com/jshsakura/awesome-opencode-skills