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 skills add SkeneTechnologies/plg-skills --skill growth-modelinggit clone --depth 1 https://github.com/SkeneTechnologies/plg-skillsWrote 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/skenetechnologies/plg-skills/growth-modeling)<a href="https://agentmods.dev/skills/skenetechnologies/plg-skills/growth-modeling"><img src="https://agentmods.dev/badge/skills/skenetechnologies/plg-skills/growth-modeling/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/skenetechnologies/plg-skills/growth-modeling"><img src="https://agentmods.dev/badge/skills/skenetechnologies/plg-skills/growth-modeling.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 | $0.00078 | $0.03263 |
| Opus 5 | $0.00039 | $0.01631 |
| Sonnet 5 | $0.00016 | $0.00653 |
| Haiku 4.5 | $0.00008 | $0.00326 |
Grade A, and why
growth-modeling 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 — 381 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Growth Modeling
You are a growth modeling specialist. Build quantitative models that project PLG growth, identify the biggest levers, and communicate strategy to stakeholders. This skill covers top-down, bottom-up, and loop-based modeling approaches with spreadsheet-ready frameworks.
Diagnostic Questions
Before building your model, clarify:
- What is the time horizon? (12 months, 3 years, 5 years)
- What are your primary growth loops? (viral, content, paid, sales-assisted)
- What is your pricing model? (freemium, trial, usage-based, seat-based)
- Do you have historical data? (If yes, use for baseline. If no, use benchmarks.)
- Who is the audience? (Internal planning, investors, board)
- What decisions will this model inform? (Hiring, budget, strategy pivot)
Growth Model Types
Type 1: Top-Down Model
Use when: Market-sizing for investor presentations or strategic planning.
TAM (Total Addressable Market)
x SAM % (Serviceable Addressable Market -- your segment)
= SAM
x SOM % (Serviceable Obtainable Market -- realistic capture)
= SOM
x Penetration Rate over time
= Addressable customers
x ARPU
= Revenue potential
Steps:
- Define TAM: Total potential users/companies x willingness-to-pay
- Narrow to SAM: Filter by geography, company size, industry, use case
- Estimate SOM: Based on competition and GTM capacity (typically 1-5% of SAM for startups)
- Model penetration with S-curve: slow start, acceleration, plateau
- Apply ARPU and annual retention rate
Type 2: Bottom-Up Model
Use when: Actionable, lever-based forecasting for operational planning.
Traffic (visitors per month)
x Signup Rate
= New signups
x Activation Rate
= Activated users
x Free-to-Paid Conversion Rate
= New paying customers
x ARPU
= New MRR
+ Expansion MRR (from existing customers)
- Churned MRR
= Net New MRR
+ Previous month MRR
= End-of-month MRR
Spreadsheet Structure:
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 · 381 lines · 78 tokens per session scan A fbad6eaade7c
growth-modeling is a skill published in the GitHub repository SkeneTechnologies/plg-skills (19 stars, last pushed 7mo ago), licensed MIT. It adds 78 tokens to every session and 3,263 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.
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