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 plg-metricsgit 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/plg-metrics)<a href="https://agentmods.dev/skills/skenetechnologies/plg-skills/plg-metrics"><img src="https://agentmods.dev/badge/skills/skenetechnologies/plg-skills/plg-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/skills/skenetechnologies/plg-skills/plg-metrics"><img src="https://agentmods.dev/badge/skills/skenetechnologies/plg-skills/plg-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 | $0.00087 | $0.04613 |
| Opus 5 | $0.00044 | $0.02306 |
| Sonnet 5 | $0.00017 | $0.00923 |
| Haiku 4.5 | $0.00009 | $0.00461 |
Grade A, and why
plg-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 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 — 409 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PLG Metrics
You are a PLG metrics specialist. Build the definitive metrics framework for a product-led growth business. This skill helps you define, measure, and act on the KPIs that matter for PLG -- from acquisition through monetization and retention.
Diagnostic Questions
Before building your metrics framework, answer these questions:
- What is your business model? (freemium, free trial, open-source, reverse trial, usage-based)
- What is your primary growth loop? (viral, content-led, sales-assisted, product-led)
- What is your product's core value action? (the thing users do that delivers value)
- Who is your ideal user vs. buyer? (same person or different?)
- What is your current stage? (pre-PMF, early growth, scaling, mature)
- Do you have a sales team layered on top of PLG? (pure PLG vs. product-led sales)
- What analytics tools do you currently use?
- What metrics do you currently track, and what gaps exist?
The PLG Metrics Stack
1. Acquisition Metrics
These measure how effectively you attract new users into your product.
| Metric | Formula | Benchmark | Cadence |
|---|---|---|---|
| Signups | Count of new account creations per period | Varies by stage | Daily/Weekly |
| Signup-to-Activation Rate | (Activated users / Total signups) x 100 | 20-40% | Weekly |
| Organic vs. Paid Split | % of signups from organic channels | >60% organic is healthy for PLG | Monthly |
| Viral Coefficient (K-factor) | Invites sent per user x invite acceptance rate | K > 1 = viral growth | Monthly |
| CAC by Channel | Total channel spend / New customers from channel | Varies; PLG should have low blended CAC | Monthly |
| Signup Completion Rate | (Completed signups / Started signups) x 100 | 70-90% | Weekly |
Key insight: In PLG, your product IS your acquisition channel. Track what percentage of new signups come from product-driven sources (referrals, shared content, embeds, word-of-mouth) vs. traditional marketing.
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 · 409 lines · 87 tokens per session scan A dde7d4cb3a71
plg-metrics is a skill published in the GitHub repository SkeneTechnologies/plg-skills (19 stars, last pushed 7mo ago), licensed MIT. It adds 87 tokens to every session and 4,613 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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