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 user-segmentationgit 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/user-segmentation)<a href="https://agentmods.dev/skills/skenetechnologies/plg-skills/user-segmentation"><img src="https://agentmods.dev/badge/skills/skenetechnologies/plg-skills/user-segmentation/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/user-segmentation"><img src="https://agentmods.dev/badge/skills/skenetechnologies/plg-skills/user-segmentation.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 | $0.00082 | $0.04603 |
| Opus 5 | $0.00041 | $0.02302 |
| Sonnet 5 | $0.00016 | $0.00921 |
| Haiku 4.5 | $0.00008 | $0.00460 |
Grade A, and why
user-segmentation 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.
How it starts
The opening of the file, as written. The whole thing — 474 lines — stays where its author put it; the contents beside it link to each section on GitHub.
User Segmentation
You are a user segmentation specialist. Divide your user base into meaningful groups so you can deliver the right experience, messaging, and upgrade path to each user. In PLG, segmentation is the difference between a generic product that sort of works for everyone and a personalized experience that converts each user type optimally.
1. Diagnostic Questions
Before building your segmentation strategy, answer these:
- What data do you collect about users? (Profile data, usage events, billing data, firmographic data)
- How many active users do you have? (Segments need sufficient sample sizes -- at least 100-200 users per segment)
- Do you have a way to act on segments? (Can you target messages, emails, features, or experiences by segment?)
- What decisions are you trying to inform? (Onboarding, messaging, pricing, sales outreach, feature development)
- Do you already have implicit segments? (Different plans, roles, use cases that naturally separate users)
- What is your activation metric? (Needed to segment by lifecycle stage)
- What engagement data do you track? (Feature usage, session frequency, depth of usage)
- Do you have churn prediction signals? (Declining usage, support tickets, failed payments)
2. Segmentation Dimensions
2.1 Behavioral Segmentation
| Dimension | How to Measure | Use Case |
|---|---|---|
| Usage frequency | Sessions per week, DAU/WAU/MAU ratios | Identify power users vs casual |
| Feature usage patterns | Features used, feature breadth, feature depth | Recommend features, personalize onboarding |
| Engagement depth | Time in product, actions per session, content created | Measure value received |
| Collaboration activity | Invites sent, shared content, team interactions | Identify expansion potential |
| Growth signals | Increasing usage, new feature adoption, team growth | Identify upgrade candidates |
| Decline signals | Decreasing frequency, fewer features used, shorter sessions | Identify churn risk |
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 · 474 lines · 82 tokens per session scan A b2904407a942
user-segmentation is a skill published in the GitHub repository SkeneTechnologies/plg-skills (19 stars, last pushed 7mo ago), licensed MIT. It adds 82 tokens to every session and 4,603 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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