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 agentmods add skills/thuong-nc/perlytics-skill/segmentation-analysisnpx skills add thuong-nc/perlytics-skill --skill segmentation-analysisgit clone --depth 1 https://github.com/thuong-nc/perlytics-skillWrote 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/thuong-nc/perlytics-skill/segmentation-analysis)<a href="https://agentmods.dev/skills/thuong-nc/perlytics-skill/segmentation-analysis"><img src="https://agentmods.dev/badge/skills/thuong-nc/perlytics-skill/segmentation-analysis.svg" alt="Measured on agentmods" 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 | $0.00026 | $0.01371 |
| Opus 5 | $0.00013 | $0.00685 |
| Sonnet 5 | $0.00005 | $0.00274 |
| Haiku 4.5 | $0.00003 | $0.00137 |
Grade A, and why
segmentation-analysis 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 5d 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 — 111 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Segmentation Analysis
Purpose
Identify and characterize meaningful groups in a population in ways that support different decisions for each group.
When to use
Use this skill when:
- asked "who are our best customers?" or "how do we group our users?"
- building targeting lists for different campaigns, offers, or interventions
- conducting RFM (Recency, Frequency, Monetary) analysis
- evaluating whether different customer segments have different needs or behaviors
- deciding which users to prioritize for retention, upsell, or onboarding attention
When not to use
Do not use this skill when:
- the goal is to understand why a KPI changed (use
root-cause-analysis) - the goal is to predict future behavior for individual entities (requires a predictive model)
- segments are already defined and documented - just apply them rather than re-deriving
Required thinking discipline
- A segment is only useful if you would do something different for each group. If the action is the same for all groups, the segmentation adds no value.
- Define the decision use case before defining the segments. Segments derived backward from a decision question are more actionable than segments derived from data patterns alone.
- Validate segment stability. A segmentation scheme that reclassifies 60% of customers month over month is operationally unusable.
- Name segments by behavior, not by rank. "High-value retained buyers" is more actionable than "Tier 1."
- Evidence constraint: Every conclusion must cite specific data — a number, a rate, a segment, or a timeframe. Do not speculate without evidential basis. If data is insufficient, state what is missing rather than asserting an unsupported inference.
Workflow
-
Define the decision use case: Why are we segmenting? What action will differ by segment? (Examples: different email cadences, different onboarding tracks, different discount thresholds, different retention interventions.)
-
Choose the segmentation basis:
- Behavioral: what the entity does (purchase frequency, feature usage, engagement level, product mix)
- RFM: Recency (when did they last transact?), Frequency (how often?), Monetary (how much value?)
- Attribute-based: firmographic (industry, size, geography), demographic, plan type
- Lifecycle stage: new, active, at-risk, churned, reactivated
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 5d ago First seen · 111 lines · 26 tokens per session scan A 2b4790d57bc1
segmentation-analysis is a skill published in the GitHub repository thuong-nc/perlytics-skill (5 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 26 tokens to every session and 1,371 once invoked, about $0.0001 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 2026-08-31.
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