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 finsilabs/awesome-ecommerce-skills --skill customer-segmentationgit clone --depth 1 https://github.com/finsilabs/awesome-ecommerce-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/finsilabs/awesome-ecommerce-skills/customer-segmentation)<a href="https://agentmods.dev/skills/finsilabs/awesome-ecommerce-skills/customer-segmentation"><img src="https://agentmods.dev/badge/skills/finsilabs/awesome-ecommerce-skills/customer-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/finsilabs/awesome-ecommerce-skills/customer-segmentation"><img src="https://agentmods.dev/badge/skills/finsilabs/awesome-ecommerce-skills/customer-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 | $0.00038 | $0.02623 |
| Opus 5 | $0.00019 | $0.01311 |
| Sonnet 5 | $0.00008 | $0.00525 |
| Haiku 4.5 | $0.00004 | $0.00262 |
Grade A, and why
customer-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 11d 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 — 257 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Customer Segmentation
Overview
Customer segmentation divides your customer base into groups with similar purchase behavior so marketing campaigns, promotions, and product recommendations can be precisely targeted. Klaviyo, Omnisend, and Metorik all provide RFM-style segmentation out of the box for Shopify and WooCommerce without custom SQL. Only build a custom segmentation system if your platform's tools don't support the segment logic you need.
When to Use This Skill
- When personalizing email campaigns by lifecycle stage (new, active, at-risk, lapsed)
- When building suppression lists to avoid wasting ad spend on already-converted customers
- When identifying "champion" customers for VIP programs and early access campaigns
- When analyzing which acquisition cohort has the best 90-day retention
- When syncing behavioral segments to advertising platforms (Meta, Google)
Core Instructions
Step 1: Determine platform and choose the right segmentation tool
| Platform | Built-in Segmentation | Recommended Tool |
|---|---|---|
| Shopify | Basic: Admin → Customers → Filters; Advanced: Klaviyo or Omnisend | Klaviyo for email + SMS; Lifetimely for cohort analysis |
| WooCommerce | WooCommerce Analytics → Customers (basic filters) | Klaviyo + WooCommerce plugin; or Metorik for analytics |
| BigCommerce | Customer Groups (tier-based); Analytics → Customers | Klaviyo for behavioral segmentation |
| Custom / Headless | Build RFM scoring in SQL; sync to Klaviyo for activation | Required when platform has no segmentation tools |
Step 2: Platform-specific setup
Shopify
Option A: Shopify Admin segments (basic, free)
- Go to Admin → Customers
- Use the filter bar to create segments based on:
- Order count, total spent, last order date
- Email subscription status, tags, location
- Product purchased
- Save the filter as a customer segment
- Export the segment to CSV for use in ads or email campaigns
What ships with it
7 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.
- evals/cohort-retention-analysis-and-behavioral/criteria.json 3.0 KB
- evals/cohort-retention-analysis-and-behavioral/task.md 2.7 KB
- evals/rfm-scoring-and-segment-classification/criteria.json 2.8 KB
- evals/rfm-scoring-and-segment-classification/task.md 2.2 KB
- evals/segment-export-to-marketing-platforms-wi/criteria.json 2.8 KB
- evals/segment-export-to-marketing-platforms-wi/task.md 2.5 KB
- tile.json 225 B
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.
- 11d ago First seen · 257 lines · 38 tokens per session scan A 02dbeaa609b2
customer-segmentation is a skill published in the GitHub repository finsilabs/awesome-ecommerce-skills (52 stars, last pushed 6mo ago), licensed MIT. It adds 38 tokens to every session and 2,623 once invoked, about $0.0002 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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