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-analyticsgit 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-analytics)<a href="https://agentmods.dev/skills/finsilabs/awesome-ecommerce-skills/customer-analytics"><img src="https://agentmods.dev/badge/skills/finsilabs/awesome-ecommerce-skills/customer-analytics/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-analytics"><img src="https://agentmods.dev/badge/skills/finsilabs/awesome-ecommerce-skills/customer-analytics.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 | $0.00026 | $0.02465 |
| Opus 5 | $0.00013 | $0.01233 |
| Sonnet 5 | $0.00005 | $0.00493 |
| Haiku 4.5 | $0.00003 | $0.00247 |
Grade A, and why
customer-analytics 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 — 191 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Customer Analytics
Overview
Customer analytics transforms raw order data into actionable insights about purchase patterns, lifecycle stages, and churn risk. The core analyses — RFM scoring, cohort retention, purchase frequency, and churn prediction — reveal which customers are loyal, which are at risk, and which channels produce the best long-term customers.
This skill guides you through running these analyses using your platform's built-in tools and dedicated analytics apps, with data warehouse approaches for stores that need deeper segmentation.
When to Use This Skill
- When the marketing team needs data-driven segments beyond simple demographic filters
- When calculating at-risk customer counts for quarterly business reviews
- When measuring the impact of loyalty programs on purchase frequency
- When identifying the acquisition channels that produce the highest-LTV customers
- When preparing customer health dashboards for account management or VIP programs
- When building cohort retention analysis to understand customer lifetime value trends
Core Instructions
Step 1: Choose the right tool for your platform
| Platform | Recommended Tool | What It Provides |
|---|---|---|
| Shopify | Klaviyo + Shopify's built-in customer segments | RFM-style segments, purchase frequency, CLV prediction, cohort reports |
| Shopify (advanced) | Lifetimely or Triple Whale | True cohort LTV, CLV by acquisition channel, retention curves |
| WooCommerce | Metorik | Customer segmentation, RFM analysis, cohort retention, churn identification |
| WooCommerce (email) | Klaviyo for WooCommerce | Behavioral segments + automated flows based on customer lifecycle stage |
| BigCommerce | Klaviyo for BigCommerce + Glew.io | Glew provides cohort analysis and CLV tracking natively for BigCommerce |
| All platforms (data-first) | Export to Google Looker Studio + BigQuery via Fivetran | Full SQL-based analysis; required for advanced RFM and cohort modeling |
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/acquisition-channel-quality-and-cohort-r/criteria.json 3.1 KB
- evals/acquisition-channel-quality-and-cohort-r/task.md 2.5 KB
- evals/churn-prediction-scoring-algorithm/criteria.json 3.2 KB
- evals/churn-prediction-scoring-algorithm/task.md 2.0 KB
- evals/rfm-scoring-pipeline-and-segment-classif/criteria.json 2.8 KB
- evals/rfm-scoring-pipeline-and-segment-classif/task.md 1.9 KB
- tile.json 229 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.
- 12d ago First seen · 191 lines · 26 tokens per session scan A 800815d11482
customer-analytics is a skill published in the GitHub repository finsilabs/awesome-ecommerce-skills (52 stars, last pushed 6mo ago), licensed MIT. It adds 26 tokens to every session and 2,465 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 2026-08-30.
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