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 40RTY-ai/shopify-admin-skills --skill shopify-admin-rfm-customer-segmentationgit clone --depth 1 https://github.com/40RTY-ai/shopify-admin-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/40rty-ai/shopify-admin-skills/shopify-admin-rfm-customer-segmentation)<a href="https://agentmods.dev/skills/40rty-ai/shopify-admin-skills/shopify-admin-rfm-customer-segmentation"><img src="https://agentmods.dev/badge/skills/40rty-ai/shopify-admin-skills/shopify-admin-rfm-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/40rty-ai/shopify-admin-skills/shopify-admin-rfm-customer-segmentation"><img src="https://agentmods.dev/badge/skills/40rty-ai/shopify-admin-skills/shopify-admin-rfm-customer-segmentation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00044 | $0.02064 |
| Opus 5 | $0.00022 | $0.01032 |
| Sonnet 5 | $0.00009 | $0.00413 |
| Haiku 4.5 | $0.00004 | $0.00206 |
Grade A, and why
shopify-admin-rfm-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 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 — 197 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
Performs full RFM (Recency, Frequency, Monetary) analysis across the entire customer base. Each customer is scored 1-5 on three dimensions — how recently they purchased, how often they purchase, and how much they spend — then classified into actionable segments: Champions, Loyal Customers, Potential Loyalists, At-Risk, Hibernating, and Lost. Read-only — no mutations.
Prerequisites
- Authenticated Shopify CLI session:
shopify store auth --store <domain> --scopes read_orders,read_customers - API scopes:
read_orders,read_customers
Parameters
| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
| store | string | yes | — | Store domain (e.g., mystore.myshopify.com) |
| days_back | integer | no | 365 | Lookback window for order history |
| segments | integer | no | 5 | Number of quintile buckets per dimension (3 or 5) |
| min_orders | integer | no | 1 | Minimum orders for a customer to be scored |
| tag_customers | boolean | no | false | If true, add RFM segment tag to customer (requires write_customers scope) |
| format | string | no | human | Output format: human or json |
Safety
ℹ️ Read-only by default. If
tag_customers: true, will add tags via customerUpdate mutation — usedry_run: truefirst.
RFM Segment Definitions
| Segment | R Score | F Score | M Score | Description |
|---|---|---|---|---|
| Champions | 5 | 5 | 5 | Best customers — recent, frequent, high spend |
| Loyal Customers | 3-5 | 4-5 | 4-5 | Consistent buyers with strong spend |
| Potential Loyalists | 4-5 | 2-3 | 2-3 | Recent buyers who could become loyal |
| New Customers | 5 | 1 | 1-2 | Just made first purchase |
| Promising | 4 | 1-2 | 1-2 | Recent but low frequency — nurture them |
| Need Attention | 3 | 3 | 3 | Average across all dimensions — slipping |
| About to Sleep | 2-3 | 2 | 2 | Below average recency and frequency |
| At Risk | 1-2 | 4-5 | 4-5 | Were great customers, haven't bought recently |
| Hibernating | 1-2 | 1-2 | 1-3 | Low on all dimensions — nearly lost |
| Lost | 1 | 1-2 | 1-5 | Haven't bought in a very long time |
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 · 197 lines · 44 tokens per session scan A 465fd5c37273
shopify-admin-rfm-customer-segmentation is a skill published in the GitHub repository 40RTY-ai/shopify-admin-skills (185 stars, last pushed 28d ago), licensed MIT. It adds 44 tokens to every session and 2,064 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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