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 charlieviettq/awesome-agent-skill --skill ecom-rfm-analysisgit clone --depth 1 https://github.com/charlieviettq/awesome-agent-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/charlieviettq/awesome-agent-skill/ecom-rfm-analysis)<a href="https://agentmods.dev/skills/charlieviettq/awesome-agent-skill/ecom-rfm-analysis"><img src="https://agentmods.dev/badge/skills/charlieviettq/awesome-agent-skill/ecom-rfm-analysis/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/charlieviettq/awesome-agent-skill/ecom-rfm-analysis"><img src="https://agentmods.dev/badge/skills/charlieviettq/awesome-agent-skill/ecom-rfm-analysis.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 | $0.00087 | $0.01344 |
| Opus 5 | $0.00044 | $0.00672 |
| Sonnet 5 | $0.00017 | $0.00269 |
| Haiku 4.5 | $0.00009 | $0.00134 |
Grade A, and why
"ecom-rfm-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 9d 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.
This is a copy
92% identical to ecom-rfm-analysis — 8 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.
RFM Analysis
Overview
RFM segments customers based on three behavioral dimensions: Recency (when they last bought), Frequency (how often they buy), and Monetary (how much they spend). It converts raw transaction data into actionable customer segments for targeted marketing.
Framework
IRON LAW: RFM Uses ACTUAL Behavior, Not Demographics
RFM is behavioral segmentation — it classifies by what customers DO,
not who they ARE. A 25-year-old and a 65-year-old in the same RFM segment
should receive the same treatment. Never mix RFM with demographic
assumptions.
The Three Dimensions
| Dimension | What It Measures | How to Calculate |
|---|---|---|
| Recency (R) | Days since last purchase | Today - Last purchase date |
| Frequency (F) | Number of purchases in period | Count of distinct transactions |
| Monetary (M) | Total spend in period | Sum of transaction values |
Scoring Method (Quintile-Based)
- For each dimension, rank all customers and divide into 5 equal groups (quintiles)
- Score 5 (best) to 1 (worst): R=5 means most recent, F=5 means most frequent, M=5 means highest spend
- Combine into 3-digit RFM score (e.g., R5-F4-M5 = recent, frequent, high-value)
Note: For Recency, LOWER days = HIGHER score (more recent is better).
Key Segments
| Segment | RFM Pattern | Description | Strategy |
|---|---|---|---|
| Champions | R5, F5, M5 | Best customers, recent, frequent, high-value | Reward, loyalty program, early access |
| Loyal | R4-5, F4-5, M3-5 | Consistent buyers | Upsell, cross-sell, referral program |
| Potential Loyalists | R4-5, F2-3, M2-3 | Recent, moderate frequency | Nurture to increase frequency |
| At Risk | R2-3, F3-5, M3-5 | Were frequent/high-value, not buying recently | Win-back campaign, special offers |
| Hibernating | R1-2, F1-2, M1-2 | Long dormant, low value | Low-cost reactivation or let go |
| New Customers | R5, F1, M1-2 | Just made first purchase | Onboarding, second-purchase incentive |
What ships with it
4 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.
- 9d ago First seen · 117 lines · 87 tokens per session scan A cb3d06afd964
"ecom-rfm-analysis" is a skill published in the GitHub repository charlieviettq/awesome-agent-skill (26 stars, last pushed 1mo ago), licensed MIT. It adds 87 tokens to every session and 1,344 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 92% identical to ecom-rfm-analysis, differing in 8 lines, and is treated as a copy.
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