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 fraud-detectiongit 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/fraud-detection)<a href="https://agentmods.dev/skills/finsilabs/awesome-ecommerce-skills/fraud-detection"><img src="https://agentmods.dev/badge/skills/finsilabs/awesome-ecommerce-skills/fraud-detection/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/fraud-detection"><img src="https://agentmods.dev/badge/skills/finsilabs/awesome-ecommerce-skills/fraud-detection.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 | $0.00031 | $0.02600 |
| Opus 5 | $0.00015 | $0.01300 |
| Sonnet 5 | $0.00006 | $0.00520 |
| Haiku 4.5 | $0.00003 | $0.00260 |
Grade A, and why
fraud-detection 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.
How it starts
The opening of the file, as written. The whole thing — 234 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fraud Detection
Overview
Payment fraud costs e-commerce merchants 2–3% of revenue through chargebacks, lost goods, and dispute fees. Effective fraud detection layers platform-native risk scoring, 3D Secure authentication, velocity checks, and manual review queues for suspicious orders. The right approach depends on your platform — Shopify includes a built-in fraud analysis tool, while WooCommerce and BigCommerce require a dedicated fraud prevention service or payment processor's fraud tools.
When to Use This Skill
- When chargeback rates exceed 0.5% of transaction volume (Visa's threshold for "excessive" disputes is 0.9%)
- When launching in a new market with unfamiliar fraud patterns
- When selling high-value, easily resold goods (electronics, gift cards, luxury items)
- When you observe account takeover patterns, card testing, or bulk bot purchases
- When building or auditing a checkout flow that processes card-not-present transactions
Core Instructions
Step 1: Determine the merchant's platform and choose the right fraud tools
| Platform | Built-in Fraud Analysis | Recommended Fraud Service |
|---|---|---|
| Shopify | Shopify Fraud Analysis (included free); basic risk scoring on orders | Enable Stripe Radar or Signifyd (Shopify App Store) for advanced ML scoring |
| WooCommerce | None built in | Use Stripe (with Radar) or Braintree as payment processor; or install Kount or NoFraud plugin |
| BigCommerce | Payment processor fraud tools (varies by processor) | Signifyd integrates natively with BigCommerce; NoFraud also supports BigCommerce |
| All platforms | — | Stripe Radar (if using Stripe) provides ML-based fraud scoring on every charge at no extra cost |
Step 2: Enable and configure platform-native fraud tools
Shopify
Shopify includes a Fraud analysis indicator on every order based on signals like IP/billing address mismatch, card verification failure, and known fraud patterns.
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/composite-fraud-scoring-and-3ds-enforcem/criteria.json 2.3 KB
- evals/composite-fraud-scoring-and-3ds-enforcem/task.md 1.8 KB
- evals/manual-review-queue-with-authorize-only-/criteria.json 2.8 KB
- evals/manual-review-queue-with-authorize-only-/task.md 1.7 KB
- evals/redis-velocity-check-thresholds-and-ttl/criteria.json 2.6 KB
- evals/redis-velocity-check-thresholds-and-ttl/task.md 1.5 KB
- tile.json 228 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.
- 9d ago First seen · 234 lines · 31 tokens per session scan A a548aac10874
fraud-detection is a skill published in the GitHub repository finsilabs/awesome-ecommerce-skills (52 stars, last pushed 6mo ago), licensed MIT. It adds 31 tokens to every session and 2,600 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 2026-09-03.
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