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 returns-refund-policygit 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/returns-refund-policy)<a href="https://agentmods.dev/skills/finsilabs/awesome-ecommerce-skills/returns-refund-policy"><img src="https://agentmods.dev/badge/skills/finsilabs/awesome-ecommerce-skills/returns-refund-policy/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/returns-refund-policy"><img src="https://agentmods.dev/badge/skills/finsilabs/awesome-ecommerce-skills/returns-refund-policy.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 | $0.00030 | $0.02678 |
| Opus 5 | $0.00015 | $0.01339 |
| Sonnet 5 | $0.00006 | $0.00536 |
| Haiku 4.5 | $0.00003 | $0.00268 |
Grade A, and why
returns-refund-policy 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 — 218 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Returns & Refund Policy Engine
Overview
A returns and refund policy engine enforces your rules automatically: different return windows per product category, restocking fees for specific item types, final-sale exclusions, and extended windows for loyalty members. This prevents your customer service team from manually evaluating every return request and ensures consistent policy enforcement. Most platforms can implement these rules through their returns apps combined with product tags and customer segments.
When to Use This Skill
- When your return logic is inconsistent because it's handled case-by-case by customer service
- When you need different return windows for different product categories (electronics vs. apparel vs. consumables)
- When implementing tiered return policies where loyalty members get extended windows or waived restocking fees
- When building an automated approval workflow that handles most returns without human intervention
- When compliance or legal requirements mandate that return policies be auditable and version-controlled
Core Instructions
Step 1: Determine your platform and choose the right returns tool
| Platform | Recommended Tool | Why |
|---|---|---|
| Shopify | Loop Returns or AfterShip Returns | Loop is the most feature-complete: supports per-product-type policies, restocking fees, final-sale blocking, and loyalty tier overrides |
| WooCommerce | ReturnGo or WooCommerce Returns and Warranty Requests | ReturnGo supports custom policy rules per product category and automated approval logic |
| BigCommerce | AfterShip Returns Center or Loop Returns | Both support per-category policy rules and restocking fees |
| Custom / Headless | Build a policy evaluation engine + Shippo for return labels | Store policies in a database; evaluate them programmatically when return requests come in |
Step 2: Define your return policy rules
Before configuring any tool, define your policy matrix clearly:
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/auto-approval-workflow-and-value-cap/criteria.json 3.4 KB
- evals/auto-approval-workflow-and-value-cap/task.md 2.5 KB
- evals/policy-resolution-and-eligibility-evalua/criteria.json 3.4 KB
- evals/policy-resolution-and-eligibility-evalua/task.md 2.6 KB
- evals/policy-versioning-and-audit-trail/criteria.json 3.0 KB
- evals/policy-versioning-and-audit-trail/task.md 2.3 KB
- tile.json 238 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 · 218 lines · 30 tokens per session scan A 8a3c10821e67
returns-refund-policy is a skill published in the GitHub repository finsilabs/awesome-ecommerce-skills (52 stars, last pushed 6mo ago), licensed MIT. It adds 30 tokens to every session and 2,678 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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