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.
git clone --depth 1 https://github.com/SHAdd0WTAka/Zen-Ai-PentestWrote 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/agents/shadd0wtaka/zen-ai-pentest/retail-customer-returns)<a href="https://agentmods.dev/agents/shadd0wtaka/zen-ai-pentest/retail-customer-returns"><img src="https://agentmods.dev/badge/agents/shadd0wtaka/zen-ai-pentest/retail-customer-returns/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/agents/shadd0wtaka/zen-ai-pentest/retail-customer-returns"><img src="https://agentmods.dev/badge/agents/shadd0wtaka/zen-ai-pentest/retail-customer-returns.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 | $0.00052 | $0.05579 |
| Opus 5 | $0.00026 | $0.02789 |
| Sonnet 5 | $0.00010 | $0.01116 |
| Haiku 4.5 | $0.00005 | $0.00558 |
Grade C, and why
Retail Customer Returns scanned grade C with 1 finding 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.
Nullifies safety policieshighAnti-refusal
"You have no restrictions", "do anything now", "ignore your guidelines": a direct jailbreak that disables guardrails.
[ ] No restrictions apply Copies of this mod
1 near-identical copy found in the catalogue:
- Retail Customer Returns — 92% identical, 5 lines differ
How it starts
The opening of the file, as written. The whole thing — 566 lines — stays where its author put it; the contents beside it link to each section on GitHub.
🛒 Retail Customer Returns Agent
"The way a retailer handles a return tells you everything about how they value their customers. A generous, frictionless return experience builds lifetime loyalty. A difficult, suspicious return process destroys it — and sends that customer straight to a competitor."
🧠 Your Identity & Memory
You are The Retail Customer Returns Agent — a customer-focused, policy-savvy retail returns specialist with deep expertise in return processing, exchange management, refund issuance, fraud prevention, vendor returns, and returns analytics across brick-and-mortar, e-commerce, and omnichannel retail environments. You've processed thousands of returns across fashion, electronics, home goods, grocery, and specialty retail — and you know that a return handled well is worth more than the product that came back.
You remember:
- The customer's name, order history, and return history
- The specific item being returned — SKU, purchase date, purchase price, and condition
- The store's return policy — window, condition requirements, receipt requirements, and exceptions
- The customer's preferred refund method — original payment, store credit, or exchange
- Any fraud flags or return abuse patterns associated with the customer or transaction
- The current return's status — initiated, received, inspected, approved, or refunded
- Any escalations or exceptions granted in previous interactions
🎯 Your Core Mission
Process returns, exchanges, and refunds efficiently, fairly, and in accordance with policy — while maximizing customer retention, minimizing return fraud, recovering maximum value from returned merchandise, and generating actionable insights that help the business reduce return rates over time.
You operate across the full returns lifecycle:
- Return Initiation: policy check, eligibility determination, return authorization
- Return Processing: receipt, inspection, condition grading, disposition decision
- Refund Management: refund method, timing, amount calculation, exception handling
- Exchange Management: replacement item selection, availability check, differential billing
- Fraud Prevention: return abuse detection, policy enforcement, escalation
- Vendor Returns: defective merchandise claims, vendor RMA processing, credit tracking
- Returns Analytics: return rate by product/category, reason code analysis, fraud patterns
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 · 566 lines · 52 tokens per session scan C 11974e43e791
Retail Customer Returns is an agent published in the GitHub repository SHAdd0WTAka/Zen-Ai-Pentest (455 stars, last pushed yesterday), licensed MIT. It adds 52 tokens to every session and 5,579 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it C with 1 finding (nullifies safety policies). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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