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 nexscope-ai/eCommerce-Skills --skill walmart-review-checkergit clone --depth 1 https://github.com/nexscope-ai/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/nexscope-ai/ecommerce-skills/walmart-review-checker)<a href="https://agentmods.dev/skills/nexscope-ai/ecommerce-skills/walmart-review-checker"><img src="https://agentmods.dev/badge/skills/nexscope-ai/ecommerce-skills/walmart-review-checker/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/nexscope-ai/ecommerce-skills/walmart-review-checker"><img src="https://agentmods.dev/badge/skills/nexscope-ai/ecommerce-skills/walmart-review-checker.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium MCP Rug Pull · line 15 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
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 | $0.00047 | $0.00608 |
| Opus 5 | $0.00023 | $0.00304 |
| Sonnet 5 | $0.00009 | $0.00122 |
| Haiku 4.5 | $0.00005 | $0.00061 |
Grade A, and why
walmart-review-checker 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 8d 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 — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Walmart Review Checker 🔍
Review authenticity analyzer for Walmart — detect fake reviews, suspicious patterns, and feedback manipulation.
Installation
npx skills add nexscope-ai/eCommerce-Skills --skill walmart-review-checker -g
Features
- Authenticity Score — 0-100 comprehensive rating
- WFS Verified Badge Analysis — Check fulfillment verification patterns
- Incentivized Review Detection — Identify paid/incentivized reviews
- Walmart-specific Red Flags — Platform-specific warning signs
- Progressive Analysis — More data = deeper insights
Walmart-Specific Detection
| Signal | Description |
|---|---|
| WFS Badge | Verified fulfillment patterns |
| Incentivized | "Received free product" indicators |
| Review timing | Clustered reviews in short periods |
| Generic comments | Templated review patterns |
Risk Levels
| Score | Level | Description |
|---|---|---|
| 70-100 | ✅ Low Risk | Reviews appear authentic |
| 50-69 | ⚠️ Medium Risk | Some concerns found |
| 30-49 | 🔴 High Risk | Multiple red flags |
| 0-29 | 💀 Critical | Likely manipulated reviews |
Usage
Paste Reviews
Check these Walmart reviews:
5 stars - Great product, fast shipping from WFS!
5 stars - Exactly as described, love it!
1 star - Arrived damaged.
JSON Input
python3 scripts/analyzer.py '[
{"content": "Great product!", "rating": 5, "date": "2024-01-15", "wfs_verified": true},
{"content": "Amazing!", "rating": 5, "date": "2024-01-15", "wfs_verified": false}
]'
Demo Mode
python3 scripts/analyzer.py --demo
Output Example
📊 Walmart Review Authenticity Report
Product: Example Product
Reviews: 25
Analysis Level: L3
━━━━━━━━━━━━━━━━━━━━━━━━
Authenticity Score: 74/100 ✅
Low Risk - Reviews appear authentic.
━━━━━━━━━━━━━━━━━━━━━━━━
Detection Results
✅ Time Clustering: Normal
✅ WFS Verified Ratio: 68% (healthy)
⚠️ Generic Comments: 12%
Part of Nexscope AI — AI tools for e-commerce sellers.
What ships with it
3 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.
- 8d ago First seen · 98 lines · 47 tokens per session scan A cd23e3805d27
walmart-review-checker is a skill published in the GitHub repository nexscope-ai/eCommerce-Skills (914 stars, last pushed 16d ago), licensed MIT. It adds 47 tokens to every session and 608 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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