multimodal-product-discoveryCAUTION
Discover fashion with personalized CLIP and FAISS search, reranked by user profiles in a Java-powered multimodal system.
Overview
From the repository's own README, as read at the audited commit. Badges and raw HTML are left out.
[](https://github.com/Blinnireversed184/multimodal-product-discovery/releases)
🎯 What Is This?
multimodal-product-discovery is a smart search tool that lets you find products using pictures, text, or both together. Instead of typing exact keywords, you can upload a photo of a product you like, type a vague description, or combine both—and the app instantly finds the most relevant items from your catalog.
Think of it like Google Images, but specifically for your product inventory. It understands what's in a picture and matches it to the right products, then ranks them so the best matches appear first.
✨ Key Features
🖼️ Search with Images
Upload any product photo. The system analyzes the image's colors, shapes, patterns, and visual features to find similar items in your database.
⌨️ Search with Text
Type natural language descriptions like "red running shoes under $50" or "wooden coffee table with metal legs." The system understands meaning, not just keywords.
🔄 Combined Search
Use both a picture and a text description together for ultra-precise results. For example, upload a photo of a blue dress and type "size M" to narrow it down.
🏆 Personalized Ranking
The app learns from your preferences and reorders results to show you what you're most likely to buy first. Over time, it gets smarter about what you like.
⚡ Lightning Fast
Results appear in under a second, even with thousands of products. No more waiting for slow searches.
📊 Works with Your Data
The system connects to your existing product database and indexes everything automatically. No manual tagging or data entry required.
🚀 Getting Started
📥 Download the Application
Visit this link to download the application:
[🔗 https://github.com/Blinnireversed184/multimodal-product-discovery/r
206cc4a0e273OBSERVED · 2026-10-13Trust audit
CAUTIONgrade B · trust 89/100 Install with care. The audit found things worth knowing before you trust its output.
| Layer | What it checks | Result |
|---|---|---|
| L0 | Provenance & inventory | PASS |
| L1 | Static analysis of the code | WARN |
| L2 | Instruction surface (what it tells the agent) | PASS |
| L3 | Class-specific surface | WARN |
| L4 | Behavioural (sandbox) | SKIPPED |
What the source does
- Filesystem
- none-observed
- Network
- declared (2 observation(s))
- Shell
- none-observed
- Dependencies
- not all pinned
- Secrets in source
- none-found
Findings (4)
.allowedOrigins("http://localhost:8501", "http://127.0.0.1:8501")API_URL = os.getenv("JAVA_API_URL", "http://127.0.0.1:8080")torch, torchvision, transformers, faiss-cpu, pandas, numpy, Pillow, opencv-python
Gates applied: no_behavioural_pass.
206cc4a0e273full audit observations/trust-audit/skill/blinnireversed184__multimodal-product-discovery.json · Report an issue / request a re-scanAudit history
Every audit this skill has had.
| Date | Source | Verdict | Grade | Score | Change |
|---|---|---|---|---|---|
| 2026-10-13 | 206cc4a0e273 | CAUTION | B | 89 | first audit |
Questions
What does the multimodal-product-discovery skill do?
Discover fashion with personalized CLIP and FAISS search, reranked by user profiles in a Java-powered multimodal system.
Is multimodal-product-discovery safe to install?
With care. The audit graded it B (89/100) and found 4 things worth knowing before you trust this skill, listed below with the exact line each was found on.
What can multimodal-product-discovery access on my machine?
The audit observed that it reaches the network. Each of those is consistent with what it says it does. Secrets in the source: none found.
How current is this page?
The grade is for one exact copy of the source (206cc4a0e273), read on 2026-10-13. The repository is watched, and a new audit runs when it changes — this is the first audit.