pyr0ball opened issue Circuit-Forge/snipe#21

pyr0ball
Sat, 04 Apr 2026 18:43:45 -0700

21#feat: vision classification pipeline — condition scoring, listing quality, fraud signals#

Summary

Snipe currently runs basic vision tasks (photo analysis, serial number check). A proper classification pipeline using cf-core's LLMRouter + CFOrchClient would enable:

Condition scoring — structured assessment from listing photos (box condition, visible wear, completeness)

Listing quality signals — photo count/quality, description completeness, seller history indicators

Fraud/misrepresentation signals — stock photo detection, description/image mismatch, VERO risk

Category-aware classification — electronics vs. collectibles vs. antiques have different condition rubrics

Architecture

Build on top of circuitforge_core.documents (Dolphin-v2 ingestion pipeline) + circuitforge_core.llm.router.LLMRouter for structured output. The existing app/tasks/runner.py + tasks/scheduler.py shim is the right integration point.

Relationship to trust score

The existing eBay trust score is seller-reputation-based. This adds a listing-quality layer. Both feed into a combined confidence score shown to the user before they bid.

Phases

1. Structured condition scoring from photos (Dolphin-v2 via cf-docuvision)

2. Description/photo consistency check (LLMRouter structured output)

3. Fraud signal flagging (stock photo fingerprint, VERO keyword scan)

4. Combined confidence score UI

Related

circuitforge_core.documents — Dolphin-v2 ingestion

circuitforge_core.llm.router — structured output

circuitforge_core.resources.CFOrchClient — VRAM allocation for vision tasks

cf-core#8 (cf-docuvision service, merged)

Existing vision tasks: app/tasks/runner.py