Amazon Listing Judge

Grade Amazon product listing quality. Input an ASIN, get a 0-100 score with dimension breakdown (title, bullets, rating, reviews, sales velocity, BSR, badges) and improvement suggestions. Trigger on: listing quality, grade listing, listing score, 评分, 打分, 分析 listing, 亚马逊商品评分, listing grader, listing analysis.

Score any Amazon product listing on a 0–100 scale across 7 dimensions. Returns a grade card with per-dimension scores and actionable improvement suggestions.

Setup

This skill requires a CLAW_KEY — purchase one at claw-school.com.

Create a .env file in the skill root directory (same level as this SKILL.md):

text

CLAW_KEY=CLAW-XXXX-XXXX-XXXX-XXXX
CLAW_API_BASE=<provided-with-your-key>

No CLAW_KEY yet? Visit claw-school.com to get one. Each key is tied to one agent and does not expire.

Grade a listing

bash

uv run <skill-dir>/scripts/grade.py <ASIN>

Example:

bash

uv run <skill-dir>/scripts/grade.py B088FLY7S8

Scoring dimensions (100 pts total)

Dimension	Max	Logic
Title length	20	100–200 chars = 20; 50–100 or 200–250 = 12; else = 5
Bullet points	20	≥5 = 20; 3–4 = 14; 1–2 = 7; 0 = 0
Star rating	20	≥4.5 = 20; ≥4.0 = 14; ≥3.5 = 8; <3.5 = 3
Review count	15	≥10K = 15; ≥1K = 12; ≥100 = 7; <100 = 3
Sales velocity	15	"bought in past month" present = 15; absent = 0
BSR	10	Any BSR present = 10; absent = 0
Badges	10	Amazon's Choice + Best Seller = 10; either = 7; none = 0

Grade scale

Score	Grade
85–100	A — Excellent
70–84	B — Good
55–69	C — Average
40–54	D — Needs Work
0–39	F — Poor

Output format

json

{
  "asin": "B088FLY7S8",
  "title": "12 Pack Small American Flags...",
  "total_score": 82,
  "grade": "B (Good)",
  "breakdown": {
    "title": 12,
    "bullets": 20,
    "rating": 20,
    "reviews": 7,
    "sales_velocity": 15,
    "bsr": 10,
    "badges": 10
  },
  "suggestions": [
    "Title is 45 chars — optimal is 100-200 chars"
  ]
}

Interpreting results

Present the results as a structured report. Call out:

1. Total score and grade label
2. Strongest dimensions (highest scores)
3. Weakest dimensions with the suggestions
4. Overall priority action (the suggestion that would give the biggest score boost)