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BD amazon-review-analyzer

输入ASIN，自动抓取数百条评论，AI逐条深度打标，生成痛点/卖点/Listing优化洞察报告。支持RapidAPI数据采集、OpenAI/DeepSeek兼容API、交互式HTML报告输出。

ClawHub Agent Skills author: bettermen v1.0.0 MIT-0 9 files body ≈ 1 697 tokens Open the sourceclawhub.ai↗ analyzed 32 h ago

输入ASIN，自动抓取数百条评论，AI逐条深度打标，生成痛点/卖点/Listing优化洞察报告。支持RapidAPI数据采集、OpenAI/DeepSeek兼容API、交互式HTML报告输出。

As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerAI and agentstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
B
87/100
safety, quality, tests
Safety 60%
100
Quality 40%
68
Run on models
none yet
Process rating
D
46/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 0

✓ No critical or high findings

Files scanned: 9. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • note frontmatter-key unknown frontmatter key "triggers"
  • note frontmatter-key unknown frontmatter key "agent_created"

Process rating: all ten parameters 46/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 100Steps. 71 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1697 tokens
  • 100Running it twice. No mutating operations
  • low 13 top-level sections: this looks like several domains in one skill

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Description length 96: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • -217 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +4Structure: 26 headings
  • +3Step-by-step instructions: 71 items
  • +4Has examples (12 code blocks)
  • +3All 5 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 68.

External checks

ClawHub: suspicious
The skill does what it advertises, but it sends review data to external APIs and auto-opens an unescaped HTML report built from fetched review content, so users should review it before installing.
LLM: suspicious (medium) · 18 Jun 2026