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AD pangolinfo-amazon-scraper

Use when: 用户要"抓 Amazon 商品/ASIN 详情" / "搜某关键词的商品列表" / "拉某类目/某卖家的在售品" / "Best Sellers / New Releases 榜单" / "批量抓评论做 VOC" / "scrape this ASIN" / "get reviews for B0XXX" / "search Amazon for X" / "category bestsellers". Covers: 通过 MCP tool 程序化抓取 Amazon 全域数据 —— ASIN 详情、关键词 SERP、类目在售品、卖家店铺、Best Sellers、New Releases、批量评论；含自定义 URL/筛选兜底。绕过验证码与封 IP，喂给 AI Agent 做自动化分析。 NOT for: 选品/找蓝海 niche 的完整 GTM（用 amazon-product-explorer）/ 类目级遥测筛选与利基挖掘（用 pangolinfo-amazon-niche）/ 写 Listing 文案（用 amazon-listing-optimization）/ 非 Amazon 平台（Walmart / Shopify 不支持）。

ClawHub Agent Skills author: Pangolinfo v4.0.0 MIT-0 2 files body ≈ 4 571 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process D 48/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, failures and branches

AnalyzerShopifyAI and agentsInfrastructuretype 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
A
94/100
safety, quality, tests
Safety 60%
98
Quality 40%
89
Run on models
none yet
Process rating
D
48/100
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

    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 · 2

    ✓ No critical or high findings

    Medium and low: 2
    • low Secrets in code secret-high-entropy-token SKILL.md:333
      High-entropy token-like string (may be an id, hash or a credential) (documentation table row)
      | `top5…max` | `top5…Max`（products 不是 brands）|
      table
    • low Secrets in code secret-high-entropy-token SKILL.md:383
      High-entropy token-like string (may be an id, hash or a credential) (documentation table row)
      | filter_niches 入参 | 传 `categoryId` | 只认 `nicheId`/`nicheTitle`;0-1 小数字段(`top5…Max`/`retu…Max`)别传整数 |
      table

    Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "mcp_tools_used"
    • note frontmatter-key unknown frontmatter key "applies_to"

    Process rating: all ten parameters 48/100

    • 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
    • 50When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
    • 70Execution cost. Instruction body is 4571 tokens
    • 100Steps. 74 steps
    • 100Consistency. Name and required fields are in place
    • 100Running it twice. No mutating operations
    • low 18 top-level sections: this looks like several domains in one skill
    • low The response is described with custom markup (5 tags): a typed call is more reliable

    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

    • +3Output format is not stated: the model decides each time
    • -243 emoji in the instructions: noise for the model
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 9 example trigger phrases
    • +4Description says when NOT to use the skill
    • +3Description length 538: enough signal without eating the budget
    • +4Structure: 43 headings
    • +3Step-by-step instructions: 74 items
    • +4Has examples (8 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.

    External checks

    ClawHub: suspicious
    This skill mostly matches its Amazon scraping purpose, but it gives conflicting instructions about whether the agent should read a Pangolinfo API key from the environment.
    LLM: suspicious (high) · 20 Aug 2026