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BC amazon-product-research

亚马逊选品调研 Skill：用评论数据验证一个品类值不值得做——采集在售竞品评论， 看买家的抱怨集中在哪、现有产品没解决什么、愿意为什么加价， 把拍脑袋选品变成有证据的需求判断。 本包同时提供 ARI 完整评论分析能力：订阅 ASIN 并采集评论、浏览与筛选评论、 查看星级/关键词/趋势与雷达图表、生成 VOC 与深度洞察等分析报告、 运行商品运营工作流、商品变化监控（watch）、差评预警与差评工作台、 行业对标与类目排行，并导出评论 CSV 与报告 Markdown/HTML。 Use when the user asks about product research, niche validation, market research, opportunity sizing, 选品、选品调研、市场调研、 需求验证、蓝海分析、品类机会、产品定位。 Requires an ARI API key (ari_live_*).

ClawHub Agent Skills author: funewa v1.4.13 MIT-0 9 files · 1 script body ≈ 2 398 tokens Open the sourceclawhub.ai↗ analyzed 8 d ago

亚马逊选品调研 Skill：用评论数据验证一个品类值不值得做——采集在售竞品评论， 看买家的抱怨集中在哪、现有产品没解决什么、愿意为什么加价， 把拍脑袋选品变成有证据的需求判断。 本包同时提供 ARI 完整评论分析能力：订阅 ASIN 并采集评论、浏览与筛选评论、 查看星级/关键词/趋势与雷达图表、生成 VOC…

As a process C 52/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches

IntegrationData and analyticstype 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
90/100
safety, quality, tests
Safety 60%
90
Quality 40%
89
Run on models
none yet
Process rating
C
52/100
Has gaps
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

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Exfiltration medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The instructions or scripts send data out: environment variables, keys, file contents, chat history. You may never notice, because the agent performs the upload as "part of the task".

For the author

If the upload is not needed for the task, remove it: catalogs flag such skills and delist them. If it is needed, name the destination explicitly, say what leaves the machine, and give the user a switch.

Broad scope medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The skill asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.

For the author

Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.

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
    • medium Exfiltration net-redirectable-api-key scripts/ari.py:82
      Helper sends the API key to a host configured by an environment variable — the key can be redirected to another server
      API key + configurable base URL from environment
    • medium Broad scope meta-broad-allowed-tools SKILL.md:1
      Broad tool permissions pre-approved: Bash
      allowed-tools: Bash

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

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "display_name"
    • note frontmatter-key unknown frontmatter key "agent_created"

    Process rating: all ten parameters 52/100

    • 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
    • 30Running it twice. 6 mutating operations with no state check
    • 40Consistency. Frontmatter name (amazon-product-research) differs from the folder (product-research)
    • 70When it triggers. States when to use, but not when not to
    • 100Tools and files. Tools declared in frontmatter
    • 100Steps. 70 steps
    • 100Execution cost. Instruction body is 2398 tokens
    • low 11 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)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +3Description length 418: enough signal without eating the budget
    • +4Structure: 12 headings
    • +3Step-by-step instructions: 70 items
    • +4Has examples (1 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)
    • +3All 1 scripts are documented

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

    External checks

    ClawHub: suspicious
    The skill is a real ARI Amazon review research integration, but it can spend ARI credits through service-side auto-confirm rules without fresh per-operation approval.
    LLM: suspicious (high) · VirusTotal: · 17 Sept 2026