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AD amazon-review-report

亚马逊评论分析报告 Skill：一条指令走完采集到出报告的全流程， 生成 VOC、深度洞察、趋势、变体、竞品对比等类型的结构化报告并自动归档， 随时可回看历史报告或导出分享。Use when the user asks to generate an Amazon review report, VOC report, analysis deliverable, 评论报告、分析报告、VOC 报告、洞察报告、 报告生成、一键分析。Requires an ARI API key (ari_live_*).

ClawHub Agent Skills author: funewa v1.4.9 MIT-0 9 files body ≈ 2 415 tokens Open the sourceclawhub.ai↗ analyzed 3 h ago

亚马逊评论分析报告 Skill：一条指令走完采集到出报告的全流程， 生成 VOC、深度洞察、趋势、变体、竞品对比等类型的结构化报告并自动归档， 随时可回看历史报告或导出分享。Use when the user asks to generate an Amazon review report, VOC report…

As a process D 45/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, failures and branches

AnalyzerData 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
A
93/100
safety, quality, tests
Safety 60%
95
Quality 40%
89
Run on models
none yet
Process rating
D
45/100
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

The same skill appears in 1 more place: ClawHub

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.

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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • 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

    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"
    • note frontmatter-key unknown frontmatter key "slug"
    • note frontmatter-key unknown frontmatter key "displayName"
    • note frontmatter-key unknown frontmatter key "summary"

    Process rating: all ten parameters 45/100

    • 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-review-report) differs from the folder (review-report)
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 100Steps. 70 steps
    • 100Execution cost. Instruction body is 2415 tokens
    • low 10 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
    • +2Single-language instructions
    • +3Description length 251: enough signal without eating the budget
    • +4Structure: 11 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
    • +1License stated

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

    External checks

    ClawHub: suspicious
    The skill mostly does what it claims, but it can run credit-consuming report generation without fresh confirmation and can change future spending-confirmation settings from natural-language requests.
    LLM: suspicious (high) · 13 Sept 2026