AD amazon-seller-assistant
亚马逊运营助手 Skill：用评论数据支撑日常运营决策——采集自家与竞品评论， 查看评分结构与趋势，生成 VOC 或深度洞察报告，回答差评为什么涨、卖点该怎么排、 该改产品还是改文案这类具体问题。Use when the user asks for help with Amazon seller operations, data-driven decisions, review-based diagnosis, 亚马逊运营、店铺运营、 卖家工具、运营助手、数据分析、经营诊断。Requires an ARI API key (ari_live_*).
亚马逊运营助手 Skill：用评论数据支撑日常运营决策——采集自家与竞品评论， 查看评分结构与趋势，生成 VOC 或深度洞察报告，回答差评为什么涨、卖点该怎么排、 该改产品还是改文案这类具体问题。Use when the user asks for help with Amazon seller…
As a process D 45/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, failures and branches
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.
Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.
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".
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
- 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
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medium Exfiltration
net-redirectable-api-keyscripts/ari.py:61Helper sends the API key to a host configured by an environment variable — the key can be redirected to another serverAPI 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-keyunknown frontmatter key "display_name" - note
frontmatter-keyunknown frontmatter key "agent_created" - note
frontmatter-keyunknown frontmatter key "slug" - note
frontmatter-keyunknown frontmatter key "displayName" - note
frontmatter-keyunknown 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-seller-assistant) differs from the folder (seller-assistant)
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 100Steps. 69 steps
- 100Execution cost. Instruction body is 2366 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 277: enough signal without eating the budget
- +4Structure: 11 headings
- +3Step-by-step instructions: 69 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.