AD amazon-bad-review-analysis
亚马逊差评分析 Skill：采集指定 ASIN 的评论，聚焦 1-3 星负面反馈， 拆解质量缺陷、物流破损、描述不符、使用困惑等差评根因并按出现频次排序， 给出降低退货的产品与文案改进建议，附星级分布与差评趋势图表。 Use when the user asks about negative reviews, one-star complaints, bad review analysis, 差评分析、负面评论、投诉分析、质量问题、评分下滑排查、退货诱因。 Requires an ARI API key (ari_live_*).
亚马逊差评分析 Skill：采集指定 ASIN 的评论，聚焦 1-3 星负面反馈， 拆解质量缺陷、物流破损、描述不符、使用困惑等差评根因并按出现频次排序， 给出降低退货的产品与文案改进建议，附星级分布与差评趋势图表。 Use when the user asks about negative reviews…
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
-
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-bad-review-analysis) differs from the folder (bad-review)
- 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 268: 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.