BD linkfox-keepa-product-request
通过ASIN获取亚马逊商品详情，包括价格、标题、主图、上架日期、材质、重量、变体月销量及近12个月的月销数据。当用户查询亚马逊商品详情、ASIN查询、商品定价、销售排名历史、月销量趋势、商品尺寸、FBA费用、产品规格、批量ASIN查询、Keepa product details, ASIN detail lookup, monthly sales data, pricing info, product specifications, FBA fees, batch ASIN query时触发此技能。即使用户未明确提及"Keepa"，只要其需求涉及获取一个或多个亚马逊ASIN的结构化商品数据，也应触发此技能。
通过ASIN获取亚马逊商品详情，包括价格、标题、主图、上架日期、材质、重量、变体月销量及近12个月的月销数据。当用户查询亚马逊商品详情、ASIN查询、商品定价、销售排名历史、月销量趋势、商品尺寸、FBA费用、产品规格、批量ASIN查询、Keepa product details, ASIN detail…
As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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 · 6
✓ No critical or high findings
Medium and low: 6
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low Secrets in code
secret-high-entropy-tokenreferences/api.md:68High-entropy token-like string (may be an id, hash or a credential)| mont…Ago | integer | 10月前月销量 |
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low Secrets in code
secret-high-entropy-tokenreferences/api.md:69High-entropy token-like string (may be an id, hash or a credential)| mont…Ago | integer | 11月前月销量 |
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low Secrets in code
secret-high-entropy-tokenreferences/api.md:70High-entropy token-like string (may be an id, hash or a credential)| mont…Ago | integer | 12月前月销量 |
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low Dangerous commands
cmd-shell-rcreferences/onboarding.md:13Writes to a shell startup file (quoted — discussed, not commanded)- macOS zsh：`echo 'export LINKFOX_AGENT_API_KEY="<key>"' >> ~/.zshrc && source ~/.zshrc`
quoted -
low Dangerous commands
cmd-shell-rcreferences/onboarding.md:14Writes to a shell startup file (detector / deny-list definition)- Linux bash：`echo 'export LINKFOX_AGENT_API_KEY="<key>"' >> ~/.bashrc && source ~/.bashrc`
detector -
low Secrets in code
secret-high-entropy-tokenscripts/onboarding.py:49High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)or "eyJh…iJ9")
quoted
Files scanned: 6. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when")
Process rating: all ten parameters 46/100
- 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
- 20When it triggers. No condition that starts the skill
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 100Steps. 37 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1971 tokens
- 100Running it twice. No mutating operations
- low The response is described with custom markup (7 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
- +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
- -31 of 2 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +3Description length 307: enough signal without eating the budget
- +4Structure: 14 headings
- +3Step-by-step instructions: 37 items
- +4Has examples (5 code blocks)
- +4Reference files are cited in the instructions (2 of 2)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 73.