BD linkfox-sorftime-amazon-product-query
基于Sorftime数据的亚马逊多维度产品搜索与筛选，涵盖14个站点，支持历史月份快照回看。当用户提到Sorftime产品搜索、亚马逊产品筛选、竞品调研、类目分析、品牌热销、卖家分析、季节性产品、历史快照回看、产品搜索、月销量月销额、ABA关键词找产品、价格范围筛选、新品发现、多条件组合筛选、product search, competitor research, category analysis, brand bestsellers, seller analysis, seasonal products, historical snapshot时触发此技能。即使用户未明确提及"Sorftime"，只要其需求涉及亚马逊产品搜索、筛选、对比或类目/品牌/卖家维度的产品探索，也应触发此技能。
基于Sorftime数据的亚马逊多维度产品搜索与筛选，涵盖14个站点，支持历史月份快照回看。当用户提到Sorftime产品搜索、亚马逊产品筛选、竞品调研、类目分析、品牌热销、卖家分析、季节性产品、历史快照回看、产品搜索、月销量月销额、ABA关键词找产品、价格范围筛选、新品发现、多条件组合筛选、product…
As a process D 41/100 · 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 · 3
✓ No critical or high findings
Medium and low: 3
-
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 41/100
- 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
- 30Running it twice. 1 mutating operations with no state check
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 85Steps. 47 steps, 1 vague phrases
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2479 tokens
- low 10 top-level sections: this looks like several domains in one skill
- 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 349: enough signal without eating the budget
- +4Structure: 16 headings
- +3Step-by-step instructions: 47 items
- +4Has examples (9 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.