BD linkfox-amazon-opportunity-report-by-keyword
按关键词查询亚马逊商业洞察报告，涵盖市场潜力、产品特征、用户评论、客户画像、搜索趋势、定价分析六大维度的AI 综合分析。当用户提到亚马逊商业洞察、市场洞察报告、选品报告、市场机会分析、竞争格局、消费者画像、定价分析、细分市场调研、Amazon opportunity report, market insight, business insight, market potential, competitive landscape, consumer behavior, pricing analysis, product selection report, niche analysis时触发此技能。即使用户未明确说"商业洞察"，只要其需求涉及对某个亚马逊关键词做全面的市场机会评估或综合性报告生成，也应触发此技能。
按关键词查询亚马逊商业洞察报告，涵盖市场潜力、产品特征、用户评论、客户画像、搜索趋势、定价分析六大维度的AI 综合分析。当用户提到亚马逊商业洞察、市场洞察报告、选品报告、市场机会分析、竞争格局、消费者画像、定价分析、细分市场调研、Amazon opportunity report, market insight…
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 · 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 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. 33 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1440 tokens
- 100Running it twice. No mutating operations
- 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 359: enough signal without eating the budget
- +4Structure: 12 headings
- +3Step-by-step instructions: 33 items
- +4Has examples (3 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.