BC linkfox-sif-asin-keywords
使用SIF数据反查任意亚马逊ASIN的流量关键词,包括自然排名、广告排名、搜索量、流量占比、自然/付费得分、ABA TOP3点击集中度、点击转化率、搜索量同比涨跌及周/月时间窗。当用户提到ASIN关键词分析、ASIN反查、流量关键词研究、自然排名查询、广告排名查询、关键词位置追踪、SIF关键词数据、竞品关键词窥探、查看哪些关键词为产品带来流量、分析特定ASIN的关键词表现、按周/月/最近N天的关键词时间窗、ASIN reverse keyword lookup, traffic keywords, organic ranking, ad ranking, search volume, SIF keywords, competitor keyword reverse lookup, click concentration, click-to-purchase conversion, week-over-week search volume时触发此技能。即使用户未明确提及"SIF",只要其需求涉及查找与特定亚马逊商品(ASIN)关联的关键词,也应触发此技能。
As a process C 50/100 · Has gaps — 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 50/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 3 mutating operations with no state check
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 65Failures and branches. 3 branches
- 100Steps. 77 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 3932 tokens
- low 11 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 484: enough signal without eating the budget
- +4Structure: 18 headings
- +3Step-by-step instructions: 77 items
- +4Has examples (8 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.