BC linkfox-seerfar-ozon-category-search
Seerfar Ozon 类目商品搜索：按 Ozon 类目 ID 拉取该类目下的商品列表，返回类目级聚合（类目总销量、总销售额、平均价格、平均评分、季节性）与每个商品的销量、价格、评分、评论数、品牌、卖家、配送方式。用于类目选品分析、类目爆品挖掘、类目容量与价格带分析、季节性判断。当用户提到 Ozon 类目商品、Ozon 类目分析、Ozon 类目选品、Ozon 类目爆品、Ozon 类目总销量、Ozon 类目平均价格、Ozon category search, Ozon category products, category best-sellers, category analysis 时触发此技能。即使用户未明确提到"Seerfar"，只要其意图是查看某 Ozon 类目下的商品与类目级汇总数据，也应触发此技能。
Seerfar Ozon 类目商品搜索：按 Ozon 类目 ID 拉取该类目下的商品列表，返回类目级聚合（类目总销量、总销售额、平均价格、平均评分、季节性）与每个商品的销量、价格、评分、评论数、品牌、卖家、配送方式。用于类目选品分析、类目爆品挖掘、类目容量与价格带分析、季节性判断。当用户提到 Ozon…
As a process C 58/100 · 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 58/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 20When it triggers. No condition that starts the skill
- 60Tools and files. Uses tools (web, python) that frontmatter does not declare
- 100Steps. 37 steps
- 100Failures and branches. 3 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2626 tokens
- 100Running it twice. No mutating operations
- 100Progress reporting. Reports progress
- 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 361: enough signal without eating the budget
- +4Structure: 12 headings
- +3Step-by-step instructions: 37 items
- +4Has examples (6 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.