BC linkfox-mpstats-ozon-product-search
MPSTATS Ozon 俄罗斯站商品搜索与反查。按俄语关键词或 SKU 在 MPSTATS 数据库中检索 Ozon 商品，返回商品 ID、标题、品牌和卖家信息，是 Ozon 选品与竞品链路的起点。当用户提到 Ozon 选品、Ozon 商品搜索、俄罗斯电商选品、Ozon 关键词搜索、Ozon SKU 查询、MPSTATS Ozon、Ozon product search, MPSTATS Ozon, Russian marketplace, Ozon SKU lookup, Ozon keyword search 时触发此技能。即使用户未明确提到"MPSTATS"，只要其意图是在 Ozon 俄罗斯站按关键词或 SKU 发现或反查商品，也应触发此技能。
As a process C 53/100 · 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 53/100
- 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
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 70Failures and branches. 4 branches
- 100Steps. 33 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2032 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 329: 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.