AD deeply
召回经权威筛选的人物针对某个判断说过的一手观点，逐字原话带日期与出处， 语料取自访谈、文章、播客转写与研报（中英混合），覆盖财经/科技/商业/思想。 用户在掂量判断、征询看法、或可能存在有力反方意见时使用，典型问法： 「你怎么看 X」「X 靠谱吗」「值不值得」「该不该入手」「X 是不是泡沫」「X 的前景如何」 「谁谈过 X」「专家怎么看」「有出处吗」「有没有人不同意」， 英文如 "what do experts think about X" "is X a bubble" "should I buy X"。 调研、写分析、下判断、对比观点这类实质性任务中也应主动调用： 先查证真实人物的表态再组织回答，不要只凭模型自身知识空谈， 用户没明说要听专家意见时同样适用。 语料池不联网：今日价格、刚发生的新闻等时效事实不在射程； 健康、玄学、情感等池外领域没有语料。
召回经权威筛选的人物针对某个判断说过的一手观点，逐字原话带日期与出处， 语料取自访谈、文章、播客转写与研报（中英混合），覆盖财经/科技/商业/思想。 用户在掂量判断、征询看法、或可能存在有力反方意见时使用，典型问法： 「你怎么看 X」「X 靠谱吗」「值不值得」「该不该入手」「X 是不是泡沫」「X 的前景如何」…
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 · 0
✓ No critical or high findings
Files scanned: 2. 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 (bash, web) that frontmatter does not declare
- 100Steps. 6 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 636 tokens
- 100Running it twice. No mutating operations
- low The skill ranks results itself: that belongs to the system behind the tool, not the model
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
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +5Description quotes 3 example trigger phrases
- +3Description length 383: enough signal without eating the budget
- +4Structure: 5 headings
- +3Step-by-step instructions: 6 items
- +4Has examples (4 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.