CD cue-equity-incentive
用 Cue 查询和分析上市公司股权激励计划与股份回购——基于市面上最全的股权激励数据库（2015年至今10年+历史覆盖），独家特有数据源。查单个计划或要素、或一次多家公司的同类要素，可直连 Cue 数据接口秒级返回；单家公司的方案设计、实施效果、同行对比，以及全市场方案盘点与趋势分析，则生成深度报告（后者走自由式深研），用真实数据评估激励方案的竞争力与合理性。
用 Cue 查询和分析上市公司股权激励计划与股份回购——基于市面上最全的股权激励数据库（2015年至今10年+历史覆盖），独家特有数据源。查单个计划或要素、或一次多家公司的同类要素，可直连 Cue…
As a process D 49/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
What is at stake
Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.
Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.
The instructions or scripts send data out: environment variables, keys, file contents, chat history. You may never notice, because the agent performs the upload as "part of the task".
If the upload is not needed for the task, remove it: catalogs flag such skills and delist them. If it is needed, name the destination explicitly, say what leaves the machine, and give the user a switch.
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 · 2
✓ No critical or high findings
Medium and low: 2
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medium Exfiltration
net-credential-useSKILL.md:436Credential used in a network call (verify the destination is the intended service)echo "=== 2/3 Cue 服务 ===" && curl -sS --max-time 10 "https://cuecue.cn/api/health" -H "Authorization: Bearer $CUE_KEY"
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medium Exfiltration
net-credential-useSKILL.md:437Credential used in a network call (verify the destination is the intended service)echo "=== 3/3 搭子 ===" && curl -sS --max-time 10 "https://cuecue.cn/api/playbook" -H "Authorization: Bearer $CUE_KEY" | python3 -c "import sys,json;scenes=json.load(sys.stdin).get('data',{}).get('scene
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") - note
frontmatter-keyunknown frontmatter key "slug" - note
frontmatter-keyunknown frontmatter key "Name" - note
frontmatter-keyunknown frontmatter key "description_zh"
Process rating: all ten parameters 49/100
- 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
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (web, python) that frontmatter does not declare
- 70Execution cost. Instruction body is 4759 tokens
- 100Steps. 65 steps
- 100Consistency. Name and required fields are in place
- 100Running it twice. No mutating operations
- low 22 top-level sections: this looks like several domains in one skill
- low The response is described with custom markup (3 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
- +1No license
- +2Single-language instructions
- +3Description length 181: enough signal without eating the budget
- +4Structure: 35 headings
- +3Step-by-step instructions: 65 items
- +4Has examples (11 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 69.