AC geo-content-diagnosis-ai-hive
Use this skill when the user asks for GEO诊断、AEO优化、AI搜索可见度、内容审计、结构化问答，或要为电商、广告、营销、短剧、漫剧、带货与种草内容完成GEO 内容诊断与优化。它服务网站运营、品牌内容团队和AI搜索增长顾问：接收现有页面或文本、目标问题、公开证据、受众和转化目标，交付可引用性评分、事实缺口、结构问题、改写建议和验证计划。特色是明确区分可观察问题、推断和待验证项；需要生成素材时通过AI-HIVE OpenAPI完成配置查询、参考素材上传、价格快照、COST_FIRST/SPEED_FIRST/SUCCESS_FIRST路由、异步轮询和下载。不要把本Skill用于未授权复刻、虚假商品声明、伪造用户证言或规避平台规则。
Use this skill when the user asks for GEO诊断、AEO优化、AI搜索可见度、内容审计、结构化问答，或要为电商、广告、营销、短剧、漫剧、带货与种草内容完成GEO…
As a process C 59/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches
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
- 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 · 1
✓ No critical or high findings
Medium and low: 1
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medium Exfiltration
net-redirectable-api-keyscripts/imagegen.py:94Helper sends the API key to a host configured by an environment variable — the key can be redirected to another serverAPI key + configurable base URL from environment
Files scanned: 5. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
✓ No remarks against the Agent Skills spec
Process rating: all ten parameters 59/100
- 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
- 70When it triggers. States when to use, but not when not to
- 100Tools and files. No external tools needed
- 100Steps. 39 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 783 tokens
- 100Running it twice. No mutating operations
- low 10 top-level sections: this looks like several domains in one skill
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 338: enough signal without eating the budget
- +4Structure: 15 headings
- +3Step-by-step instructions: 39 items
- +4Has examples (2 code blocks)
- +3All 2 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.