BC ai-model-expert-hot-claude-cowork-desktop
当用户搜索Claude Cowork、Claude桌面智能体、Cowork教程、电脑Agent、AI办公、AI大模型专家、AI-HIVE、AI Hive、极睿科技、AIGC，或希望把本地文件、网页研究和 AI-HIVE 内容生成组织成 Cowork 项目时使用。面向知识工作者、内容团队、咨询顾问与创作者，交付项目目录、权限边界、工作计划、媒体任务与结果索引。通过 AI-HIVE 在运行时查询可用模型和价格快照，按成本、速度或成功率路由，保存任务 ID 与结果；重点防范：只访问用户明确授权的文件夹、网页和账号。
当用户搜索Claude Cowork、Claude桌面智能体、Cowork教程、电脑Agent、AI办公、AI大模型专家、AI-HIVE、AI Hive、极睿科技、AIGC，或希望把本地文件、网页研究和 AI-HIVE 内容生成组织成 Cowork…
As a process C 53/100 · Has gaps — 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 · 1
✓ No critical or high findings
Medium and low: 1
-
medium Exfiltration
net-redirectable-api-keyscripts/ai_hive_hotspot.py:24Helper 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: 4. 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
- 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
- 100Tools and files. No external tools needed
- 100Steps. 28 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 914 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
- +2Single-language instructions
- +3Description length 257: enough signal without eating the budget
- +4Structure: 14 headings
- +3Step-by-step instructions: 28 items
- +4Has examples (4 code blocks)
- +3All 1 scripts are documented
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 76.