AD aicloud-thought-proxy
云思客（AIcloud-thought-proxy）——通过操控浏览器访问网页版 AI（DeepSeek、Kimi、豆包、通义千问、ChatGPT、Claude、Gemini、Grok 等）与本地 Agent 协同工作以节省 tokens。触发场景：用户要求"用浏览器打开某 AI 官网对话并协作"、"让网页版 AI 规划步骤/编写代码/逻辑推理、本地 Agent 执行"、"节省 tokens"、提到"云思客"等。自动检测浏览器内核（Chromium → chrome-mcp/BrowserSkill；Gecko → GeckoDriver + Marionette），引导用户选择模型/思考模式/联网搜索（含"最新/最强模型"等模糊语言解析），提示用户手动登录与人机验证，建立"网页 AI 出方案、本地 Agent 执行"的协作循环。
云思客（AIcloud-thought-proxy）——通过操控浏览器访问网页版 AI（DeepSeek、Kimi、豆包、通义千问、ChatGPT、Claude、Gemini、Grok 等）与本地 Agent 协同工作以节省 tokens。触发场景：用户要求"用浏览器打开某 AI 官网对话并协作"、"让网页版 AI…
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: 10. 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 "agent_created"
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 (node) that frontmatter does not declare
- 100Steps. 84 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1784 tokens
- 100Running it twice. No mutating operations
- low 13 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
- +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
- +5Description quotes 5 example trigger phrases
- +3Description length 370: enough signal without eating the budget
- +4Structure: 21 headings
- +3Step-by-step instructions: 84 items
- +4Has examples (2 code blocks)
- +4Reference files are cited in the instructions (5 of 5)
- +3All 1 scripts are documented
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.