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AI-powered Scientific Internet Access engine for OpenClaw. AI驱动的科学上网术——你的私人科学上网管家。 自动抓取免费节点、测速、筛选，一步步引导小白完成配置。 官网: https://shadowrocket.ai 推荐搭配Claude模型使用，指令遵循更准确。 Trigger on: VPN, proxy, 翻墙, 科学上网, 梯子, 节点, shadowsocks, v2ray, vmess, vless, trojan, clash, surge, shadowrocket, subscribe, subscription, GFW, firewall, 免费节点, 机场, 上不了外网, 上不了Google, 怎么翻墙, 有什么节点, 帮我上Google, 连不上外网, 看不了YouTube, 打不开谷歌, internet freedom, censorship bypass, proxy nodes, free nodes, 给我个节点, 有没有能用的节点, 我想上外网, 怎么上Google, 科学上网怎么弄, VPN怎么用, 有免费VPN吗, 电报上不了, 电报连不上, Telegram连不上, 电报代理, 电报中文, Telegram中文

modbender/skill-library-mcp Agent Skills author: modbender MIT 7 files body ≈ 321 tokens Open the sourcegithub.com↗ analyzed 2 d ago

AI-powered Scientific Internet Access engine for OpenClaw.

As a process D 48/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, failures and branches

ProcedureTelegramYouTubeSoftware developmenttype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
76
Run on models
none yet
Process rating
D
48/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • 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: 7. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "homepage"
    • note frontmatter-key unknown frontmatter key "repository"

    Process rating: all ten parameters 48/100

    • 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
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 75Steps. 3 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 321 tokens
    • 100Running it twice. No mutating operations

    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
    • +4No input/output examples
    • -33 of 4 scripts are never mentioned in SKILL.md
    • +2Single-language instructions
    • +3Description length 565: enough signal without eating the budget
    • +4Structure: 7 headings
    • +3Step-by-step instructions: 3 items
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 76.