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通过彩云天气 API 查询天气数据 — 实时天气、逐小时/一周预报、历史天气和天气预警。当用户询问任何城市的天气、温度、空气质量、天气预报、降雨概率、历史天气或天气预警时使用此技能。需要设置 CAIYUN_WEATHER_API_TOKEN 环境变量。Use when: user asks about current weather, temperature, air quality, forecast, rain, historical weather, or alerts for any city.

modbender/skill-library-mcp Agent Skills author: modbender MIT 2 files body ≈ 380 tokens Open the sourcegithub.com↗ analyzed 2 d ago

通过彩云天气 API 查询天气数据 — 实时天气、逐小时/一周预报、历史天气和天气预警。当用户询问任何城市的天气、温度、空气质量、天气预报、降雨概率、历史天气或天气预警时使用此技能。需要设置 CAIYUNWEATHERAPITOKEN 环境变量。Use when: user asks about current…

As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

IntegrationData and analyticsSoftware 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
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
87
Run on models
none yet
Process rating
D
46/100
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: 2. 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 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 (python) that frontmatter does not declare
    • 100Steps. 15 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 380 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
    • +1No license
    • +2Single-language instructions
    • +3Description length 254: enough signal without eating the budget
    • +4Structure: 12 headings
    • +3Step-by-step instructions: 15 items
    • +4Has examples (7 code blocks)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.