Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Lord-hair/pyclaw --skill weathergit clone --depth 1 https://github.com/Lord-hair/pyclawWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/lord-hair/pyclaw/weather)<a href="https://agentmods.dev/skills/lord-hair/pyclaw/weather"><img src="https://agentmods.dev/badge/skills/lord-hair/pyclaw/weather/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/lord-hair/pyclaw/weather"><img src="https://agentmods.dev/badge/skills/lord-hair/pyclaw/weather.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00064 | $0.00783 |
| Opus 5 | $0.00032 | $0.00392 |
| Sonnet 5 | $0.00013 | $0.00157 |
| Haiku 4.5 | $0.00006 | $0.00078 |
Grade A, and why
weather scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
天气技能
获取当前天气状况与天气预报。
执行步骤
- 从用户消息中提取城市名称
- 使用
fetch_url工具访问wttr.in天气服务
✅ 在以下情况使用此技能：
- “今天天气怎么样？”
- “今天/明天会下雨吗？”
- “[城市] 的气温”
- “本周天气预报”
- 出行前的天气查询
何时不要使用
❌ 在以下情况不要使用此技能：
- 历史天气数据 → 使用天气档案/相关 API
- 气候分析或趋势研判 → 使用专业数据源
- 超本地微气候数据 → 使用本地传感器
- 恶劣天气预警 → 查看官方 NWS 来源
- 航空气象/海洋气象 → 使用专业服务（如 METAR 等）
位置信息
天气查询中应始终包含城市、地区或机场代码。
命令
当前天气
# 单行摘要
fetch_url("wttr.in/London?format=3")
# 详细当前状况
fetch_url("wttr.in/London?0")
# 指定城市
fetch_url("wttr.in/New+York?format=3")
天气预报
# 3 天预报
fetch_url("wttr.in/London")
# 一周预报
fetch_url("wttr.in/London?format=v2")
# 指定某一天（0=今天，1=明天，2=后天）
fetch_url("wttr.in/London?1")
输出格式选项
# 单行输出
fetch_url("wttr.in/London?format=%l:+%c+%t+%w")
# JSON 输出
fetch_url("wttr.in/London?format=j1")
# PNG 图片
fetch_url("wttr.in/London.png")
格式代码
%c— 天气状况 emoji%t— 温度%f— “体感温度”%w— 风况%h— 湿度%p— 降水%l— 地点
快速回复
“今天天气怎么样？”
fetch_url("wttr.in/London?format=%l:+%c+%t+(feels+like+%f),+%w+wind,+%h+humidity")
“会下雨吗？”
fetch_url("wttr.in/London?format=%l:+%c+%p")
“周末天气预报”
fetch_url("wttr.in/London?format=v2")
备注
- 无需 API Key（使用 wttr.in）
- 有频率限制；不要高频刷请求
- 支持大多数全球城市
- 支持机场代码：
fetch_url("wttr.in/ORD")
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 115 lines · 64 tokens per session scan A d52d11b668e8
weather is a skill published in the GitHub repository Lord-hair/pyclaw (11 stars, last pushed 6mo ago), licensed Apache-2.0. It adds 64 tokens to every session and 783 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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