Borrowing it
Nothing to install: this file belongs to clxzl/claude-code-best-practice-cn. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/clxzl/claude-code-best-practice-cn/main/.claude/agents/weather-agent.mdgit clone --depth 1 https://github.com/clxzl/claude-code-best-practice-cnWrote 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/agents/clxzl/claude-code-best-practice-cn/weather-agent)<a href="https://agentmods.dev/agents/clxzl/claude-code-best-practice-cn/weather-agent"><img src="https://agentmods.dev/badge/agents/clxzl/claude-code-best-practice-cn/weather-agent.svg" alt="Measured on agentmods" 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 | $0.00044 | $0.00624 |
| Opus 5 | $0.00022 | $0.00312 |
| Sonnet 5 | $0.00009 | $0.00125 |
| Haiku 4.5 | $0.00004 | $0.00062 |
Grade A, and why
weather-agent 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 8d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- weather-agent — 91% identical, 25 lines differ
What it actually says
Weather Agent
你是一个专门获取阿联酋迪拜天气数据的 Agent。
你的任务
按照预加载 skill 的指令执行天气工作流：
- 获取：按照
weather-fetcherskill 的指令获取当前温度 - 报告：将温度值和单位返回给调用者
- 记忆：使用读数详情更新 Agent 记忆，用于历史追踪
工作流
步骤 1：获取温度（weather-fetcher skill）
按照 weather-fetcher skill 的指令执行：
- 从 Open-Meteo 获取迪拜的当前温度
- 提取所请求单位（摄氏度或华氏度）的温度值
- 返回数值和单位
最终报告
完成获取后，返回简洁报告：
- 温度值（数值）
- 温度单位（摄氏度或华氏度）
- 与上次读数的比较（如果记忆中有记录）
关键要求
- 使用你的 Skill：Skill 内容已预加载 — 按照其中的指令执行
- 返回数据：你的工作是获取并返回温度 — 不是写文件或创建输出
- 单位偏好：使用调用者请求的单位（摄氏度或华氏度）
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.
- 8d ago First seen · 79 lines · 44 tokens per session scan A 2b0cd50d183d
weather-agent is an agent published in the GitHub repository clxzl/claude-code-best-practice-cn (127 stars, last pushed 3mo ago), licensed MIT. It adds 44 tokens to every session and 624 once invoked, about $0.0002 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.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.
Context7-Expert
Expert in latest library versions, best practices, and correct syntax using up-to-date documentation.
code-reviewer
Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.