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Weather Forecast Report

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staruhub
weather-forecast-report

将天气要素（风速、能见度、降雨量等）单站预报系统的项目文档、模型、报告、评估结果整理成符合省级自然科学基金项目编写规范的专题研究报告。适用于：(1) 基于Claude Code + OpenSpec开发的气象预报系统结题报告编写，(2) 将技术文档转换为学术研究报告格式，(3) 生成符合省级自然科学基金规范的专题研究报告，(4) 整合项目代码、模型、评估指标等技术材料为规范化学术报告。触发关键词："专题研究报告"、"结题报告"、"天气预报系统"、"基金项目报告"、"风速预报"、"能见度预报"、"降雨量预报"、"气象预测"、"单站预报"等。

Overview

Publisherstaruhub
RepositoryClaudeSkills
Skill nameweather-forecast-report
Stars
728
Forks
131
Bundled files
5
LicenseMIT
Links
Skill sourceRepositoryHomepage
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 5 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by staruhub on GitHub. Read the source before you install it.

Installation

Install the Weather Forecast Report AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/staruhub/ClaudeSkills/tree/main/lab/Geek-skills-weather-forecast-report
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/staruhub/ClaudeSkills.git /tmp/ClaudeSkills
mkdir -p .claude/skills
cp -r /tmp/ClaudeSkills/lab/Geek-skills-weather-forecast-report .claude/skills/weather-forecast-report
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Weather Forecast Report in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Weather Forecast Report on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Weather Forecast Report is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

天气要素专题研究报告生成器

概述

本skill用于将Claude Code + OpenSpec开发的天气要素单站预报系统相关材料（项目文档、模型文件、评估结果等）整理成符合省级自然科学基金项目编写规范的专题研究报告。

工作流程

第一步：材料收集与分析

  1. 识别输入材料类型

    • 项目文档（README、设计文档、API文档等）
    • 模型文件（.pkl、.h5、.pt等）
    • 评估报告（准确率、RMSE、MAE等指标）
    • 代码文件（数据处理、特征工程、模型训练等）
    • 可视化图表（训练曲线、预测对比图等）

  2. 确定天气要素类型

    • 风速预报
    • 能见度预报
    • 降雨量预报
    • 温度预报
    • 湿度预报
    • 气压预报

  3. 提取关键信息

    • 研究背景与科学问题
    • 技术方案与创新点
    • 实验数据与评估指标
    • 结论与应用价值

第二步：报告结构规划

参照 references/report-structure.md 中的详细结构说明，按省级自然科学基金规范组织报告。

第三步：内容撰写

  1. 阅读撰写指南：先阅读 references/writing-guidelines.md
  2. 使用报告模板：参考 assets/report-template.md
  3. 格式规范：遵循 references/format-standards.md

第四步：生成DOCX文档

使用宿主环境中的 docx skill 生成规范的 Word 文档（路径因环境而异,以宿主 skill 系统为准）,按本 skill 的格式规范产出最终报告。

关键要求

学术规范性要求

  • 一级标题：黑体四号加粗
  • 二级标题：黑体小四号
  • 正文：宋体小四号，首行缩进2字符
  • 英文与数字：Times New Roman
  • 行距：固定值26-28磅
  • 序号格式：一、（一）、1、(1)、①

内容质量要求

  • 科学性：数据准确、方法严谨、结论可靠
  • 创新性：突出技术创新点和研究贡献
  • 规范性：符合学术写作规范和基金报告要求
  • 完整性：研究逻辑链条完整闭环

图表规范

  • 图表需有编号和标题
  • 表格标题置于表格上方
  • 图片标题置于图片下方
  • 图表应在正文中被引用

验收标准（报告交付前自查）

  • 报告结构与 report-structure.md 的省级基金规范逐节对应,无缺章
  • 所有数据（准确率/RMSE/MAE 等）来自用户提供的评估材料,不编造实验数字
  • 图表全部有编号、标题且在正文中被引用
  • 格式合规:标题字体字号、正文宋体小四、行距、序号格式符合"关键要求"
  • 摘要中英文各约 400 字,关键词 3-5 个

不做什么

  • 不替用户跑模型或生成评估数据——材料不足时列出缺口向用户索取,不虚构
  • 不做气象学结论的对错判断,只负责规范化呈现
  • 非气象/非基金报告的通用学术写作,不强行套用本模板

已知陷阱

陷阱具体表现应对
编造实验数据材料缺指标时用"典型值"补齐缺口列清单向用户索取;宁可留待补充标注,不虚构
格式细节失守字体/行距/序号与基金规范不符,评审被打回生成 docx 后对照"关键要求"逐项核
技术报告腔把 README 语气直接搬进学术报告按 writing-guidelines.md 改写为学术表述,图表编号并被正文引用

资源文件说明

文件用途
references/report-structure.md省级基金报告详细结构说明
references/writing-guidelines.md学术写作指南与注意事项
references/format-standards.md格式规范与排版要求
references/evaluation-metrics.md气象预报评估指标说明
assets/report-template.md研究报告Markdown模板

示例：风速预报专题研究报告

输入材料：

  • wind_speed_model.py（模型代码）
  • evaluation_results.json（评估结果）
  • README.md（项目说明）
  • training_log.txt（训练日志）

输出报告结构：

风速要素单站预报方法研究报告
├── 摘要（中英文各400字）
├── 关键词（3-5个）
├── 一、研究背景与意义
├── 二、研究目标与内容
├── 三、研究方法与技术路线
├── 四、数据来源与处理
├── 五、模型构建与训练
├── 六、实验结果与分析
├── 七、结论与展望
├── 参考文献
└── 附录

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Weather Forecast Report AI skill do?

将天气要素（风速、能见度、降雨量等）单站预报系统的项目文档、模型、报告、评估结果整理成符合省级自然科学基金项目编写规范的专题研究报告。适用于：(1) 基于Claude Code + OpenSpec开发的气象预报系统结题报告编写，(2) 将技术文档转换为学术研究报告格式，(3) 生成符合省级自然科学基金规范的专题研究报告，(4) 整合项目代码、模型、评估指标等技术材料为规范化学术报告。触发关键词："专题研究报告"、"结题报告"、"天气预报系统"、"基金项目报告"、"风速预报"、"能见度预报"、"降雨量预报"、"气象预测"、"单站预报"等。

Why use Weather Forecast Report on TypingMind?

Because you install it once and use it with any model. Weather Forecast Report is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Weather Forecast Report in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/staruhub/ClaudeSkills/tree/main/lab/Geek-skills-weather-forecast-report. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Weather Forecast Report?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Weather Forecast Report?

As many as you like. As long as a model supports skills, you can use Weather Forecast Report with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Weather Forecast Report AI skill free?

Yes. It is published on GitHub by staruhub under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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