firecrawl-research by glebis/claude-skills
npx skills add https://github.com/glebis/claude-skills --skill firecrawl-research通过使用 FireCrawl API 自动搜索和抓取网络资源来丰富研究文档。从 Markdown 文件中提取研究主题,并生成包含原始材料的全面研究文档。
当用户出现以下情况时使用此技能:
脚本使用两种方法自动从 Markdown 文件中提取研究主题:
方法 1:标题
## Spatial Reasoning in AI
### Computer Vision Applications
Spatial Reasoning in AI 和 Computer Vision Applications 都将成为研究主题。
方法 2:研究标签
- [research] Large Language Models for robotics
- [search] Theory of Mind in autonomous driving
两个带标签的条目都将成为研究主题。
对于每个主题:
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在指定的输出目录中创建新的 Markdown 文件:
{topic}_{timestamp}.mdpython scripts/firecrawl_research.py research.md
输出到当前目录。
python scripts/firecrawl_research.py research.md ./output
在 ./output/ 文件夹中创建文件。
python scripts/firecrawl_research.py research.md ./output 3
每个主题最多检索 3 个结果。
将 .env.example 复制为 .env:
cp .env.example .env
添加 FireCrawl API 密钥:
FIRECRAWL_API_KEY=fc-your-actual-api-key
脚本会自动从技能的 .env 文件中加载 API 密钥。
脚本包含针对 FireCrawl 免费层的自动速率限制:
处理多个主题时,预计:
用户请求: “使用 FireCrawl 研究这些 AI 主题”
输入文件 (ai-research.md):
# AI Research Topics
## Spatial Reasoning in Vision-Language Models
- [research] Embodied AI for robotics
- [research] Computer Use Agents
命令:
python scripts/firecrawl_research.py ai-research.md ./research_output 5
输出:
research_output/
├── Spatial_Reasoning_in_Vision-Language_Models_20251122_140530.md
├── Embodied_AI_for_robotics_20251122_140542.md
└── Computer_Use_Agents_20251122_140554.md
每个文件包含:
从现有笔记中提取主题,进行研究,保存到当前文件夹:
python scripts/firecrawl_research.py my-notes.md
为研究结果创建专用的输出文件夹:
python scripts/firecrawl_research.py topics.md ./research_results
增加每个主题的结果数量以获得全面覆盖:
python scripts/firecrawl_research.py topics.md ./deep_research 10
直接输出到知识库的研究文件夹:
python scripts/firecrawl_research.py topics.md ~/Brains/brain/Research
在技能文件夹中创建包含 FIRECRAWL_API_KEY=... 的 .env 文件。
使用以下格式向 Markdown 添加主题:
## Header format- [research] Topic format- [search] Topic format位置: scripts/firecrawl_research.py
依赖项:
python-dotenv - 环境变量管理requests - 向 FireCrawl API 发送 HTTP 请求安装依赖项:
pip install python-dotenv requests
使用的 FireCrawl 功能:
/v1/search 端点 - 搜索并自动抓取scrapeOptions.formats: ['markdown'] - Markdown 输出scrapeOptions.onlyMainContent: true - 过滤噪音此技能包含用于以 Markdown 格式撰写科学论文的模板。
1. Pandoc 学术论文 (assets/templates/pandoc-scholarly-paper.md)
2. MyST 科学论文 (assets/templates/myst-scientific-paper.md)
将模板复制到您的项目:
cp assets/templates/pandoc-scholarly-paper.md my-paper.md
# 或
cp assets/templates/myst-scientific-paper.md my-paper.md
编辑内容:
[@AuthorYear](Pandoc)或 {cite}\AuthorYear``(MyST)添加引用转换为 PDF/DOCX:
python scripts/convert_academic.py my-paper.md pdf
python scripts/convert_academic.py my-paper.md docx
python scripts/convert_academic.py my-paper.md pdf --myst # 用于 MyST
将 FireCrawl 研究结果转换为 BibTeX 参考文献条目:
python scripts/generate_bibliography.py research_output/*.md -o references.bib
功能:
@misc 条目示例工作流程:
# 1. 研究主题
python scripts/firecrawl_research.py topics.md ./research
# 2. 生成参考文献
python scripts/generate_bibliography.py research/*.md -o refs.bib
# 3. 复制模板
cp assets/templates/pandoc-scholarly-paper.md paper.md
# 4. 编辑 paper.md(添加内容,引用来源)
# 5. 转换为 PDF
python scripts/convert_academic.py paper.md pdf
Pandoc 语法:
Recent research [@Smith2024] shows...
Multiple studies [@Jones2023; @Brown2024] indicate...
MyST 语法:
Recent research {cite}`Smith2024` shows...
Multiple studies {cite}`Jones2023,Brown2024` indicate...
assets/references.bib 中提供了一个包含常见条目类型的示例参考文献:
@article)@inproceedings)@book)@phdthesis)@misc)@article 带 arXiv)## 表示主要主题,### 表示子主题每周安装次数
124
代码仓库
GitHub 星标数
66
首次出现
2026年1月24日
安全审计
安装于
cursor115
opencode114
gemini-cli113
codex112
github-copilot109
claude-code107
Enrich research documents by automatically searching and scraping web sources using the FireCrawl API. Extract research topics from markdown files and generate comprehensive research documents with source material.
Use this skill when the user:
The script automatically extracts research topics from markdown files using two methods:
Method 1: Headers
## Spatial Reasoning in AI
### Computer Vision Applications
Both Spatial Reasoning in AI and Computer Vision Applications become research topics.
Method 2: Research Tags
- [research] Large Language Models for robotics
- [search] Theory of Mind in autonomous driving
Both tagged items become research topics.
For each topic:
Creates new markdown files in the specified output directory:
{topic}_{timestamp}.mdpython scripts/firecrawl_research.py research.md
Outputs to current directory.
python scripts/firecrawl_research.py research.md ./output
Creates files in ./output/ folder.
python scripts/firecrawl_research.py research.md ./output 3
Retrieves maximum 3 results per topic.
Copy .env.example to .env:
cp .env.example .env
Add FireCrawl API key:
FIRECRAWL_API_KEY=fc-your-actual-api-key
The script automatically loads the API key from the skill's .env file.
The script includes automatic rate limiting for FireCrawl's free tier:
When processing multiple topics, expect:
User request: "Research these AI topics using FireCrawl"
Input file (ai-research.md):
# AI Research Topics
## Spatial Reasoning in Vision-Language Models
- [research] Embodied AI for robotics
- [research] Computer Use Agents
Command:
python scripts/firecrawl_research.py ai-research.md ./research_output 5
Output:
research_output/
├── Spatial_Reasoning_in_Vision-Language_Models_20251122_140530.md
├── Embodied_AI_for_robotics_20251122_140542.md
└── Computer_Use_Agents_20251122_140554.md
Each file contains:
Extract topics from existing notes, research them, save to current folder:
python scripts/firecrawl_research.py my-notes.md
Create dedicated output folder for research results:
python scripts/firecrawl_research.py topics.md ./research_results
Increase results per topic for comprehensive coverage:
python scripts/firecrawl_research.py topics.md ./deep_research 10
Direct output to vault's research folder:
python scripts/firecrawl_research.py topics.md ~/Brains/brain/Research
Create .env file in skill folder with FIRECRAWL_API_KEY=...
Add topics to markdown using:
## Header format- [research] Topic format- [search] Topic formatLocation: scripts/firecrawl_research.py
Dependencies:
python-dotenv - Environment variable managementrequests - HTTP requests to FireCrawl APIInstall dependencies:
pip install python-dotenv requests
FireCrawl Features Used:
/v1/search endpoint - Search with automatic scrapingscrapeOptions.formats: ['markdown'] - Markdown outputscrapeOptions.onlyMainContent: true - Filter noiseThis skill includes templates for writing scientific papers in markdown format.
1. Pandoc Scholarly Paper (assets/templates/pandoc-scholarly-paper.md)
2. MyST Scientific Paper (assets/templates/myst-scientific-paper.md)
Copy template to your project:
cp assets/templates/pandoc-scholarly-paper.md my-paper.md
# or
cp assets/templates/myst-scientific-paper.md my-paper.md
Edit content:
[@AuthorYear] (Pandoc) or {cite}\AuthorYear`` (MyST)Convert to PDF/DOCX:
python scripts/convert_academic.py my-paper.md pdf
python scripts/convert_academic.py my-paper.md docx
python scripts/convert_academic.py my-paper.md pdf --myst # For MyST
Convert FireCrawl research results into BibTeX bibliography entries:
python scripts/generate_bibliography.py research_output/*.md -o references.bib
What it does:
@misc entriesExample workflow:
# 1. Research topics
python scripts/firecrawl_research.py topics.md ./research
# 2. Generate bibliography
python scripts/generate_bibliography.py research/*.md -o refs.bib
# 3. Copy template
cp assets/templates/pandoc-scholarly-paper.md paper.md
# 4. Edit paper.md (add content, cite sources)
# 5. Convert to PDF
python scripts/convert_academic.py paper.md pdf
Pandoc syntax:
Recent research [@Smith2024] shows...
Multiple studies [@Jones2023; @Brown2024] indicate...
MyST syntax:
Recent research {cite}`Smith2024` shows...
Multiple studies {cite}`Jones2023,Brown2024` indicate...
An example bibliography is provided in assets/references.bib with common entry types:
@article)@inproceedings)@book)@phdthesis)@misc)@article with arXiv)## for main topics, ### for subtopicsWeekly Installs
124
Repository
GitHub Stars
66
First Seen
Jan 24, 2026
Security Audits
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Installed on
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github-copilot109
claude-code107
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