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.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionfirecrawl-researchExecute the skills CLI command in your project's root directory to begin installation:
Fetches firecrawl-research from glebis/claude-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate firecrawl-research. Access via /firecrawl-research in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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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 subtopicsMake data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
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ailabs-393/ai-labs-claude-skills
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
Useful defaults in firecrawl-research — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Registry listing for firecrawl-research matched our evaluation — installs cleanly and behaves as described in the markdown.
I recommend firecrawl-research for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
firecrawl-research reduced setup friction for our internal harness; good balance of opinion and flexibility.
Keeps context tight: firecrawl-research is the kind of skill you can hand to a new teammate without a long onboarding doc.
firecrawl-research is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Registry listing for firecrawl-research matched our evaluation — installs cleanly and behaves as described in the markdown.
firecrawl-research is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Useful defaults in firecrawl-research — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
firecrawl-research reduced setup friction for our internal harness; good balance of opinion and flexibility.
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