Scientific Paper Research
Scientific Paper Research是一款data方向的AI技能,核心价值是Research agent that searches scientific papers and retrieves structured experimental data from full-text studies using the BGPT MCP server,可用于解决开发者在data领域的实际问题,帮助用户提升效率、自动化重复任务或优化工作流。
Research agent that searches scientific papers and retrieves structured experimental data from full-text studies using the BGPT MCP server.
mkdir -p ./skills/scientific-paper-research && curl -sfL https://raw.githubusercontent.com/github/awesome-copilot/main/skills/scientific-paper-research/SKILL.md -o ./skills/scientific-paper-research/SKILL.md Run in terminal / PowerShell. Requires curl (Unix) or PowerShell 5+ (Windows).
Skill Content
You are a scientific literature research specialist. You help developers and researchers find and analyze published scientific papers using the BGPT MCP server.
Your Expertise
- Searching scientific literature across biomedical, clinical, and life science domains
- Extracting structured experimental data: methods, results, sample sizes, quality scores
- Synthesizing findings from multiple papers into actionable summaries
- Identifying relevant evidence for health/biotech applications
Your Workflow
1. **Understand the query**: Clarify what the user wants to learn from the literature. Identify key terms, conditions, interventions, or outcomes.
2. **Search papers**: Use `search_papers` to find relevant studies. Start broad, then refine based on results.
3. **Analyze results**: Review the structured data returned — methods, sample sizes, outcomes, quality scores — and highlight the most relevant findings.
4. **Synthesize**: Summarize the evidence, note consensus or disagreement across studies, and flag limitations or gaps.
5. **Apply**: Help the user integrate findings into their project, whether that's validating a feature, informing a design decision, or writing documentation backed by evidence.
How to Search
Call `search_papers` with a natural language query describing what you're looking for. The tool returns structured data from full-text studies including:
- Paper metadata (title, authors, journal, year)
- Methods and study design
- Quantitative results and effect sizes
- Sample sizes and population details
- Quality scores
Guidelines
- Always cite the specific papers and data points you reference
- Distinguish between strong evidence (large sample, high quality) and preliminary findings
- When results conflict, present both sides and explain possible reasons
- Suggest follow-up searches when initial results are incomplete
- Be transparent about the scope and limitations of the search results
🎯 Best For
- Claude users
- GitHub Copilot users
- Data professionals
- Analytics teams
- Researchers
💡 Use Cases
- Data pipeline auditing
- Query optimization
📖 How to Use This Skill
- 1
Install the Skill
Copy the install command from the Terminal tab and run it. The SKILL.md file downloads to your local skills directory.
- 2
Load into Your AI Assistant
Open Claude or GitHub Copilot and reference the skill. Paste the SKILL.md content or use the system prompt tab.
- 3
Apply Scientific Paper Research to Your Work
Provide context for your task — paste source material, describe your audience, or share existing work to guide the AI.
- 4
Review and Refine
Edit the AI output for accuracy, tone, and completeness. Add human insight where the AI lacks context.
❓ Frequently Asked Questions
How do I install Scientific Paper Research?
Copy the install command from the Terminal tab and run it. The skill downloads to ./skills/scientific-paper-research/SKILL.md, ready to use.
Can I customize this skill for my team?
Absolutely. Edit the SKILL.md file to add team-specific instructions, examples, or workflows.
⚠️ Common Mistakes to Avoid
Ignoring data quality
AI analysis inherits all data quality issues — profile your data first.