MR
Mayur Rathi
@github
⭐ 34.1k GitHub stars

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.

Last verified on: 2026-05-30
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. 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. 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. 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. 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.

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