Files
claude-scientific-skills/scientific-skills/markdown-mermaid-writing/templates/research_paper.md
borealBytes e05e5373d0 fix(attribution): correct source repo URL to SuperiorByteWorks-LLC/agent-project
All 40 references to borealBytes/opencode updated to the correct source:
https://github.com/SuperiorByteWorks-LLC/agent-project

Affected files: SKILL.md, all 24 diagram guides, 9 templates, issue and PR
docs, plus assets/examples/example-research-report.md (new file).

The example report demonstrates full skill usage: flowchart, sequence,
timeline, xychart, radar diagrams — all with accTitle/accDescr and
classDef colors, no %%{init}. Covers HEK293T CRISPR editing efficiency
as a realistic scientific context.
2026-02-19 18:29:14 -05:00

9.9 KiB
Raw Blame History

Research Paper / Technical Analysis Template

Back to Markdown Style Guide — Read the style guide first for formatting, citation, and emoji rules.

Use this template for: Research papers, technical analyses, literature reviews, data-driven reports, competitive analyses, market research, or any document built around evidence and methodology. Designed for heavy citation, structured argumentation, and reproducible findings.

Key features: Abstract for quick assessment, methodology section for credibility, findings with supporting data/diagrams, rigorous footnote citations throughout, and a complete references section.

Philosophy: A great research document lets the reader evaluate your conclusions independently. Show your work. Cite your sources. Present counter-arguments. The reader should trust your findings because the evidence is right there — not because you said so.


How to Use

  1. Copy this file to your project
  2. Replace all [bracketed placeholders] with your content
  3. Adjust sections — not every paper needs every section, but the core flow (Abstract → Introduction → Methodology → Findings → Conclusion) should stay intact
  4. Cite aggressively — every claim, every statistic, every external methodology reference gets a [^N] footnote
  5. Add Mermaid diagrams for any process, architecture, data flow, or comparison

Template Structure

1. Abstract — What you did, what you found, why it matters (150-300 words)
2. 📋 Introduction — Problem statement, context, scope, research questions
3. 📚 Background — Prior work, literature review, industry context
4. 🔬 Methodology — How you did the research, data sources, approach
5. 📊 Findings — What you discovered, with evidence and diagrams
6. 💡 Analysis — What the findings mean, implications, limitations
7. 🎯 Conclusions — Summary, recommendations, future work
8. 🔗 References — All cited sources with full URLs

The Template

Everything below the line is the template. Copy from here:


[Paper Title: Descriptive and Specific]

[Author(s) or Team] · [Organization] · [Date]


Abstract

[150300 word summary structured as: Context (12 sentences on the problem space), Objective (what this paper investigates), Method (how the research was conducted), Key findings (the most important results), Significance (why this matters and who should care).]

Keywords: [keyword 1], [keyword 2], [keyword 3], [keyword 4], [keyword 5]


📋 Introduction

Problem statement

[What problem exists? Why does it matter? Who is affected? Be specific — include metrics where available.]

[The scope of the problem, with citation]1 .

Research questions

This paper investigates:

  1. [RQ1] — [Specific, answerable question]
  2. [RQ2] — [Specific, answerable question]
  3. [RQ3] — [Specific, answerable question]

Scope and boundaries

  • In scope: [What this paper covers]
  • Out of scope: [What this paper deliberately excludes and why]
  • Target audience: [Who will benefit from these findings]
💬 Context Notes
  • Why this research was initiated
  • Organizational context or business driver
  • Relationship to prior internal work
  • Known constraints that shaped the scope

📚 Background

Industry context

[Current state of the field. What's known. What the established approaches are. Cite existing work.]

[Key finding from prior research]2 . [Another relevant study found]3 .

Prior work

Study / Source Key Finding Relevance to Our Work
[Author (Year)]4 [What they found] [How it connects]
[Author (Year)]5 [What they found] [How it connects]
[Author (Year)]6 [What they found] [How it connects]

Gap in current knowledge

[What's missing from existing research? What question remains unanswered? This is the gap your paper fills.]

📋 Extended Literature Review

[Deeper discussion of related work, historical context, evolution of approaches, and detailed comparison of methodologies used by prior researchers. This depth supports the paper's credibility without cluttering the main flow.]


🔬 Methodology

Approach

[Describe your research methodology — qualitative, quantitative, mixed methods, experimental, observational, case study, etc.]

flowchart LR
    accTitle: Research Methodology Flow
    accDescr: Four-phase research process from data collection through analysis to validation and reporting

    collect[📥 Data **collection**] --> clean[⚙️ Data **cleaning**]
    clean --> analyze[🔍 **Analysis**]
    analyze --> validate[🧪 **Validation**]
    validate --> report[📤 Report **findings**]

    classDef process fill:#dbeafe,stroke:#2563eb,stroke-width:2px,color:#1e3a5f
    class collect,clean,analyze,validate,report process

Data sources

Source Type Size / Scope Collection Period
[Source 1] [Survey / API / Database / etc.] [N records / respondents] [Date range]
[Source 2] [Type] [Size] [Date range]

Tools and technologies

  • [Tool 1] — [Purpose and version]
  • [Tool 2] — [Purpose and version]
  • [Analysis framework] — [Why this was chosen]

Limitations of methodology

⚠️ Known limitations: [Be upfront about what could affect the validity of your results — sample size, selection bias, time constraints, data quality issues. This builds credibility, not weakness.]

🔧 Detailed Methodology

Data collection protocol

[Step-by-step description of how data was gathered]

Cleaning and preprocessing

[What transformations were applied, what was excluded and why]

Statistical methods

[Specific tests, confidence levels, software used]

Reproducibility

[How someone else could replicate this research — data availability, code repositories, environment setup]


📊 Findings

Finding 1: [Descriptive title]

[Present the finding clearly. Lead with the conclusion, then show the evidence.]

[Data supporting this finding]7 :

Metric Before After Change
[Metric 1] [Value] [Value] [+/- %]
[Metric 2] [Value] [Value] [+/- %]

📌 Key insight: [One-sentence takeaway from this finding]

Finding 2: [Descriptive title]

[Present the finding. Include a diagram if the finding involves relationships, processes, or comparisons.]

xychart-beta
    title "[Chart title]"
    x-axis ["Category A", "Category B", "Category C", "Category D"]
    y-axis "Measurement" 0 --> 100
    bar [45, 72, 63, 89]

[Explanation of what the data shows and why it matters.]

Finding 3: [Descriptive title]

[Present the finding with supporting evidence.]

📊 Supporting Data Tables

[Detailed data tables, raw numbers, statistical breakdowns that support the findings but would interrupt the reading flow if placed inline. Readers who want to verify can expand.]


💡 Analysis

Interpretation

[What do the findings mean? Connect back to your research questions. Explain the "so what?"]

  • RQ1: [How Finding 1 answers Research Question 1]
  • RQ2: [How Finding 2 answers Research Question 2]
  • RQ3: [How Finding 3 answers Research Question 3]

Implications

For [audience 1]:

  • [What this means for them and what action they should consider]

For [audience 2]:

  • [What this means for them and what action they should consider]

Comparison with prior work

[How do your findings compare with the studies referenced in the Background section? Do they confirm, contradict, or extend prior work?]

Limitations

[What caveats should the reader keep in mind? What factors might affect generalizability? Be honest — this is where credibility is built.]

💬 Discussion Notes
  • Alternative interpretations of the data
  • Edge cases or outliers observed
  • Areas where more data would strengthen conclusions
  • Potential confounding variables

🎯 Conclusions

Summary

[35 sentences. Restate the problem, summarize the key findings, and state the primary recommendation. A reader who skips to this section should understand the entire paper's value.]

Recommendations

  1. [Recommendation 1] — [Specific, actionable. What to do, who should do it, expected impact]
  2. [Recommendation 2] — [Specific, actionable]
  3. [Recommendation 3] — [Specific, actionable]

Future work

  • [Research direction 1] — [What it would investigate and why it matters]
  • [Research direction 2] — [What it would investigate and why it matters]

🔗 References

All sources cited in this paper:


Last updated: [Date]


  1. [Author/Org]. ([Year]). "[Title]." [Publication]. https://example.com ↩︎

  2. [Author/Org]. ([Year]). "[Title]." [Publication]. https://example.com ↩︎

  3. [Author/Org]. ([Year]). "[Title]." [Publication]. https://example.com ↩︎

  4. [Author/Org]. ([Year]). "[Title]." [Publication]. https://example.com ↩︎

  5. [Author/Org]. ([Year]). "[Title]." [Publication]. https://example.com ↩︎

  6. [Author/Org]. ([Year]). "[Title]." [Publication]. https://example.com ↩︎

  7. [Author/Org]. ([Year]). "[Title]." [Publication]. https://example.com ↩︎