inno-paper-writing

Files (6)

• SKILL.md
• index.js
• package.json
• acm_formatting_specs.md · references/
• ieee_formatting_specs.md · references/
• writing_style_guide.md · references/

SKILL.md (first lines)

# inno-paper-writing

## Canonical Summary

Creates formal academic research papers following IEEE/ACM formatting standards with proper structure, citations, and scholarly writing style. Use when the user asks to write a research paper, academic paper, or conference paper on any top...

## Trigger Rules

Use this skill when the user request matches its research workflow scope. Prefer the bundled resources instead of recreating templates or reference material. Keep outputs traceable to project files, citations, scripts, or upstream evidence.

## Resource Use Rules

- Read from `references/` only when the current task needs the extra detail.

## Execution Contract

- Resolve every relative path from this skill directory first.
- Prefer inspection before mutation when invoking bundled scripts.
- If a required runtime, CLI, credential, or API is unavailable, explain the blocker and continue with the best manual fallback instead of silently skipping the step.
- Do not write generated artifacts back into the skill directory; save them inside the active project workspace.

## Upstream Instructions

# Research Paper Writer

## Overview

This skill guides the creation of formal academic research papers that meet publication standards for IEEE and ACM conferences/journals. It ensures proper structure, formatting, academic writing style, and comprehensive coverage of research topics.

## Workflow

### 1. Understanding the Research Topic

When asked to write a research paper:

1. **Clarify the topic and scope** with the user:
   - What is the main research question or contribution?
   - What is the target audience (conference, journal, general academic)?
   - What is the desired length (page count or word count)?
   - Are there specific sections required?
   - What formatting standard to use (IEEE or ACM)?

2. **Gather context** if needed:
   - Review any provided research materials, data, or references
   - Understand the domain and technical background
   - Identify key related work or existing research to reference

### 2. Paper Structure

Follow this standard academic paper structure:

```
1. Title and Abstract
   - Concise title reflecting the main contribution
   - Abstract: 150-250 words summarizing purpose, methods, results, conclusions

2. Introduction
   - Motivation and problem statement
   - Research gap and significance
   - Main contributions (typically 3-5 bullet points)
   - Paper organization paragraph

3. Related Work / Background
   - Literature review of relevant research
   - Comparison with existing approaches
   - Positioning of current work

4. Methodology / Approach / System Design
   - Detailed description of proposed method/system
   - Architecture diagrams if applicable
   - Algorithms or procedures
   - Design decisions and rationale

5. Implementation (if applicable)
   - Technical details
   - Tools and technologies used
   - Challenges and solutions

6. Evaluation / Experiments / Results
   - Experimental setup
   - Datasets or test scenarios
   - Performance metrics
   - Results presentation (tables, graphs)
   - Analysis and interpretation

7. Discussion
   - Implications of results
   - Limitations and threats to validity
   - Lessons learned

8. Conclusion and Future Work
   - Summary of contributions
   - Impact and significance
   - Future research directions

9. References
   - Comprehensive bibliography in proper citation format
```

### 3. Academic Writing Style

Apply these writing conventions from scholarly research:

**Tone and Voice:**
- Formal, objective, and precise language
- Third-person perspective (avoid "I" or "we" unless describing specific contributions)
- Present tense for established facts, past tense for specific studies
- Clear, direct statements without unnecessary complexity

**Technical Precision:**
- Define all acronyms on first use: "Context-Aware Systems (C-AS)"
- Use domain-specific terminology correctly and consistently
- Quantify claims with specific metrics or evidence
- Avoid vague terms like "very", "many", "significant" without data

**Argumentation:**
- State claims clearly, then support with evidence
- Use logical progression: motivation → problem → solution → validation
- Compare and contrast with related work explicitly

From LigphiDonk/Oh-my--paper, MIT.

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