Our review
This skill helps draft research papers and meeting notes from raw experimental data and informal notes, enforcing academic rigor and formatting.
Strengths
- Clear structure with predefined sections (background, analysis, findings, conclusion)
- Prioritizes quantitative data and comparison tables
- Native support for LaTeX and academic citations
- Suggests placeholder figures for dense data
Limitations
- Requires precise numerical data to be effective
- May produce overly formal text for non-academic contexts
- Does not replace human review for overall coherence
Use this skill to quickly turn lab notes or experimental results into structured academic drafts.
Avoid using it for non-academic writing or when data is insufficient to support claims.
Security analysis
SafeThis skill only assists with text drafting and formatting; it does not execute any commands, access the network, or manipulate files. There is no risk of destructive or exfiltrating behavior.
No concerns found
Examples
Write an abstract for my paper on linear mode connectivity in neural networks. Key results: we found that 85% of random pairs are connected, training budget is 50 epochs, accuracy improves by 3%.Draft the methodology section for my experiment comparing ResNet and ViT. We used ImageNet-1k, batch size 256, learning rate 0.01. Include a table of hyperparameters.Prepare a summary for my Friday advisor meeting based on these results: validation loss decreased by 0.15, but training time increased by 20%. Suggest next steps.name: paper-writing-assistant description: Assist in drafting research papers and meeting notes, enforcing academic rigor and formatting.
Paper Writing Assistant Skill
Value Proposition
Transforms raw experimental data and informal notes into high-quality academic text suitable for paper submissions or professor meetings.
When to Use
- Drafting Sections: When the user asks to write "Abstract", "Methodology", or "Results".
- Meeting Prep: When creating summaries for advisor meetings (e.g., "Prepare for the Friday meeting").
- Reformatting: When converting code comments or rough notes into LaTeX or polished Markdown.
Instructions
- Structure: Adhere to the user's preferred structure:
- Background: Context of the problem.
- Analysis: Method of investigation.
- Findings: Data-backed results.
- Conclusion: Summary and next steps.
- Data First: Always prioritize quantitative data (metrics, tables) over qualitative descriptions.
- Comparison: When comparing models, use tables.
- Compare parameters, training data size, inference speed, and accuracy.
- Citations: Use standard citation markers (e.g.,
[Author, Year]) or BibTeX keys if provided.
Best Practices
- Visuals: Suggest placeholder figures where data is dense (e.g.,
[Figure 1: Success Rate vs Training Steps]). - Tone: Maintain a formal, academic tone. Avoid slang or overly casual language.
- LaTeX Support: Be ready to generate LaTeX snippets for tables and equations.
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