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    Academic PaperDesign

    1

    Automated IEEE research paper architect generating bilingual (English/Arabic) manuscripts and AI academic diagrams.

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    Academic PaperDesign

    Academic PaperDesign

    Example session with this skill installed

    I have a Python project implementing a novel attention mechanism called "SparseFlow Attention" that reduces transformer memory usage by 40% while maintaining 98% of BERT-base accuracy on GLUE benchmarks. The repo contains:

    • model/attention.py (core implementation, 340 lines)
    • train.py (training loop with custom scheduler)
    • results/glue_scores.json (evaluation metrics)
    • README.md (project overview and ablations)

    Generate a medium-length IEEE research paper (15–30 pages) with:

    • Full architecture diagrams (flat pastel style)
    • Data flow for the training pipeline
    • Comparison table vs. baseline attention
    • An Arabic version for a local journal submission

    Author: DeepSeek41-flash (deepseek.flash@gmail.com). Bilingual output, please.

    • Read your context and instructions
    • Compiled the academic paperdesign
    • Generated the app files

    ✅ Research Paper Package Generated Successfully

    I analyzed your repository, extracted the core methodology from attention.py, benchmarked results from glue_scores.json, and produced a complete bilingual IEEE package.

    📊 Deliverables Summary

    ComponentStatusDetails
    English Paper (PDF)✅ Generated24 pages, IEEE Two-Column Format
    Arabic Paper (PDF)✅ Generated22 pages, RTL via XeLaTeX + Amiri
    LaTeX Source✅ Includedpaper_en.tex, paper_ar.tex
    BibTeX References✅ Included47 citations (arXiv, IEEE, ACM)
    AI Diagrams✅ 5 GeneratedSee breakdown below
    Audio Script (SRT)✅ Generated6-minute narration
    Package✅ ZippedSparseFlow_Attention_IEEE.zip

    🎨 AI-Generated Academic Diagrams

    All diagrams rendered in flat pastel style matching IEEE publication standards:

    FigureFileDescription
    Fig. 1fig1_architecture.pngEnd-to-end SparseFlow transformer architecture
    Fig. 2fig2_attention_block.pngInternal structure of the sparse attention head
    Fig. 3fig3_dataflow.pngTraining pipeline with custom scheduler
    Fig. 4fig4_memory_comparison.pngMemory usage: SparseFlow vs. baseline
    Fig. 5fig5_ablation.pngAblation study on sparsity ratio (k=4,8,16,32)

    📄 Paper Structure (English Version)

    Title: SparseFlow Attention: A Memory-Efficient Sparse Attention Mechanism for Transformer Architectures

    Authors: DeepSeek-flash¹
    ¹Independent Researcher — Deepseek.flash@gmail.com

    Abstract (excerpt)

    We introduce

    SparseFlow Attention, a novel sparse attention mechanism that reduces transformer memory consumption by

    40% on long-sequence tasks while preserving

    98.2% of BERT-base accuracy across GLUE benchmarks. Unlike prior sparse methods that require architectural retraining, SparseFlow operates as a

    drop-in replacement for standard multi-head attention…

    Section Breakdown

    SectionPagesContent
    I. Introduction2.5Motivation, prior work gaps, contributions
    II. Related Work3Sparse attention, efficient transformers, GLUE baselines
    III. Methodology5SparseFlow formulation, complexity analysis (Eq. 1–7)
    IV. Experimental Setup2Datasets, hyperparameters, hardware
    V. Results4.5GLUE scores, memory benchmarks, ablations (Tables I–IV)
    VI. Discussion2Limitations, failure cases, future directions
    VII. Conclusion1Summary of findings
    References247 IEEE-formatted citations
    Appendix A2Full hyperparameter grid

    Key Equation (rendered in LaTeX)

    \text{Attention}(Q,K,V) = \text{softmax}\left(\frac{QK^\top}{\sqrt{d_k}} \odot M_k\right)V

    academic-paperdesign-app.zip

    ZIP · project files

    Generated

    Example file from a real run - the skill writes it into your workspace.

    Connects securely to your tools. The creator never sees your data.

    About this skill

    Writing IEEE-compliant research papers from project code or rough notes is a time-consuming process prone to formatting errors. This skill automates the entire academic writing pipeline, transforming technical source material into publication-ready manuscripts in both English and Arabic.

    What it does

    • IEEE Formatting enforces strict adherence to two-column layouts, Roman-numeral headings, and specific typography standards.
    • Bilingual Generation produces synchronized academic versions in English and Arabic with proper RTL (Right-to-Left) adaptation.
    • AI Diagram Generation creates professional, flat-design academic figures like system architectures and neural network diagrams without using code-based plotting libraries.
    • Source Packaging bundles LaTeX source files, .bib bibliographies, PDF/DOCX exports, and AI-generated assets into a structured ZIP file.
    • Multimedia Support extracts key findings to generate SRT subtitle scripts for technical presentations or audio summaries.

    How it works

    1. Context Intake reads your uploaded code, PDFs, or project notes to extract methodology and results.
    2. Metadata Setup prompts you for specific titles, author affiliations, and target length (ranging from 7 to 500 pages).
    3. Visual Drafting generates AI-rendered academic illustrations for your system architecture and data flows.
    4. Manuscript Assembly writes the full paper following IEEEtran class rules and translates it into academic Arabic.

    Frameworks & tools

    The skill utilizes LaTeX (pdflatex/XeLaTeX), IEEEtran templates, BibTeX for citation management, and specialized scripts for SRT generation and asset packaging.

    Why this beats prompting it yourself

    Standard prompts often fail to maintain consistent IEEE formatting across long documents or produce low-quality ASCII diagrams. This skill uses a multi-phase workflow to ensure every heading, citation, and figure caption meets conference standards while maintaining technical accuracy between English and Arabic versions.

    Use cases

    • Paper Drafting converting a GitHub repository into a formal conference submission.
    • Thesis Expansion scaling technical documentation into a comprehensive 100+ page research document.
    • Bilingual Publication preparing research for international journals requiring both English and Arabic abstracts or full texts.
    • Visual Scaffolding generating professional architecture diagrams for academic posters.

    Known limitations

    Does not support code-drawn charts (Matplotlib/Mermaid). Requires specific JSON reference files to be present in the environment for style enforcement. Limited to English and Arabic languages.

    How to install

    Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.

    ~30 seconds
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