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zsyggg paper-craft-skills
Claude Code skills for academic papers: deep analysis, comics, summaries | 论文工艺:深度解读、漫画生成、速览总结
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Score breakdown
- popularity 53
- growth 0
- activity 35
- freshness 100
- community 8
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Turn academic papers into polished method figures, visual slide decks, and in-depth articles — zero config, one command.
From arxiv link to publication-ready visuals, AIGC slide decks, and deep-dive articles.
Drop a paper, pick a style, get output that looks like a human expert made it.
Copy this into Codex or Claude Code:
Please install zsyggg/paper-craft-skills for me.
GitHub: https://github.com/zsyggg/paper-craft-skills
That's it. The agent handles clone, symlink, and registration. No API keys. No accounts. If you prefer a terminal:
npx skills add zsyggg/paper-craft-skillsWorks with: Codex · Claude Code · Cursor · Windsurf
paper-deck turns a paper, article, or technical note into a designed slide deck. It first builds a deck brief and slide-by-slide outline, then writes reproducible visual prompts, generates polished 16:9 slide images, and merges them into .pptx and .pdf.
It is built for iteration: every page has its own prompt, so you can ask for precise changes like “make slide 5 more journal-like”, “replace slide 8 with a real benchmark chart”, or “keep the layout but switch the cover to liquid glass”.
Four compact style presets for the same paper topic
| Style | Best for |
|---|---|
| journal-minimal | Nature/IEEE-inspired academic decks and thesis defenses |
| business-research | Strategy memos, industry research, investor/client briefings |
| warm-notes | Study-note explanations, teaching, approachable paper walkthroughs |
| liquid-glass | Apple-inspired visual chapters, covers, and high-impact section pages |
It also supports real source visuals. When a PDF contains strong figures, tables, plots, or screenshots, the skill plans which slides should use them, where they should be cropped, and how they should be framed. Real visuals can be mounted into clean academic panels, evidence blocks, or glass-style layouts instead of being hallucinated from scratch.
/paper-deck https://arxiv.org/abs/1706.03762
/paper-deck /path/to/paper.pdf --style journal-minimal --slides 12
/paper-deck notes.md --style liquid-glass/paper-comic https://arxiv.org/abs/1706.03762
Reads the paper, then recommends:
I suggest 6 figures:
1. Cover: one-line contribution + visual anchor
2. Transformer architecture overview
3. Self-attention mechanism
4. Multi-head attention detail
5. Encoder / Decoder Block
6. Key results
Or generate only 1 overview figure, or expand to 8 detailed mechanism figures.
Language? [Chinese / English] Style? [sketchnote / paper-figure] Scope and count?
paper-figure — clean, publication-grade
sketchnote — bright, warm, approachable
Full walkthrough: examples/paper-illustrated/attention-is-all-you-need
Not a paper translator — a re-interpreter. It reads the full paper, searches GitHub for open-source implementations, cross-references code with the paper, and writes in your chosen style.
| Style | Reads like | Use it for |
|---|---|---|
| storytelling | A viral blog post — hooks, analogies, golden takeaway | WeChat, Twitter, blogs |
| academic | A peer-reviewed deep dive — KaTeX formulas, comparison tables | Lab meetings, lit reviews |
| concise | A cheat sheet — Mermaid diagram + key data table | Quick understanding |
![]() Formula Explanation Extracted paper formulas with symbol-by-symbol breakdown |
![]() Code Analysis Aligns paper concepts with the GitHub source code |
/paper-analyzer https://arxiv.org/abs/1706.03762 # arxiv link
/paper-analyzer /path/to/paper.pdf # local PDF
/paper-analyzer # then paste textMIT
Languages
Share of the codebase by language, based on repository metadata from the host.
- Python 100.0%
Repository Radar analysis
Deterministic insights derived from public metadata and our observations — not personal testing or reviews.
Why this repository is interesting
- Listed in our discovery index with public GitHub metadata for analysis.
Who should use it
- Developers working primarily with Python
- Teams exploring AI tooling, agents, or ML infrastructure
Potential use cases
- Reference or evaluate Python open-source approaches in this domain
- Prototype AI/agent workflows or study reference architectures
Strengths
- README present in our index
Limitations / considerations
- License not recorded in our index — confirm on GitHub before production use
What to watch
- Radar Score is modest — dig into activity and docs before committing
Source: GitHub (public metadata) + Repository Radar analysis. We do not claim ownership of third-party repositories.
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