Generate conference presentation slides (beamer LaTeX → PDF + editable PPTX) from a compiled paper, with speaker notes and full talk script. Use when user says \"做PPT\", \"做幻灯片\", \"make slides\", \"conference talk\", \"presentation slides\", \"生成slides\", \"写演讲稿\", or wants beamer slides for a conference talk.
npx skills add https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep --skill paper-slides
Generate conference presentation slides from: $ARGUMENTS
This skill runs after Workflow 3 (/paper-writing). It takes a compiled paper and generates a presentation slide deck for conference oral talks, spotlight presentations, or poster lightning talks.
Unlike posters (single page, visual-first), slides tell a temporal story: each slide builds on the previous one, with progressive revelation of the research narrative. A good talk makes the audience understand *why this matters* before showing *what was done*.
NeurIPS — Target venue, determines color scheme. Supported: NeurIPS, ICML, ICLR, AAAI, ACL, EMNLP, CVPR, ECCV, GENERIC. Override via argument.spotlight — Talk format. Options: oral (15-20 min), spotlight (5-8 min), poster-talk (3-5 min), invited (30-45 min). Determines slide count and content depth.16:9 — Slide aspect ratio. Options: 16:9 (default, modern projectors), 4:3 (legacy).\note{} blocks in beamer and corresponding PPTX notes. Set false for clean slides without notes.paper/ — Directory containing the compiled paper.slides/ — Output directory for all slide files.gpt-5.6-sol — Model used via Codex MCP for slide review.latexmk — LaTeX build tool.pdflatex — LaTeX engine. Use xelatex for CJK text.> 💡 Override: /paper-slides "paper/" — talk_type: oral, venue: ICML, minutes: 20, aspect: 4:3
| Talk Type | Duration | Slides | Content Depth |
|-----------|----------|:------:|---------------|
| poster-talk | 3-5 min | 5-8 | Problem + 1 method slide + 1 result + conclusion |
| spotlight | 5-8 min | 8-12 | Problem + 2 method + 2 results + conclusion |
| oral | 15-20 min | 15-22 | Full story with motivation, method detail, experiments, analysis |
| invited | 30-45 min | 25-40 | Comprehensive: background, related work, deep method, extensive results, discussion |
Same as /paper-poster-html:
| Venue | Primary | Accent | Background | Text |
|-------|---------|--------|------------|------|
| NeurIPS | #8B5CF6 | #2563EB | #FFFFFF | #1E1E1E |
| ICML | #DC2626 | #1D4ED8 | #FFFFFF | #1E1E1E |
| ICLR | #059669 | #0284C7 | #FFFFFF | #1E1E1E |
| CVPR | #2563EB | #7C3AED | #FFFFFF | #1E1E1E |
| GENERIC | #334155 | #2563EB | #FFFFFF | #1E1E1E |
Persist state to slides/SLIDES_STATE.json after each phase:
{
"phase": 3,
"venue": "NeurIPS",
"talk_type": "spotlight",
"slide_count": 10,
"codex_thread_id": "019cfcf4-...",
"status": "in_progress",
"timestamp": "2026-03-18T15:00:00"
}
On startup: if SLIDES_STATE.json exists with "status": "in_progress" and within 24h → resume. Otherwise → fresh start.
which pdflatex && which latexmk
ls $PAPER_DIR/main.tex || ls $PAPER_DIR/main.pdf
ls $PAPER_DIR/sections/*.tex
ls $PAPER_DIR/figures/
slides/ exists, copy to slides-backup-{timestamp}/mkdir -p slides/figuresxelatexslides/SLIDES_STATE.json if it existsState: Write SLIDES_STATE.json with phase: 0.
Read paper/sections/*.tex and build a slide-by-slide outline.
Slide template by talk type:
| Slide | Purpose | Content Source | Figure? |
|:-----:|---------|----------------|:-------:|
| 1 | Title | Paper metadata | No |
| 2 | Outline | Section headers | No |
| 3-4 | Motivation & Problem | Introduction | Optional |
| 5 | Key Insight | Introduction (contribution) | No |
| 6-9 | Method | Method section | Yes (hero figure) |
| 10-14 | Results | Experiments | Yes (per slide) |
| 15-16 | Analysis / Ablations | Experiments | Yes |
| 17 | Limitations | Conclusion | No |
| 18 | Conclusion / Takeaway | Conclusion | No |
| 19 | Thank You + QR | — | QR code |
| Slide | Purpose | Content Source | Figure? |
|:-----:|---------|----------------|:-------:|
| 1 | Title | Paper metadata | No |
| 2-3 | Problem + Why It Matters | Introduction | Optional |
| 4 | Key Insight | Contribution | No |
| 5-6 | Method | Method (condensed) | Yes (hero) |
| 7-9 | Results | Key results only | Yes |
| 10 | Takeaway | Conclusion | No |
| 11 | Thank You + QR | — | QR code |
| Slide | Purpose | Content Source | Figure? |
|:-----:|---------|----------------|:-------:|
| 1 | Title | Paper metadata | No |
| 2 | Problem | Introduction (1 slide) | No |
| 3 | Method | Method (1 slide) | Yes |
| 4-5 | Results | Key result only | Yes |
| 6 | Takeaway + QR | Conclusion | QR |
For each slide, specify:
Output: slides/SLIDE_OUTLINE.md
🚦 Checkpoint:
📊 Slide outline ready:
- Talk type: [TALK_TYPE] ([TALK_MINUTES] min)
- Slide count: [N] slides
- Figures used: [N] from paper/figures/
- Time budget: [breakdown]
Slide-by-slide outline:
1. [Title slide]
2. [Motivation — 1.5 min]
3. [Problem statement — 1 min]
...
Proceed to drafting? Or adjust the outline?
⛔ STOP HERE and wait for user response. This is the most critical checkpoint — the outline determines the entire talk flow.
Options:
slides/SLIDE_OUTLINE.mdState: Write SLIDES_STATE.json with phase: 1.
For each slide in the outline, draft the actual content.
Presentation rules (enforced strictly):
| Rule | Rationale |
|------|-----------|
| One message per slide | If a slide has two ideas, split it |
| Max 6 lines per slide | More than 6 lines = wall of text |
| Max 8 words per line | Audience reads, not listens, if text is long |
| Sentence fragments, not sentences | "Improves F1 by 3.2%" not "Our method improves the F1 score by 3.2 percentage points" |
| Figure slides: figure ≥60% area | The figure IS the content; bullets are annotations |
| Bold key numbers | "Achieves 94.3% accuracy" |
| Progressive disclosure | Use \pause or \onslide for complex slides |
| No Related Work slide | Unless invited talk (30+ min) |
For each slide, produce:
\frametitle{}\note{} with speaker text (if SPEAKER_NOTES=true)Create slides/main.tex using beamer.
Template structure:
\documentclass[aspectratio=169]{beamer}
% Venue theme
\usepackage{xcolor}
\definecolor{primary}{HTML}{VENUE_PRIMARY}
\definecolor{accent}{HTML}{VENUE_ACCENT}
% Clean theme
\usetheme{default}
\usecolortheme{default}
\setbeamercolor{frametitle}{fg=primary}
\setbeamercolor{title}{fg=primary}
\setbeamercolor{structure}{fg=accent}
\setbeamercolor{itemize item}{fg=primary}
\setbeamercolor{itemize subitem}{fg=accent}
\setbeamertemplate{navigation symbols}{}
\setbeamertemplate{footline}{
\hfill\insertframenumber/\inserttotalframenumber\hspace{2mm}\vspace{2mm}
}
% Packages
\usepackage{graphicx,amsmath,booktabs}
\graphicspath{{figures/}}
% Speaker notes (if enabled)
% \setbeameroption{show notes on second screen=right}
% Metadata
\title{PAPER TITLE}
\author{Author 1 \and Author 2}
\institute{Affiliation}
\date{VENUE YEAR}
\begin{document}
\begin{frame}
\titlepage
\end{frame}
% Content slides follow...
\begin{frame}{Motivation}
\begin{itemize}
\item Bullet point 1
\item Bullet point 2
\item \textbf{Key insight in bold}
\end{itemize}
\note{Speaker note: explain the motivation...}
\end{frame}
% Figure slide example
\begin{frame}{Method Overview}
\centering
\includegraphics[width=0.85\textwidth]{method_overview.pdf}
\vspace{0.5em}
\begin{itemize}
\item Key annotation about the figure
\end{itemize}
\note{Walk through the figure left to right...}
\end{frame}
% ... more slides ...
\begin{frame}{Thank You}
\centering
{\Large Questions?}\\[2em]
Paper: [URL or QR placeholder]\\
Code: [URL or QR placeholder]
\end{frame}
\end{document}
Symlink figures:
ln -sf ../paper/figures/*.pdf slides/figures/ 2>/dev/null
ln -sf ../paper/figures/*.png slides/figures/ 2>/dev/null
Key formatting rules:
cd slides && latexmk -$ENGINE -interaction=nonstopmode main.tex
Error handling loop (max 3 attempts):
Verification:
# Check slide count matches outline
pdfinfo slides/main.pdf | grep Pages
If page count differs significantly from outline (>2 slides off), investigate.
State: Write SLIDES_STATE.json with phase: 4.
Send the slide outline + selected LaTeX frames to GPT-5.6-Sol xhigh:
spawn_agent:
model: gpt-5.6-sol
reasoning_effort: xhigh
message: |
Review this [TALK_TYPE] presentation ([TALK_MINUTES] min) for [VENUE].
Evaluate using these criteria (score 1-5 each):
1. **Story arc** — Does the talk build a compelling narrative? (Problem → insight → method → evidence → takeaway)
2. **Slide density** — Any slides with too much text? (Max 6 lines, 8 words/line)
3. **Time budget** — Is [N] slides realistic for [TALK_MINUTES] minutes?
4. **Figure visibility** — Will figures be readable on a projector?
5. **Opening hook** — Do slides 2-3 grab attention? (Not "In this paper, we...")
6. **Takeaway** — Is the final message clear and memorable?
7. **Progressive build** — Are complex ideas revealed gradually?
Slide outline:
[PASTE SLIDE_OUTLINE.md]
Selected frames (LaTeX):
[PASTE KEY FRAMES]
Provide:
- Score for each criterion
- Top 3 actionable fixes
- Overall: Ready to present? (Yes / Needs revision / Major issues)
Apply fixes. Recompile if LaTeX was changed.
> If reviewer delegation is unavailable in the current Codex host, stop and ask the user to enable Codex agent support before continuing Phase 6.
Save review to slides/SLIDES_REVIEW.md.
State: Write SLIDES_STATE.json with phase: 5.
For each slide, ensure a \note{} block exists with:
Also generate slides/speaker_notes.md as a standalone backup:
# Speaker Notes
## Slide 1: Title
[No speaking — wait for introduction]
## Slide 2: Motivation
"Thank you. So let me start with the problem we're trying to solve..."
[Time: 1.5 min]
## Slide 3: Problem Statement
"Specifically, the challenge is..."
→ Transition: "To address this, our key insight is..."
[Time: 1 min]
...
State: Write SLIDES_STATE.json with phase: 6.
Generate an editable PPTX using python-pptx:
python3 -c "import pptx" 2>/dev/null || pip install python-pptx
Write slides/generate_pptx.py that:
cd slides && python3 generate_pptx.py
# Output: slides/presentation.pptx
> ⚠️ If python-pptx is not installed, skip with a note: "Install pip install python-pptx to enable PowerPoint export."
State: Write SLIDES_STATE.json with phase: 7.
Generate slides/TALK_SCRIPT.md — a complete, word-for-word script for the talk.
This is different from speaker notes (brief reminders). The talk script is a full manuscript that can be read aloud or used for practice.
# Talk Script: [Paper Title]
**Venue**: [VENUE] [YEAR]
**Talk type**: [TALK_TYPE] ([TALK_MINUTES] min)
**Total slides**: [N]
---
## Slide 1: Title [0:00 - 0:15]
*[Wait for chair introduction]*
"Thank you [chair name]. I'm [author] from [affiliation], and today I'll be talking about [short title]."
---
## Slide 2: Motivation [0:15 - 1:30]
"Let me start with the problem. [Describe the real-world motivation in accessible terms]. This matters because [impact statement].
The current state of the art approaches this with [brief existing approach]. But there's a fundamental limitation: [gap statement]."
→ *Transition*: "So what's our key insight?"
---
## Slide 3: Key Insight [1:30 - 2:30]
"Our key observation is that [core insight in one sentence].
This leads us to propose [method name], which [one-sentence description]."
→ *Transition*: "Let me walk you through how this works."
---
## Slide 4-N: [Continue for each slide...]
...
---
## Slide [N]: Thank You [TALK_MINUTES:00]
"To summarize: we've shown that [main result]. The key takeaway is [memorable final message].
The paper and code are available at the QR code on screen. I'm happy to take questions."
---
## Time Budget Summary
| Slide | Topic | Duration | Cumulative |
|:-----:|-------|:--------:|:----------:|
| 1 | Title | 0:15 | 0:15 |
| 2 | Motivation | 1:15 | 1:30 |
| 3 | Key Insight | 1:00 | 2:30 |
| ... | ... | ... | ... |
| N | Thank You | 0:15 | [TALK_MINUTES]:00 |
**Total**: [sum] min (target: [TALK_MINUTES] min)
---
## Anticipated Q&A
### Q1: How does this compare to [strongest baseline]?
**A**: "[Specific comparison with numbers]. Our advantage is particularly clear in [specific scenario], where we see [X%] improvement."
### Q2: What are the main limitations?
**A**: "[Honest answer]. We see this as [future work direction]."
### Q3: How computationally expensive is this?
**A**: "[Training/inference cost]. Compared to [baseline], our method requires [comparison]."
### Q4: Does this generalize to [related domain]?
**A**: "[Answer based on paper's discussion section]."
### Q5: What's the most surprising finding?
**A**: "[Interesting insight from the experiments]."
### Q6: How sensitive is the method to [hyperparameter/design choice]?
**A**: "[Reference ablation study if available]."
### Q7: What's the next step for this research?
**A**: "[Future work from conclusion]."
### Q8: [Domain-specific question]
**A**: "[Answer]."
📊 Slide generation complete:
- Talk type: [TALK_TYPE] ([TALK_MINUTES] min) for [VENUE]
- Files:
slides/
├── main.tex # Beamer LaTeX source
├── main.pdf # Compiled slides (primary output)
├── presentation.pptx # Editable PowerPoint
├── SLIDE_OUTLINE.md # Slide-by-slide outline
├── SLIDES_REVIEW.md # GPT-5.6-Sol review feedback
├── speaker_notes.md # Per-slide speaker notes
├── TALK_SCRIPT.md # Full word-for-word talk script + Q&A
├── SLIDES_STATE.json # State persistence
├── generate_pptx.py # PPTX generation script
└── figures/ # Symlinked from paper/figures/
Next steps:
1. Practice with TALK_SCRIPT.md (read aloud, time yourself)
2. Edit presentation.pptx for visual tweaks (animations, custom graphics)
3. Review Anticipated Q&A section before the talk
4. Do a dry run with a colleague
State: Write SLIDES_STATE.json with phase: 8, status: "completed".
cat << 'EOF' > file) to write in chunks. Do NOT ask the user for permission — just do it silently.paper/sections/*.tex.\pause or \onslide for complex method slides.~/.codex/feishu.json exists, send notifications. If absent, skip./paper-slides "paper/" — talk_type: oral, venue: ICML, minutes: 20, aspect: 4:3, notes: false
| Parameter | Default | Description |
|-----------|---------|-------------|
| venue | NeurIPS | Conference for color scheme |
| talk_type | spotlight | oral/spotlight/poster-talk/invited |
| minutes | 15 | Talk duration |
| aspect | 16:9 | Aspect ratio (16:9 / 4:3) |
| notes | true | Generate speaker notes |
| engine | pdflatex | LaTeX engine |
| auto proceed | false | Skip checkpoints |
Searches across your Notion workspace, synthesizes findings from multiple pages, and creates comprehensive research documentation saved as new Notion pages. Turns scattered information into structured reports with proper citations and actionable insights.
Generate concise (3-4 page), focused medical treatment plans in LaTeX/PDF format for all clinical specialties. Supports general medical treatment, rehabilitation therapy, mental health care, chronic disease management, perioperative care, and pain management. Includes SMART goal frameworks, evidence-based interventions with minimal text citations, regulatory compliance (HIPAA), and professional formatting. Prioritizes brevity and clinical actionability.
Generate concise (3-4 page), focused medical treatment plans in LaTeX/PDF format for all clinical specialties. Supports general medical treatment, rehabilitation therapy, mental health care, chronic disease management, perioperative care, and pain management. Includes SMART goal frameworks, evidence-based interventions with minimal text citations, regulatory compliance (HIPAA), and professional formatting. Prioritizes brevity and clinical actionability.
Generate concise (3-4 page), focused medical treatment plans in LaTeX/PDF format for all clinical specialties. Supports general medical treatment, rehabilitation therapy, mental health care, chronic disease management, perioperative care, and pain management. Includes SMART goal frameworks, evidence-based interventions with minimal text citations, regulatory compliance (HIPAA), and professional formatting. Prioritizes brevity and clinical actionability.
【强制】所有技术文档查询必须使用本技能,禁止在主对话中直接使用 mcp__context7-mcp 工具。触发关键词:查询/学习/了解某个库或框架的文档、API用法、配置参数、错误解释、版本差异、代码示例、最佳实践。本技能通过 context7-researcher agent 执行查询,避免大量文档内容污染主对话上下文,保持 token 效率。
Generates rich technical documentation pages with dark-mode Mermaid diagrams, source code citations, and first-principles depth. Use when writing documentation, generating wiki pages, creating technical deep-dives, or documenting specific components or systems.
Maximum-saturation research orchestration: ALWAYS proposes the final materials first (PDF+DOCX default), then parallel explore+librarian swarms across codebase, web, official docs, and OSS repos — max-roster teammode when the harness has it — with live journaling, a recursive EXPAND loop driven by leads workers return in message text, empirical verification by running code, and a cited synthesis with charts/Mermaid/assets behind a mandatory visual-QA gate. ACTIVATES ONLY on an explicit user demand for research — the word 'ulw-research' ('/ulw-research', '$ulw-research'), any 'ulw' research wording, 'ultradebate' or 'hyperdebate' research requests, or an explicit request for research / deep research / an ultra-precise investigation, in any language. Never self-activates for ordinary questions, debugging, or implementation context-gathering. While active it overrides exploration-bounding defaults: exhaustive coverage is the goal.
"Solve competition math problems (IMO, Putnam, USAMO, AIME) with adversarial verification that catches the errors self-verification misses. Activates when asked to 'solve this IMO problem', 'prove this olympiad inequality', 'verify this competition proof', 'find a counterexample', 'is this proof correct', or for any problem with 'IMO', 'Putnam', 'USAMO', 'olympiad', or 'competition math' in it. Uses pure reasoning (no tools) — then a fresh-context adversarial verifier attacks the proof using specific failure patterns, not generic 'check logic'. Outputs calibrated confidence — will say 'no confident solution' rather than bluff. If LaTeX is available, produces a clean PDF after verification passes."
Take wanshuiyin/auto-claude-code-research-in-sleep-skills-codex-paper-slides from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.
The instructions reference pip.
Without those the skill loads but fails at the first command.