[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6KQB4965XHN5PN1RPMNE5PQ":3,"skill-same:01M6KQB4965XHN5PN1RPMNE5PQ":95},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":33,"publishedAt":55,"linkOnly":56,"markdownContent":57,"markdownBody":58,"frontMatter":59,"refName":69,"commitSha":70,"skillDirectory":71,"forksCount":13,"repositoryHtmlUrl":72,"repositoryHomepageUrl":6,"ownerHtmlUrl":73,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":74,"install":75,"resources":88},"01M6KQB4965XHN5PN1RPMNE5PQ","presenting-conference-talks",null,"Generates conference presentation slides (Beamer LaTeX PDF and editable PPTX) from a compiled paper with speaker notes and talk script. Use when preparing oral talks, spotlight presentations, or invited talks for ML and systems conferences.","endman100\u002Fskill-router-nexus","endman100","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F31997019?v=4","01M6KPDX17BYDWW4RRB0TNGGMX","01M6KPDX64Y2QBEWVFG5W5PWPF",0,"MIT","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FAI-Research\u002Fpresenting-conference-talks\u002FSKILL.md",[17,22,25,28],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000002","文档处理","SCENARIO","使用场景",{"categoryNo":23,"categoryName":24,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000003","演示文稿",{"categoryNo":26,"categoryName":27,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":29,"categoryName":30,"taxonomyType":31,"taxonomyTypeDesc":32},"01JBSEEDCAT000000000000034","行政与办公人员","PROFESSION","适用职业",[34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54],"Presenting Conference Talks","OSDI","when","Speaker Notes","Beamer","for","orchestra","editable","presentation","SOSP","ASPLOS","PDF","spotlight","python-pptx","use","slides","ml","presentations","notes","python","pptx","2026-11-10 10:12:30",false,"---\nname: presenting-conference-talks\ndescription: Generates conference presentation slides (Beamer LaTeX PDF and editable PPTX) from a compiled paper with speaker notes and talk script. Use when preparing oral talks, spotlight presentations, or invited talks for ML and systems conferences.\nversion: 1.0.0\nauthor: Orchestra Research\nlicense: MIT\ntags: [Presenting Conference Talks, Beamer, PPTX, Slides, Speaker Notes, OSDI, SOSP, ASPLOS, NeurIPS, ICML]\ndependencies: [python-pptx>=0.6.21]\n---\n\n# Presenting Conference Talks: From Paper to Slides\n\nGenerate conference presentation slides from a compiled research paper. Produces both **Beamer LaTeX PDF** (for polished typesetting) and **editable PPTX** (for last-minute adjustments), with speaker notes and an optional talk script.\n\n## When to Use This Skill\n\n| Scenario | Use This Skill | Use Other Skills Instead |\n|----------|---------------|--------------------------|\n| Preparing oral\u002Fspotlight\u002Fposter-talk slides | ✅ | |\n| Generating Beamer PDF + PPTX from paper | ✅ | |\n| Speaker notes and talk script | ✅ | |\n| Writing the paper itself | | ml-paper-writing |\n| Structuring a systems paper | | systems-paper-writing |\n| Creating publication-quality plots | | academic-plotting |\n\n**Attribution**: This skill's structure draws inspiration from the ARIS paper-slides skill (570 lines, supporting poster\u002Fspotlight\u002Foral\u002Finvited with Beamer+PPTX). This is an independent implementation for the AI-Research-SKILLs ecosystem.\n\n---\n\n## Talk Types and Slide Counts\n\n| Talk Type | Duration | Slides | Content Depth |\n|-----------|----------|--------|---------------|\n| poster-talk | 3–5 min | 5–8 | Problem + key result only |\n| spotlight | 5–8 min | 8–12 | Problem + approach + key results |\n| oral | 15–20 min | 15–22 | Full story with evaluation highlights |\n| invited | 30–45 min | 25–40 | Deep dive with context and demos |\n\n**Rule of thumb**: ~1 slide per minute for oral, ~1.5 slides per minute for spotlight.\n\n---\n\n## Slide Structure Templates\n\n### Poster-Talk (5–8 slides)\n\n```text\nSlide 1: Title + Authors + Affiliation\nSlide 2: Problem — Why this matters (1 motivating figure)\nSlide 3: Key Insight — One-sentence thesis\nSlide 4: Approach Overview — Architecture diagram\nSlide 5: Main Result — Headline numbers (1 figure)\nSlide 6: Takeaway + QR code to paper\u002Fcode\n```\n\n### Spotlight (8–12 slides)\n\n```text\nSlide 1:  Title + Authors\nSlide 2:  Problem Statement — Concrete, quantified\nSlide 3:  Motivation — Why existing solutions fall short\nSlide 4:  Key Insight — Thesis statement\nSlide 5:  System Overview — Architecture diagram\nSlide 6:  Design Highlight 1 — Core mechanism\nSlide 7:  Design Highlight 2 — Key innovation\nSlide 8:  Evaluation Setup — Baselines and workloads (brief)\nSlide 9:  Main Results — Headline performance figure\nSlide 10: Ablation \u002F Breakdown — What contributes most\nSlide 11: Summary + Contributions\nSlide 12: Thank You + Links\n```\n\n### Oral (15–22 slides)\n\n```text\nSlide 1:  Title + Authors + Venue\nSlide 2:  Outline (optional — \"roadmap\" slide)\nSlide 3:  Problem Context — Domain importance\nSlide 4:  Problem Statement — Specific challenge\nSlide 5:  Motivation — Gaps in existing systems\nSlide 6:  Key Insight — Thesis\nSlide 7:  System Overview — Architecture diagram\nSlide 8:  Design Component 1 — Detailed walkthrough\nSlide 9:  Design Component 2 — Detailed walkthrough\nSlide 10: Design Component 3 — Detailed walkthrough\nSlide 11: Design Alternatives — Why not other approaches\nSlide 12: Implementation — Key engineering highlights\nSlide 13: Evaluation Setup — Testbed, baselines, metrics\nSlide 14: End-to-End Results — Main performance\nSlide 15: Result Deep Dive — Breakdown or per-workload\nSlide 16: Ablation Study — Component contributions\nSlide 17: Scalability — Scaling behavior\nSlide 18: Demo Slide (systems talks) — Screenshot or recording\nSlide 19: Related Work — Positioning (brief)\nSlide 20: Summary — Contributions restated\nSlide 21: Future Work — Open questions\nSlide 22: Thank You + Paper Link + QR Code\n```\n\n### Invited Talk (25–40 slides)\nExtends the oral structure with:\n- Additional context slides (field overview, historical progression)\n- Multiple demo\u002Fwalkthrough slides\n- Deeper evaluation analysis\n- Broader implications and future directions\n- Q&A preparation slides (hidden, for backup)\n\n---\n\n## Systems Talk Specifics\n\nSystems conference talks have unique requirements compared to ML talks:\n\n### Demo Slide\n- Include a **live demo** or **pre-recorded screencast** of the system in action\n- Always have a **recorded backup** — live demos fail at the worst times\n- Show the system under realistic load, not toy examples\n\n### Architecture Walkthrough\n- Animate the architecture diagram: highlight components as you explain them\n- Use Beamer `\\only\u003CN>` or `\\onslide\u003CN>` for progressive reveal\n- Walk through a **concrete request** end-to-end through the system\n\n### Evaluation Highlights\n- Select 2–3 strongest figures from the paper\n- Annotate figures on slides (arrows, circles highlighting key points)\n- State the takeaway **before** showing the figure (\"Our system is 2x faster — here's the data\")\n\n---\n\n## Speaker Notes Guidelines\n\n### Structure per Slide\n```text\n[Timing: X minutes]\n[Key point to convey]\n[Transition sentence to next slide]\n```\n\n### Mike Dahlin's Layered Approach\nApply \"Say what you're going to say, say it, then say what you said\" at three levels:\n\n1. **Talk level**: Outline slide → body → summary slide\n2. **Section level**: Section heading → content slides → section takeaway\n3. **Slide level**: Headline statement → supporting evidence → transition\n\n### Timing Guidelines\n- Poster-talk: 30–60 sec per slide\n- Spotlight: 30–45 sec per slide\n- Oral: 45–90 sec per slide\n- Invited: 60–120 sec per slide\n\n---\n\n## Output Formats\n\n### Beamer LaTeX → PDF\n\nAdvantages: Professional typesetting, math support, version control friendly.\n\n```latex\n\\documentclass[aspectratio=169]{beamer}\n\\usetheme{metropolis}  % Clean, modern theme\n\\usepackage{appendixnumberbeamer}\n\n\\title{Your Paper Title}\n\\subtitle{Venue Year}\n\\author{Author 1 \\and Author 2}\n\\institute{Institution}\n\\date{}\n\n\\begin{document}\n\\maketitle\n\n\\begin{frame}{Problem}\n  \\begin{itemize}\n    \\item Key problem statement\n    \\item Concrete motivation with numbers\n  \\end{itemize}\n  \\note{Speaker note: Start with the big picture...}\n\\end{frame}\n\n% ... more frames ...\n\\end{document}\n```\n\n### python-pptx → Editable PPTX\n\nAdvantages: Easy last-minute edits, corporate template compatibility, animations.\n\n```python\nfrom pptx import Presentation\nfrom pptx.util import Inches, Pt\nfrom pptx.enum.text import PP_ALIGN\n\nprs = Presentation()\nprs.slide_width = Inches(13.333)  # 16:9\nprs.slide_height = Inches(7.5)\n\n# Title slide\nslide = prs.slides.add_slide(prs.slide_layouts[0])\nslide.shapes.title.text = \"Your Paper Title\"\nslide.placeholders[1].text = \"Author 1, Author 2\\nVenue Year\"\n\n# Content slide\nslide = prs.slides.add_slide(prs.slide_layouts[1])\nslide.shapes.title.text = \"Problem Statement\"\nbody = slide.placeholders[1]\nbody.text = \"Key point 1\\nKey point 2\"\n\n# Add speaker notes\nnotes_slide = slide.notes_slide\nnotes_slide.notes_text_frame.text = \"Speaker note: explain the motivation...\"\n\nprs.save(\"talk.pptx\")\n```\n\n---\n\n## Color Scheme Suggestions\n\n> These are aesthetic suggestions, not official venue requirements. Adjust freely.\n\n| Venue Type | Primary | Accent | Background |\n|-----------|---------|--------|------------|\n| USENIX (OSDI\u002FNSDI) | Dark Blue (#003366) | Red (#CC0000) | White |\n| ACM (SOSP\u002FASPLOS) | ACM Blue (#0071BC) | Dark Gray (#333333) | White |\n| NeurIPS | Purple (#7B2D8E) | Gold (#F0AD00) | White |\n| ICML | Teal (#008080) | Orange (#FF6600) | White |\n| Generic | Dark Gray (#333333) | Blue (#0066CC) | White |\n\n---\n\n## Workflow\n\n### Step 1: Content Extraction\n```text\n- Read the compiled paper (PDF or LaTeX source)\n- Identify: thesis, contributions, architecture figure, key eval figures\n- Note the talk type and duration\n```\n\n### Step 2: Outline Generation\n```text\n- Select the appropriate slide structure template (above)\n- Map paper sections to slide groups\n- Allocate time per slide group\n```\n\n### Step 3: Slide-by-Slide Generation\n```text\n- Generate Beamer source slide by slide\n- Add speaker notes per slide\n- Include figures from paper (copy to slides\u002F directory)\n- Generate python-pptx script for PPTX version\n```\n\n### Step 4: Review and Polish\n```text\n- Check total slide count matches talk duration\n- Verify all figures are readable at presentation resolution\n- Run Beamer compilation: latexmk -pdf slides.tex\n- Run PPTX generation: python3 generate_slides.py\n- Review speaker notes for timing and transitions\n```\n\n### Quick Checklist\n- [ ] Slide count appropriate for talk type\u002Fduration\n- [ ] Title slide has correct authors, affiliations, venue\n- [ ] Architecture diagram included and clearly labeled\n- [ ] Key eval figures annotated with takeaways\n- [ ] Speaker notes include timing markers\n- [ ] Transitions between sections are smooth\n- [ ] Demo slide has recorded backup\n- [ ] Thank-you slide includes paper link \u002F QR code\n- [ ] Font sizes ≥ 24pt for readability from back of room\n- [ ] Consistent color scheme throughout\n\n---\n\n## Common Issues and Solutions\n\n| Issue | Solution |\n|-------|----------|\n| Too many slides for time limit | Cut details, keep one figure per point |\n| Slides feel like paper paragraphs | Use bullet points (≤ 6 per slide), let figures tell the story |\n| Audience lost during design section | Add architecture walkthrough with progressive reveal |\n| Evaluation slides overwhelming | Show 2–3 strongest figures, put rest in backup slides |\n| Speaker notes too long | Target 3–4 sentences per slide, focus on transitions |\n| Beamer compilation fails | Check figure paths, use `\\graphicspath{{figures\u002F}}` |\n| PPTX looks different from Beamer | Adjust python-pptx font sizes and margins manually |\n\n---\n\n## References\n\n- [references\u002Fslide-templates.md](references\u002Fslide-templates.md) — Complete Beamer template code and python-pptx generation script\n- Mike Dahlin, \"Giving a Conference Talk\" — https:\u002F\u002Fwww.cs.utexas.edu\u002F~dahlin\u002Fprofessional\u002FgoodTalk.pdf\n","# Presenting Conference Talks: From Paper to Slides\n\nGenerate conference presentation slides from a compiled research paper. Produces both **Beamer LaTeX PDF** (for polished typesetting) and **editable PPTX** (for last-minute adjustments), with speaker notes and an optional talk script.\n\n## When to Use This Skill\n\n| Scenario | Use This Skill | Use Other Skills Instead |\n|----------|---------------|--------------------------|\n| Preparing oral\u002Fspotlight\u002Fposter-talk slides | ✅ | |\n| Generating Beamer PDF + PPTX from paper | ✅ | |\n| Speaker notes and talk script | ✅ | |\n| Writing the paper itself | | ml-paper-writing |\n| Structuring a systems paper | | systems-paper-writing |\n| Creating publication-quality plots | | academic-plotting |\n\n**Attribution**: This skill's structure draws inspiration from the ARIS paper-slides skill (570 lines, supporting poster\u002Fspotlight\u002Foral\u002Finvited with Beamer+PPTX). This is an independent implementation for the AI-Research-SKILLs ecosystem.\n\n---\n\n## Talk Types and Slide Counts\n\n| Talk Type | Duration | Slides | Content Depth |\n|-----------|----------|--------|---------------|\n| poster-talk | 3–5 min | 5–8 | Problem + key result only |\n| spotlight | 5–8 min | 8–12 | Problem + approach + key results |\n| oral | 15–20 min | 15–22 | Full story with evaluation highlights |\n| invited | 30–45 min | 25–40 | Deep dive with context and demos |\n\n**Rule of thumb**: ~1 slide per minute for oral, ~1.5 slides per minute for spotlight.\n\n---\n\n## Slide Structure Templates\n\n### Poster-Talk (5–8 slides)\n\n```text\nSlide 1: Title + Authors + Affiliation\nSlide 2: Problem — Why this matters (1 motivating figure)\nSlide 3: Key Insight — One-sentence thesis\nSlide 4: Approach Overview — Architecture diagram\nSlide 5: Main Result — Headline numbers (1 figure)\nSlide 6: Takeaway + QR code to paper\u002Fcode\n```\n\n### Spotlight (8–12 slides)\n\n```text\nSlide 1:  Title + Authors\nSlide 2:  Problem Statement — Concrete, quantified\nSlide 3:  Motivation — Why existing solutions fall short\nSlide 4:  Key Insight — Thesis statement\nSlide 5:  System Overview — Architecture diagram\nSlide 6:  Design Highlight 1 — Core mechanism\nSlide 7:  Design Highlight 2 — Key innovation\nSlide 8:  Evaluation Setup — Baselines and workloads (brief)\nSlide 9:  Main Results — Headline performance figure\nSlide 10: Ablation \u002F Breakdown — What contributes most\nSlide 11: Summary + Contributions\nSlide 12: Thank You + Links\n```\n\n### Oral (15–22 slides)\n\n```text\nSlide 1:  Title + Authors + Venue\nSlide 2:  Outline (optional — \"roadmap\" slide)\nSlide 3:  Problem Context — Domain importance\nSlide 4:  Problem Statement — Specific challenge\nSlide 5:  Motivation — Gaps in existing systems\nSlide 6:  Key Insight — Thesis\nSlide 7:  System Overview — Architecture diagram\nSlide 8:  Design Component 1 — Detailed walkthrough\nSlide 9:  Design Component 2 — Detailed walkthrough\nSlide 10: Design Component 3 — Detailed walkthrough\nSlide 11: Design Alternatives — Why not other approaches\nSlide 12: Implementation — Key engineering highlights\nSlide 13: Evaluation Setup — Testbed, baselines, metrics\nSlide 14: End-to-End Results — Main performance\nSlide 15: Result Deep Dive — Breakdown or per-workload\nSlide 16: Ablation Study — Component contributions\nSlide 17: Scalability — Scaling behavior\nSlide 18: Demo Slide (systems talks) — Screenshot or recording\nSlide 19: Related Work — Positioning (brief)\nSlide 20: Summary — Contributions restated\nSlide 21: Future Work — Open questions\nSlide 22: Thank You + Paper Link + QR Code\n```\n\n### Invited Talk (25–40 slides)\nExtends the oral structure with:\n- Additional context slides (field overview, historical progression)\n- Multiple demo\u002Fwalkthrough slides\n- Deeper evaluation analysis\n- Broader implications and future directions\n- Q&A preparation slides (hidden, for backup)\n\n---\n\n## Systems Talk Specifics\n\nSystems conference talks have unique requirements compared to ML talks:\n\n### Demo Slide\n- Include a **live demo** or **pre-recorded screencast** of the system in action\n- Always have a **recorded backup** — live demos fail at the worst times\n- Show the system under realistic load, not toy examples\n\n### Architecture Walkthrough\n- Animate the architecture diagram: highlight components as you explain them\n- Use Beamer `\\only\u003CN>` or `\\onslide\u003CN>` for progressive reveal\n- Walk through a **concrete request** end-to-end through the system\n\n### Evaluation Highlights\n- Select 2–3 strongest figures from the paper\n- Annotate figures on slides (arrows, circles highlighting key points)\n- State the takeaway **before** showing the figure (\"Our system is 2x faster — here's the data\")\n\n---\n\n## Speaker Notes Guidelines\n\n### Structure per Slide\n```text\n[Timing: X minutes]\n[Key point to convey]\n[Transition sentence to next slide]\n```\n\n### Mike Dahlin's Layered Approach\nApply \"Say what you're going to say, say it, then say what you said\" at three levels:\n\n1. **Talk level**: Outline slide → body → summary slide\n2. **Section level**: Section heading → content slides → section takeaway\n3. **Slide level**: Headline statement → supporting evidence → transition\n\n### Timing Guidelines\n- Poster-talk: 30–60 sec per slide\n- Spotlight: 30–45 sec per slide\n- Oral: 45–90 sec per slide\n- Invited: 60–120 sec per slide\n\n---\n\n## Output Formats\n\n### Beamer LaTeX → PDF\n\nAdvantages: Professional typesetting, math support, version control friendly.\n\n```latex\n\\documentclass[aspectratio=169]{beamer}\n\\usetheme{metropolis}  % Clean, modern theme\n\\usepackage{appendixnumberbeamer}\n\n\\title{Your Paper Title}\n\\subtitle{Venue Year}\n\\author{Author 1 \\and Author 2}\n\\institute{Institution}\n\\date{}\n\n\\begin{document}\n\\maketitle\n\n\\begin{frame}{Problem}\n  \\begin{itemize}\n    \\item Key problem statement\n    \\item Concrete motivation with numbers\n  \\end{itemize}\n  \\note{Speaker note: Start with the big picture...}\n\\end{frame}\n\n% ... more frames ...\n\\end{document}\n```\n\n### python-pptx → Editable PPTX\n\nAdvantages: Easy last-minute edits, corporate template compatibility, animations.\n\n```python\nfrom pptx import Presentation\nfrom pptx.util import Inches, Pt\nfrom pptx.enum.text import PP_ALIGN\n\nprs = Presentation()\nprs.slide_width = Inches(13.333)  # 16:9\nprs.slide_height = Inches(7.5)\n\n# Title slide\nslide = prs.slides.add_slide(prs.slide_layouts[0])\nslide.shapes.title.text = \"Your Paper Title\"\nslide.placeholders[1].text = \"Author 1, Author 2\\nVenue Year\"\n\n# Content slide\nslide = prs.slides.add_slide(prs.slide_layouts[1])\nslide.shapes.title.text = \"Problem Statement\"\nbody = slide.placeholders[1]\nbody.text = \"Key point 1\\nKey point 2\"\n\n# Add speaker notes\nnotes_slide = slide.notes_slide\nnotes_slide.notes_text_frame.text = \"Speaker note: explain the motivation...\"\n\nprs.save(\"talk.pptx\")\n```\n\n---\n\n## Color Scheme Suggestions\n\n> These are aesthetic suggestions, not official venue requirements. Adjust freely.\n\n| Venue Type | Primary | Accent | Background |\n|-----------|---------|--------|------------|\n| USENIX (OSDI\u002FNSDI) | Dark Blue (#003366) | Red (#CC0000) | White |\n| ACM (SOSP\u002FASPLOS) | ACM Blue (#0071BC) | Dark Gray (#333333) | White |\n| NeurIPS | Purple (#7B2D8E) | Gold (#F0AD00) | White |\n| ICML | Teal (#008080) | Orange (#FF6600) | White |\n| Generic | Dark Gray (#333333) | Blue (#0066CC) | White |\n\n---\n\n## Workflow\n\n### Step 1: Content Extraction\n```text\n- Read the compiled paper (PDF or LaTeX source)\n- Identify: thesis, contributions, architecture figure, key eval figures\n- Note the talk type and duration\n```\n\n### Step 2: Outline Generation\n```text\n- Select the appropriate slide structure template (above)\n- Map paper sections to slide groups\n- Allocate time per slide group\n```\n\n### Step 3: Slide-by-Slide Generation\n```text\n- Generate Beamer source slide by slide\n- Add speaker notes per slide\n- Include figures from paper (copy to slides\u002F directory)\n- Generate python-pptx script for PPTX version\n```\n\n### Step 4: Review and Polish\n```text\n- Check total slide count matches talk duration\n- Verify all figures are readable at presentation resolution\n- Run Beamer compilation: latexmk -pdf slides.tex\n- Run PPTX generation: python3 generate_slides.py\n- Review speaker notes for timing and transitions\n```\n\n### Quick Checklist\n- [ ] Slide count appropriate for talk type\u002Fduration\n- [ ] Title slide has correct authors, affiliations, venue\n- [ ] Architecture diagram included and clearly labeled\n- [ ] Key eval figures annotated with takeaways\n- [ ] Speaker notes include timing markers\n- [ ] Transitions between sections are smooth\n- [ ] Demo slide has recorded backup\n- [ ] Thank-you slide includes paper link \u002F QR code\n- [ ] Font sizes ≥ 24pt for readability from back of room\n- [ ] Consistent color scheme throughout\n\n---\n\n## Common Issues and Solutions\n\n| Issue | Solution |\n|-------|----------|\n| Too many slides for time limit | Cut details, keep one figure per point |\n| Slides feel like paper paragraphs | Use bullet points (≤ 6 per slide), let figures tell the story |\n| Audience lost during design section | Add architecture walkthrough with progressive reveal |\n| Evaluation slides overwhelming | Show 2–3 strongest figures, put rest in backup slides |\n| Speaker notes too long | Target 3–4 sentences per slide, focus on transitions |\n| Beamer compilation fails | Check figure paths, use `\\graphicspath{{figures\u002F}}` |\n| PPTX looks different from Beamer | Adjust python-pptx font sizes and margins manually |\n\n---\n\n## References\n\n- [references\u002Fslide-templates.md](references\u002Fslide-templates.md) — Complete Beamer template code and python-pptx generation script\n- Mike Dahlin, \"Giving a Conference Talk\" — https:\u002F\u002Fwww.cs.utexas.edu\u002F~dahlin\u002Fprofessional\u002FgoodTalk.pdf\n",{"name":5,"tags":60,"author":65,"license":14,"version":66,"description":7,"dependencies":67},[34,38,61,62,37,35,43,44,63,64],"PPTX","Slides","NeurIPS","ICML","Orchestra Research","1.0.0",[68],"python-pptx>=0.6.21","main","f19b254e8975d0bfddb394686706106930d7964d","AI-Research\u002Fpresenting-conference-talks","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus","https:\u002F\u002Fgithub.com\u002Fendman100","2026-11-12 18:13:37",{"source":8,"skillName":5,"commands":76,"prompt":86,"zipAvailable":87},[77,80,83],{"tool":78,"command":79},"npx","npx skills add endman100\u002Fskill-router-nexus --skill presenting-conference-talks",{"tool":81,"command":82},"bunx","bunx skills add endman100\u002Fskill-router-nexus --skill presenting-conference-talks",{"tool":84,"command":85},"pnpm","pnpm dlx skills add endman100\u002Fskill-router-nexus --skill presenting-conference-talks","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FAI-Research\u002Fpresenting-conference-talks\u002FSKILL.md",true,[89],{"path":90,"resourceType":91,"resourceTypeDesc":92,"sizeBytes":93,"sourceHtmlUrl":94},"references\u002Fslide-templates.md","REFERENCE","参考文档",15279,"https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FAI-Research\u002Fpresenting-conference-talks\u002Freferences\u002Fslide-templates.md",[96,118,152,174,183,207],{"skillNo":97,"name":98,"nameZh":6,"description":99,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":100,"sourceHtmlUrl":101,"categories":102,"keywords":106,"publishedAt":55},"01M6MPCT5BXVER8HQZTW3QE9AS","create-master","基于佛教经典文献，生成特定高僧大德的 AI 教学角色","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FMetaphysics-and-Spirituality\u002FMaster-skill\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FMetaphysics-and-Spirituality\u002FMaster-skill\u002FSKILL.md",[103],{"categoryNo":104,"categoryName":105,"taxonomyType":31,"taxonomyTypeDesc":32},"01JBSEEDCAT000000000000039","教师与教育",[107,108,109,110,111,112,113,114,115,116,117],"create master","master","create","生成","true","webfetch","edit","write","read","grep","Bash",{"skillNo":119,"name":120,"nameZh":6,"description":121,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":122,"sourceHtmlUrl":123,"categories":124,"keywords":134,"publishedAt":55},"01M6M43AKE95Y64E1JRXEF5QQ3","writing-dna-skill","从至少 20 篇完整文章中蒸馏可复用的写作 DNA，分析语言、文章结构、选题逻辑、素材策略、认知框架和视觉风格，并生成 Writing-DNA.md；按该 DNA 写作时会读取全部蒸馏产物和 5 篇相关原文。用于中英文作者、账号、品牌或出版物的风格分析与一致性写作。 Distill reusable Writing DNA from at least 20 complete articles for Chinese or English authors, publications, brands, and accounts; use for language, structure, topic logic, source strategy, cognitive-frame, and visual-style analysis.","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fwriting-dna-skill\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fwriting-dna-skill\u002FSKILL.md",[125,128,131],{"categoryNo":126,"categoryName":127,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":129,"categoryName":130,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":132,"categoryName":133,"taxonomyType":31,"taxonomyTypeDesc":32},"01JBSEEDCAT000000000000036","内容创作者",[135,136,137,138,139,140,141,142,143,144,48,145,146,110,147,148,149,39,150,151],"writing dna skill","DNA","writing","strategy","english","logic","visual","structure","chinese","reusable","style","distill","analysis","frame","source","at","complete",{"skillNo":153,"name":154,"nameZh":6,"description":155,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":156,"sourceHtmlUrl":157,"categories":158,"keywords":161,"publishedAt":55},"01M6M428TTCGQ4ME3KY99VN037","stop-slop","Remove AI writing patterns from prose. Use when drafting, editing, or reviewing text to eliminate predictable AI tells.","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fstop-slop\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fstop-slop\u002FSKILL.md",[159,160],{"categoryNo":126,"categoryName":127,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":132,"categoryName":133,"taxonomyType":31,"taxonomyTypeDesc":32},[162,163,164,165,36,166,167,168,169,48,170,171,172,39,173,137],"slop","stop slop","stop","drafting","drafts","https","patterns","prose","remove","editing","text","content",{"skillNo":175,"name":176,"nameZh":6,"description":177,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":178,"sourceHtmlUrl":179,"categories":180,"keywords":181,"publishedAt":55},"01M6M41X0B7EBX42TK435DMGGR","shuorenhua","检查和清理中英文文本里的 AI 套路，适用于“去 AI 味”“说人话”“自然一点”“别像模板”“先标问题”这类改写和审稿需求；按场景控制力度，同时保留事实、术语、语域和责任主体。","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fshuorenhua\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fshuorenhua\u002FSKILL.md",[],[176,182],"文本",{"skillNo":184,"name":185,"nameZh":6,"description":186,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":187,"sourceHtmlUrl":188,"categories":189,"keywords":190,"publishedAt":55},"01M6M3ZD12WBEW5KXKDM4N6DFG","naming","Name products, SaaS, brands, open source projects, bots, and apps. Use when the user needs to name something, find a brand name, or pick a product name. Metaphor-driven process that produces memorable, meaningful names and avoids AI slop.","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fnaming-main\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fnaming-main\u002FSKILL.md",[],[185,191,192,193,194,195,48,196,115,149,197,198,199,200,117,201,202,203,204,36,205,206,162],"repo","websearch","product name","apps","bots","view","brand","pick","gh","projects","open","saas","open source","product","describe","ai slop",{"skillNo":208,"name":209,"nameZh":6,"description":210,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":211,"sourceHtmlUrl":212,"categories":213,"keywords":215,"publishedAt":55},"01M6M3YVXAFAAWJ7F6JMPFREYE","humanizer-zh","去除文本中的 AI 生成痕迹。适用于编辑或审阅文本，使其听起来更自然、更像人类书写。\n基于维基百科的\"AI 写作特征\"综合指南。检测并修复以下模式：夸大的象征意义、\n宣传性语言、以 -ing 结尾的肤浅分析、模糊的归因、破折号过度使用、三段式法则、\nAI 词汇、否定式排比、过多的连接性短语。","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fhumanizer-zh\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fhumanizer-zh\u002FSKILL.md",[214],{"categoryNo":126,"categoryName":127,"taxonomyType":20,"taxonomyTypeDesc":21},[216,217,218,115,114,113,137,110,182,219,162,164],"humanizer","zh","humanizer zh","翻译"]