/making-academic-presentations

name: making-academic-presentations
description: >-
  Create academic presentation slide decks and optionally demo videos from
  research papers. Use when the user asks to "make slides", "create a deck",
  "make a presentation", "demo video", "paper slides", "conference talk slides",
  or wants to turn a paper into a visual presentation. Covers slide generation,
  narration scripts, TTS audio, and video assembly.

Making Academic Presentations

Produce slide decks (and optionally narrated demo videos) from research papers. The human drives all outline and visual decisions — the agent executes.

Pipeline

[1] Script Draft ──→ [2] Slide Generation ──→ [3] TTS Audio (optional) ──→ [4] Video Assembly (optional)
     Claude Code          nanobanana /edit     edge-tts / Kokoro / ElevenLabs       ffmpeg

Skip stages 3–4 for slide-only output. User can enter at any stage.

Stage 1: Script / Outline

**Input**: paper + user-provided outline or slide plan **Output**: `video-scripts.md` or `slide-outline.md` — per-slide content with talking points

The agent drafts scripts based on the user's outline. The user owns the structure — agent does not decide slide count, order, or what to emphasize.

Stage 2: Slide Generation

> Full reference: [references/slide-generation.md](references/slide-generation.md)

**Tool**: nanobanana (Gemini CLI extension)

**Priority order** (edit-first): 1. **Has paper figure** → nanobanana `/edit` to wrap into slide frame 2. **Has existing slide** → `/edit` to adapt 3. **User-provided reference** (e.g., from NotebookLM or PPTX the user made) → `/edit` to refine 4. **Title slide from scratch** → generate with academic style prompt 5. **Content slide from scratch** → generate with deck-style preamble

**Key principle**: prefer `/edit` on existing HQ paper figures over generating from scratch.

**Deck style**: create `deck-style.md` once per deck, prepend to all generate-from-scratch prompts. For `/edit`, style is inherited from the base image.

Example `deck-style.md`:

- Canvas: 1920x1080, white background
- Accent: #2563EB blue, text: #1e293b dark slate
- Clean sans-serif, flat design, no gradients/shadows
- Bottom bar: blue accent with white affiliation text

Stage 3: TTS Audio (optional)

> Full reference: [references/tts-engines.md](references/tts-engines.md) > Batch scripts: [scripts/batch_tts_edge.py](scripts/batch_tts_edge.py), [scripts/batch_tts_kokoro.py](scripts/batch_tts_kokoro.py)

**Output**: one audio file per narrated slide

Engine Selection

| Engine | Quality | Cost | Latency | Best For | |--------|---------|------|---------|----------| | **edge-tts** (default) | Very good | Free, unlimited | ~6s/slide (cloud) | Quick generation, good male voices | | **Kokoro** | Very good | Free, unlimited | ~1.5s/slide (local) | Offline use, fast batch, good female voices | | **ElevenLabs** | Premium | 10k chars free/mo | ~3s/slide (cloud) | Highest quality, voice cloning |

**Default**: Use edge-tts unless user requests offline or premium quality.

Quick Start (edge-tts)

import edge_tts, asyncio

async def tts_slide(text, output, voice="en-US-AndrewNeural"):
    await edge_tts.Communicate(text, voice).save(output)

asyncio.run(tts_slide("Your slide text here", "slide_01.mp3"))

**Voices**: AndrewNeural (male, presenter), AriaNeural (female), GuyNeural (male, warm), JennyNeural (female, pro)

Stage 4: Video Assembly (optional)

**Tool**: ffmpeg **Input**: slide PNGs + audio files + optional demo recording

# Use symlink to avoid iCloud path spaces: ln -sfn "long path" /tmp/workdir

# Slide with audio:
ffmpeg -y -loop 1 -i slide.png -i audio.mp3 \
  -c:v libx264 -tune stillimage -pix_fmt yuv420p \
  -c:a aac -ar 44100 -ac 2 -shortest seg.mp4

# Silent slide (N seconds):
ffmpeg -y -loop 1 -i slide.png -f lavfi -i anullsrc=r=44100:cl=stereo \
  -c:v libx264 -tune stillimage -pix_fmt yuv420p \
  -c:a aac -ar 44100 -ac 2 -t N seg.mp4

# Concat (always re-encode, never -c copy):
printf "file 'seg1.mp4'\nfile 'seg2.mp4'\n..." > concat.txt
ffmpeg -y -f concat -safe 0 -i concat.txt \
  -c:v libx264 -pix_fmt yuv420p -c:a aac -ar 44100 -ac 2 final.mp4

All segments MUST share: 44100Hz sample rate, stereo, AAC codec.

PPTX Conversion (if needed)

> Full reference: [references/pptx-conversion.md](references/pptx-conversion.md)

If starting from an existing PPTX, convert slides to PNG images first:

soffice --headless --convert-to pdf --outdir output/ presentation.pptx
pdftoppm -png -r 300 output/presentation.pdf output/slide

NotebookLM — Human Reference Only

**The agent must NOT auto-invoke NotebookLM or use its outputs to drive slide/script decisions.** The human owns the outline, visual arrangement, and deck direction.

**When to recommend**: only when the user says they're unsure what to put on slides or need inspiration.

Gotchas

• **iCloud paths with spaces break ffmpeg** — symlink to `/tmp/`
• **Audio format mismatch breaks concat** — always re-encode with `-ar 44100 -ac 2`
• **ElevenLabs free tier** — `mp3_22050_32` only, 10k chars/month
• **edge-tts needs internet** — falls back to Kokoro if offline
• **Kokoro WAV files are ~7x larger** — convert to MP3 with ffmpeg before video assembly
• **Kokoro first run downloads ~350MB model** — ensure pip is in the venv
• **`/edit` distorts figure** — be more explicit: "Keep the original figure exactly as-is, only add framing"
• **Style drift across slides** — use `/edit` from base slide or prepend shared `deck-style.md`

Dependencies

| Tool | Stage | Install | |------|-------|---------| | Gemini CLI + nanobanana | 2 | `gemini extensions install https://github.com/gemini-cli-extensions/nanobanana` | | LibreOffice + poppler | 2 (PPTX) | `brew install --cask libreoffice && brew install poppler` | | edge-tts | 3 | `pip install edge-tts` | | Kokoro | 3 (offline) | `pip install kokoro soundfile` | | ElevenLabs | 3 (premium) | `pip in