academic-presentations

name	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.

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

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 Batch scripts: scripts/batch_tts_edge.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

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 install elevenlabs + ELEVENLABS_API_KEY
ffmpeg	4	brew install ffmpeg