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Generate image-based PowerPoint presentations using gpt-image-2, converting articles, papers, and reports into visual slide decks
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
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| name | codex-ppt-skill |
| description | Generate image-based PowerPoint presentations using gpt-image-2, converting articles, papers, and reports into visual slide decks |
| triggers | ["create a powerpoint presentation from this document","generate slides from this article","make a ppt deck with images","convert this paper to presentation slides","build a slide deck using codex-ppt","generate visual presentation from content","create image-based slides for this report","make a ppt with gpt-image-2"] |
Skill by ara.so — Codex Skills collection.
A skill for generating image-based PowerPoint presentations where each slide is a complete 16:9 image generated by gpt-image-2. Converts articles, papers, reports, and notes into visually cohesive presentation decks with unified styling.
.pptx with speaker notesnpx -y skills@latest add ningzimu/codex-ppt-skill \
--skill codex-ppt \
--agent codex \
--global
Restart Codex after installation.
npx -y skills@latest add ningzimu/codex-ppt-skill \
--skill codex-ppt \
--agent claude-code \
--global
openclaw skills install codex-ppt
npx -y skills@latest add ningzimu/codex-ppt-skill \
--skill codex-ppt \
--agent hermes-agent \
--global
Clone and symlink to your agent's skills directory:
git clone https://github.com/ningzimu/codex-ppt-skill.git
mkdir -p ~/.codex/skills
ln -s $(pwd)/codex-ppt-skill/skills/codex-ppt ~/.codex/skills/codex-ppt
Important: Only configure if you need API/CLI fallback. If using Codex with GPT subscription and built-in image generation works, skip this section.
Configure only when:
python3 ~/.codex/skills/codex-ppt/scripts/codex_ppt_runtime.py config \
--api-key "$OPENAI_API_KEY" \
--model gpt-image-2
With custom base URL (for third-party providers):
python3 ~/.codex/skills/codex-ppt/scripts/codex_ppt_runtime.py config \
--api-key "$OPENAI_API_KEY" \
--base-url "https://api.example.com/v1" \
--model openai/gpt-image-2
Configuration is stored in ~/.codex-ppt-skill/.env and shared across all agents.
If configured, the .env file contains:
OPENAI_API_KEY=your-api-key-here
OPENAI_BASE_URL=https://api.example.com/v1 # optional
OPENAI_IMAGE_MODEL=gpt-image-2
python3 ~/.codex/skills/codex-ppt/scripts/codex_ppt_runtime.py generate \
--prompt "Clean professional slide: Introduction to AI, blue gradient background, large title, 3 bullet points" \
--output /path/to/slide_01.png \
--size 2048x1152
python3 ~/.codex/skills/codex-ppt/scripts/assemble_ppt.py \
--project-dir /path/to/ppt-project \
--title "My Presentation" \
--speech-file /path/to/ppt-project/speech.md
python3 ~/.codex/skills/codex-ppt/scripts/codex_ppt_runtime.py check-config
Output:
✓ Configuration file exists
✓ API key configured
✓ Model: gpt-image-2
✓ Base URL: https://api.openai.com/v1
# In agent conversation:
# "Use codex-ppt skill to create a 10-slide presentation from article.md"
The skill follows this workflow:
outline.md with slide titles and key pointsspeech.md with speaker notes.pptx using local script{base_dir}/{ppt_name}/
├── origin_image/
│ ├── slide_01.png # Title slide
│ ├── slide_02.png # Content slides
│ ├── slide_03.png
│ └── ...
├── outline.md # Slide structure
├── speech.md # Speaker notes (## Slide 1: Title format)
└── {ppt_name}.pptx # Final presentation
# Input: technical_article.md containing AI research summary
# Agent command: "Use codex-ppt skill to make this into slides"
# Step 1: Outline generation
outline = """
# AI Research Presentation Outline
## Slide 1: Title
- "Recent Advances in Large Language Models"
- Speaker name, date
## Slide 2: Background
- Evolution of NLP
- Pre-transformer era vs transformer era
- Key milestones timeline
## Slide 3: Architecture
- Transformer architecture diagram
- Attention mechanism
- Scaling laws
# ... (continues)
"""
# Step 2: Style selection
styles = [
"clean-professional", # Recommended for tech talks
"scientific-defense", # For academic presentations
"handdrawn-technical" # For approachable tech explanation
]
# Step 3: Sample slide generation (2K resolution)
sample_prompt = """
16:9 slide, clean professional style, blue gradient background
Title: "Recent Advances in Large Language Models"
Subtitle: "Dr. Jane Smith | May 2024"
Minimalist design, large readable font, subtle geometric accents
"""
# Step 4: Bulk generation (all remaining slides)
# Agent generates each slide with consistent style but varied layouts
# Outline with custom figure specification
outline_with_figures = """
## Slide 5: Model Architecture
- Insert: /path/to/architecture_diagram.png
- Transformer architecture
- Multi-head attention
- Feed-forward layers
## Slide 8: Experimental Results
- Insert: /path/to/results_chart.png
- Benchmark comparison
- Performance metrics
"""
# The skill will:
# 1. Use provided image as background/focal element
# 2. Add consistent styling (borders, background, text overlays)
# 3. Maintain visual coherence with other slides
#!/usr/bin/env python3
from pptx import Presentation
from pptx.util import Inches
import os
def assemble_presentation(project_dir: str, title: str, speech_file: str):
"""
Assemble PPT from images in origin_image/ directory.
Args:
project_dir: Path to PPT project directory
title: Presentation title
speech_file: Path to speech.md with speaker notes
"""
prs = Presentation()
prs.slide_width = Inches(10) # 16:9 aspect ratio
prs.slide_height = Inches(5.625)
image_dir = os.path.join(project_dir, "origin_image")
images = sorted([f for f in os.listdir(image_dir) if f.startswith("slide_")])
# Parse speaker notes
notes_map = parse_speaker_notes(speech_file)
for idx, img_file in enumerate(images, 1):
slide_layout = prs.slide_layouts[6] # Blank layout
slide = prs.slides.add_slide(slide_layout)
# Add image filling entire slide
img_path = os.path.join(image_dir, img_file)
slide.shapes.add_picture(
img_path,
Inches(0), Inches(0),
width=Inches(10), height=Inches(5.625)
)
# Add speaker notes
if idx in notes_map:
slide.notes_slide.notes_text_frame.text = notes_map[idx]
output_path = os.path.join(project_dir, f"{title}.pptx")
prs.save(output_path)
return output_path
def parse_speaker_notes(speech_file: str) -> dict:
"""Extract speaker notes by slide number from markdown."""
notes = {}
current_slide = None
current_text = []
with open(speech_file, 'r', encoding='utf-8') as f:
for line in f:
if line.startswith("## Slide "):
if current_slide:
notes[current_slide] = "\n".join(current_text).strip()
# Extract slide number
current_slide = int(line.split("Slide ")[1].split(":")[0])
current_text = []
elif current_slide:
current_text.append(line.rstrip())
if current_slide:
notes[current_slide] = "\n".join(current_text).strip()
return notes
Built-in styles in skills/codex-ppt/references/styles.md:
# Input: research_paper.pdf (converted to markdown)
# Command: "Create a 15-slide conference talk using scientific-defense style"
# Agent workflow:
# 1. Extract key sections (abstract, methodology, results, conclusions)
# 2. Create outline with 1 title + 2-3 intro + 6-8 technical + 2-3 conclusion slides
# 3. Insert paper figures on relevant slides
# 4. Generate slides with consistent academic styling
# 5. Add detailed speaker notes from paper content
# Input: Q4_2024_metrics.md with KPIs and charts
# Command: "Make data-dashboard style slides, insert my 3 chart images"
# Outline example:
"""
## Slide 1: Q4 2024 Review
## Slide 2: Revenue Overview
- Insert: revenue_chart.png
## Slide 3: User Growth
- Insert: growth_chart.png
## Slide 4: Regional Performance
- Insert: regional_map.png
## Slide 5: Key Initiatives
## Slide 6: Q1 2025 Goals
"""
# Input: lecture_notes.md (markdown with headings and bullets)
# Command: "Create handdrawn-technical style slides, about 20 pages"
# Agent approach:
# - 1 slide per major concept
# - Visual metaphors for abstract ideas
# - Step-by-step diagrams for processes
# - Summary slide every 5-6 slides
# - Consistent whiteboard aesthetic throughout
# 2048x1152 (16:9, sufficient for most presentations)
python3 codex_ppt_runtime.py generate \
--prompt "..." \
--output slide.png \
--size 2048x1152
# 3840x2160 (16:9, for text-heavy slides or printing)
python3 codex_ppt_runtime.py generate \
--prompt "..." \
--output slide.png \
--size 3840x2160
When to use 4K:
Solution: Increase resolution to 4K
# In agent: "Regenerate slides 3-5 at 4K resolution for better text clarity"
Cause: Vague or varying style prompts
Solution:
# Good: Consistent base prompt
base_style = "Clean professional, blue gradient (#1e3a8a to #3b82f6), Inter font, minimalist"
slide_2_prompt = f"{base_style}\nTitle: Introduction\n3 bullet points..."
slide_3_prompt = f"{base_style}\nTitle: Methods\nDiagram with 4 boxes..."
# Bad: Different styles per slide
slide_2_prompt = "Blue background, title and bullets"
slide_3_prompt = "Professional slide with diagram" # Too vague
Cause: speech.md format doesn't match parser
Solution: Use strict heading format
## Slide 1: Title Slide
This is the opening slide. Introduce yourself and the topic.
## Slide 2: Background
Explain the context. Mention key historical developments.
## Slide 3: Problem Statement
...
Parser expects ## Slide N: Title format exactly.
Check configuration:
python3 codex_ppt_runtime.py check-config
Common fixes:
~/.codex-ppt-skill/.env/v1)curl https://api.openai.com/v1/images/generations \
-H "Authorization: Bearer $OPENAI_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "gpt-image-2",
"prompt": "Test slide",
"size": "2048x1152"
}'
Solution: Explicitly mention the skill
# Weak: "Make slides from this article"
# Strong: "Use codex-ppt skill to generate slides from this article"
Correct outline format:
## Slide 5: Architecture
- Insert: /absolute/path/to/diagram.png
- Model architecture overview
- Key components
Agent should:
# Don't regenerate entire deck
# Command: "Regenerate slide 7 with darker background and larger font"
# Agent should:
# 1. Load slide_07 prompt from history or reconstruct from outline
# 2. Modify only specified parameters
# 3. Overwrite origin_image/slide_07.png
# 4. Re-run assembly script
# Upload reference image
# Command: "Use this slide style for the entire presentation"
# Agent approach:
# 1. Analyze uploaded image (colors, fonts, layout, visual elements)
# 2. Create detailed style description
# 3. Apply to all slide prompts
# 4. Generate sample for confirmation
from pptx import Presentation
import subprocess
def export_formats(pptx_path: str):
"""Export to PDF and images."""
base = os.path.splitext(pptx_path)[0]
# PDF (requires LibreOffice)
subprocess.run([
"libreoffice", "--headless", "--convert-to", "pdf",
"--outdir", os.path.dirname(pptx_path),
pptx_path
])
# Individual PNGs already in origin_image/
print(f"Exported: {base}.pdf")
print(f"Images: {os.path.dirname(pptx_path)}/origin_image/")
# Required only for API/CLI fallback
OPENAI_API_KEY=sk-... # Your OpenAI API key
OPENAI_BASE_URL=https://api.openai.com/v1 # Optional, for third-party providers
OPENAI_IMAGE_MODEL=gpt-image-2 # Model name
Never commit these to version control. Store in ~/.codex-ppt-skill/.env.