You have a set of PPT slide images (screenshots, exported PNGs, or AI-generated mockups) and you want them back as an editable .pptx file — not a deck of full-slide screenshots pasted as backgrounds.

Existing approaches fail in predictable ways:

This skill teaches Codex (or any compatible AI coding agent) to decompose each slide image into three layers, then rebuild them as a real PowerPoint file:

The skill runs in three phases, each in its own context window for maximum quality:

┌──────────────────┐     ┌──────────────────┐     ┌──────────────────┐
│   Phase 1        │     │   Phase 2        │     │   Phase 3        │
│                  │     │                  │     │                  │
│  Pixel-Level     │────▶│  Visual Asset    │────▶│  PPT Assembly    │
│  Analysis        │     │  Generation      │     │  & Validation    │
│                  │     │                  │     │                  │
│  • Inspect each  │     │  • Generate PNG  │     │  • Build PPTX    │
│    slide image   │     │    for each      │     │    with native   │
│  • Classify      │     │    Layer A       │     │    shapes & text │
│    every element │     │    element       │     │  • Render &      │
│  • Map positions │     │  • No text in    │     │    compare with  │
│    & layers      │     │    any image     │     │    source        │
│  • Self-check    │     │  • Verify each   │     │  • 5 slides per  │
│    for missed    │     │    asset         │     │    batch         │
│    elements      │     │                  │     │                  │
└──────────────────┘     └──────────────────┘     └──────────────────┘

This classification step is the heart of the reconstruction process: each visible element is assigned to visual assets, native PPT structure, or editable text before the deck is rebuilt.

The agent inspects each source image and catalogs every visible element with its type, position (bounding box), layer classification, and implementation method. A completeness self-check (Step 1.4) ensures small icons, in-card illustrations, and decorative details are not missed.

Output: _analysis.md — a structured element inventory for all slides.

For each Layer A element, the agent generates a clean PNG using $imagegen with precise prompts that specify content, style, colors, aspect ratio, and transparency — and always end with "No text, no labels, no numbers."

Output: assets/ folder with all generated PNGs + _phase2_assets.md report.

The image below shows Phase 2 in practice: the agent turns identified Layer A elements into clean visual assets before the final PowerPoint assembly.

The agent builds the PPTX using @presentations (PptxGenJS), stacking elements in correct z-order: background → visual assets → structural shapes → text boxes → brand elements. Slides are built in batches of 5 with rendering and self-validation after each batch.

Output: Final .pptx file + rendered previews + validation_report.md.

Copy the skill folder into your Codex skills directory:

# Clone or download
git clone https://github.com/YOUR_USERNAME/slide-image-to-editable-pptx.git

# Copy to Codex skills directory
cp -r slide-image-to-editable-pptx ~/.codex/skills/

Or install via CC Switch: Skills panel → Add from GitHub → paste the repo URL.

Recommended: 3-prompt workflow (best quality, each phase gets full context window)

Prompt 1 — Analysis:

Please use the $slide-image-to-editable-pptx skill to convert the slide images 
in this folder into an editable PPTX. Visual fidelity must match the source images. 
All text must be editable. Complex visuals use $imagegen to generate clean PNGs. 
Strictly follow Phase 1 first — complete the analysis including Step 1.4 
completeness self-check (ensure no small icons, in-card illustrations, or 
decorative details are missed). Output the full element inventory for my review 
before proceeding to Phase 2.

Prompt 2 — Asset Generation:

Proceed to Phase 2. Output the asset report for my review. 
Do not proceed to Phase 3 until I confirm.

Prompt 3 — PPT Assembly:

Proceed to Phase 3. Use @presentations to build the PPTX. 
The PNG assets in assets/ are ready. Follow the element inventory in _analysis.md 
to reconstruct each slide — match the source images' exact positions, sizes, 
and element density. Build slides 1-5 first, render screenshots for my review, 
then continue with 6-10, 11-15, etc.

your-project/
├── source_slides/           # Your input slide images
│   ├── slide_01.png
│   ├── slide_02.png
│   └── ...
├── assets/                  # Generated PNG visual assets (Phase 2 output)
├── output/                  # Final PPTX (Phase 3 output)
├── _analysis.md             # Element inventory (Phase 1 output)
├── _phase2_assets.md        # Asset generation report (Phase 2 output)
└── validation_report.md     # Quality report (Phase 4 output)

AI coding agents have limited context windows (~200K tokens). Running all phases in one shot means each phase gets only a fraction of the available context. Splitting into three phases ensures:

• Phase 1 uses full context for thorough analysis
• Phase 2 uses full context for high-quality image generation
• Phase 3 uses full context for precise PPT code

Full-slide screenshots give you zero editability. The whole point is to make text editable and shapes adjustable while preserving the visual appearance.

Cropping from slide screenshots produces low-resolution, dirty-edged fragments. AI-generated images are clean, high-resolution, and properly transparent — resulting in a much more professional output.

Some visual elements (radar scenes, scientific illustrations, campus sketches, decorative textures) are too complex for PPT shapes. Forcing them into shapes produces ugly, crude approximations. AI-generated PNGs preserve visual fidelity.

These screenshots show the kind of slide-image reconstruction workflow this skill is designed for: source slides are analyzed visually, rebuilt as editable PowerPoint objects, and checked against rendered previews.

Image Version	Editable Version

Image Version	Editable Version

Issues, suggestions, and PRs are welcome!

If you've used this skill to convert slides and want to share before/after examples, please open a PR — real-world examples help other users understand what to expect.

MIT

• PptxGenJS — The JavaScript library that makes programmatic PPTX creation possible
• OpenAI Codex — The AI coding agent platform
• CC Switch — Inspiration for project documentation structure