GLM-V-PDF-to-PPT

Convert a PDF (research paper, report, or any document) into a polished multi-slide HTML presentation with a structured outline JSON and summary markdown. Trigger this skill when the user mentions making slides or a PPT from a PDF — in Chinese or English.

Z.ai@zai-org

Install

openclaw skills install @zai-org/glmv-pdf-to-ppt

PDF → HTML PPT Skill

Convert any PDF into a multi-slide HTML presentation. Pages are converted to images at DPI 120, read sequentially to understand the content, then a structured outline.json is saved, images are cropped locally (no cloud upload), slides are rendered one by one, and finally a summary.md is generated.

Scripts are in: {SKILL_DIR}/scripts/

Dependencies

Python packages (install once):

bash

pip install pymupdf pillow

System tools: curl (pre-installed on macOS/Linux).

When to Use

Trigger when the user asks to make slides or a presentation from a PDF — phrases like: "make a PPT from a PDF", "convert PDF to slides", "create a presentation from this paper", "根据pdf做ppt", "根据论文做幻灯片", "做PPT", "做幻灯片", "生成演示文稿", "把这个pdf转成ppt", or any similar intent in Chinese or English.

Output Directory Convention

All output goes under {WORKSPACE}/ppt/<pdf_stem>_<timestamp>/:

text

ppt/
└── <pdf_stem>_<timestamp>/
    ├── outline.json        ← structured slide plan (SlidesPlan schema)
    ├── crops/              ← locally-saved cropped images
    │   ├── slide3_method_crop.png
    │   └── slide5_results_crop.png
    ├── slide_01.html
    ├── slide_02.html
    ├── ...
    └── summary.md          ← final summary document

<pdf_stem> = PDF filename without extension
<timestamp> = format YYYYMMDD_HHMMSS (e.g. 20240119_143022)
Cropped images go in crops/ subfolder
Each slide HTML references images via relative path crops/<name>.png

Input

$ARGUMENTS is the path to the PDF file (local) or an HTTP/HTTPS URL.

If user provides a URL: download with curl first, then convert
If user provides a local PDF path: convert directly

Workflow

Phase 0 — Create Output Directory

Compute the output path:

python

import os, datetime
pdf_stem = os.path.splitext(os.path.basename(pdf_path))[0]
timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
out_dir = os.path.join(workspace, "ppt", f"{pdf_stem}_{timestamp}")

Create it immediately:

bash

mkdir -p "<out_dir>/crops"

Record out_dir — use it for all subsequent phases.

Phase 1 — Convert PDF Pages to Images (DPI 120)

If the input is a URL, download it first:

bash

pdf_stem=$(basename "$ARGUMENTS" .pdf)
curl -L -o "/tmp/${pdf_stem}.pdf" "$ARGUMENTS"

Then convert (pass either the downloaded path or the original local path):

bash

python {SKILL_DIR}/scripts/pdf_to_images.py "<pdf_path>" --dpi 120

Outputs JSON to stdout:

json

[{"page": 1, "path": "/abs/path/page_001.png"}, ...]

Parse and store the full page → path map. These local paths are used for viewing pages and as --path input to crop.py.

Phase 2 — Read All Pages in Order

View all page images sequentially before planning anything. Your goal here is pure understanding — absorb the full structure, content, figures, and arguments of the document.

While reading, note:

What figures, charts, or tables appear on which pages
The overall arc (intro → method → results → conclusion for papers; or logical structure for other doc types)
Candidate visuals worth cropping for slides (page number + rough region)

Do NOT plan or write slides yet — just read and understand all pages first.

Phase 3 — Plan Outline & Save outline.json

After reading all pages, plan 8–15 slides (adapt freely for non-academic documents).

Slide	Typical purpose
1	Title, authors, affiliation, venue/year
2	Motivation / Problem statement
3	Related Work (brief)
4–N-2	Method / Core contributions (one concept per slide)
N-1	Results & Experiments
N	Conclusion & Future Work

For each slide that needs a visual, identify:

Which page it comes from (the local page path from Phase 1)
A description of what the visual shows and why it belongs on this slide

Save the outline as <out_dir>/outline.json using exactly this schema:

json

{
  "presentation_title": "Paper Title Here",
  "lang": "Chinese",
  "total_slides": 10,
  "slides_plan": [
    {
      "slide_index": 1,
      "title": "Slide Title",
      "main_content": "Key points and text content for this slide",
      "template_id": null,
      "required_crops": [
        {
          "url": "<page_image_url_from_phase1>",
          "visual_description": "Figure 3: architecture diagram showing encoder-decoder",
          "usage_reason": "Illustrates the core model structure for slide 4"
        }
      ]
    }
  ]
}

Field notes:

lang: "Chinese" or "English" — match the PDF language
template_id: always null
required_crops: empty array [] if this slide needs no images
url in each crop: the local file path of the source page image (from Phase 1 path field) — this is what crop.py will open and crop from
visual_description: what the visual shows, including figure/table number if available
usage_reason: why this visual belongs on this particular slide
For images that need cropping, note the approximate region — exact crop boxes are determined in Phase 4

Write outline.json using the Write tool to <out_dir>/outline.json.

Phase 4 — Crop Required Images (Grounding + Subagent)

IMPORTANT: You MUST delegate ALL cropping to a clean subagent using the Agent tool. By this phase your context is very long (all page images + outline), which degrades visual coordinate accuracy. A fresh subagent with only the target image produces much more precise coordinates.

IMPORTANT: You MUST use the provided {SKILL_DIR}/scripts/crop.py script for ALL image cropping. Do NOT write your own cropping code, do NOT use PIL/Pillow directly, do NOT use any other method.

Read outline.json. Collect all crops needed, then launch one subagent per source page (or one per crop if pages differ). The subagent uses grounding-style localization — it views the image, locates the target element, and outputs a precise bounding box in normalized 0–999 coordinates.

Use the Agent tool like this:

text

Agent tool call:
  description: "Grounding crop page N"
  prompt: |
    You are a visual grounding and cropping assistant. Your task is to precisely
    locate specified visual elements in a page image and crop them out.

    ## Grounding method

    Use visual grounding to locate each target:
    1. Read the source image using the Read tool to view it
    2. Identify the target element described below
    3. Determine its bounding box as normalized coordinates in the 0–999 range:
       - 0 = left/top edge of the image
       - 999 = right/bottom edge of the image
       - These are thousandths, NOT pixels, NOT percentages (0–100)
       - Format: [x1, y1, x2, y2] where (x1,y1) is top-left, (x2,y2) is bottom-right
       - Example: [0, 0, 500, 500] = top-left quarter of the image
    4. Be precise: tightly bound the target element with a small margin (~10–20 units)
       around it. Do NOT crop too wide or too narrow.

    ## Source image
    <page_image_path>

    ## Crops needed

    For each crop below, first do grounding (locate the element), then crop:

    1. Name: "slide<N>_<descriptive_name>"
       Target: "<visual_description from outline.json>"
       Context: "<usage_reason from outline.json>"

    ## Crop command

    After determining the bounding box [X1, Y1, X2, Y2] for each target, run:
    ```bash
    python <SKILL_DIR>/scripts/crop.py \
        --path "<page_image_path>" \
        --box X1 Y1 X2 Y2 \
        --name "<crop_name>" \
        --out-dir "<out_dir>/crops"
    ```

    ## Verification

    After each crop, READ the output image to visually verify the correct region
    was captured. If the crop missed the target or is too wide/narrow, adjust the
    coordinates and re-run crop.py.

    ## Output

    Report the final results as a list:
    - crop_name: <name>, file: <output_filename>, box: [X1, Y1, X2, Y2]

Replace <page_image_path>, <SKILL_DIR>, <out_dir>, and crop details with actual values from your context.

The crop.py script outputs JSON: {"path": "/abs/path/slide3_method_crop.png"}

Collect results from all subagents and build the mapping: slide_index → [crop filename, ...] to reference in HTML. The filename will be <name>_crop.png.

Launch subagents for independent pages in parallel when possible. Wait for all to complete before proceeding.

Phase 5 — Measure Cropped Image Dimensions

After cropping, get pixel dimensions:

bash

python3 -c "
from PIL import Image; import os, json
d = '<out_dir>/crops'
sizes = {}
for f in sorted(os.listdir(d)):
    if f.endswith('.png'):
        w, h = Image.open(os.path.join(d, f)).size
        sizes[f] = {'width': w, 'height': h, 'aspect': round(w/h, 2)}
print(json.dumps(sizes, indent=2))
"

Use aspect ratios to pick each slide's layout:

Aspect ratio	Layout recommendation
< 0.7 (tall/narrow)	`text + image` side-by-side — `max-height: 600px` on image
0.7 – 1.3 (square-ish)	`text + image` — image takes ~50% width
> 1.3 (wide)	Image on top or bottom, text above/below
> 2.0 (very wide, e.g. tables)	`full-image` — spans full 1280px width, caption below

Phase 6 — Generate Slides One by One

For each slide, write the HTML, save it to a temp file, then call generate_slide.py.

Step A — Write HTML to /tmp/slide_N.html

All <img src="..."> must use relative paths: crops/<name>_crop.png
Do NOT use absolute paths or URLs for cropped images
Navigation is click-area based — no buttons needed:
- Clicking the left half of the slide navigates to the previous slide
- Clicking the right half of the slide navigates to the next slide
- On slide 1, left click does nothing; on the last slide, right click does nothing
- Keyboard ← / → arrows also navigate
- Implement with two transparent <div> overlays covering each half, positioned absolute over the slide canvas

Step B — Save slide:

bash

python {SKILL_DIR}/scripts/generate_slide.py \
    --html-file /tmp/slide_N.html \
    --index N \
    --total <total> \
    --title "<presentation title>" \
    --out-dir "<out_dir>/"

Repeat until all slides are saved.

Phase 7 — Generate summary.md

Write <out_dir>/summary.md in the same language as the slides (lang from outline.json).

Include:

Document title and basic info (authors, venue, year if applicable)
Brief abstract/overview (2–3 sentences)
Per-slide breakdown table: slide number, title, 1–2 sentence summary
Main contributions or takeaways (bullet list)
Link to slide_01.html to open the first slide

Example structure:

markdown

# [Presentation Title]

> **来源 / Source:** [PDF filename] | **语言 / Language:** Chinese | **幻灯片数 / Slides:** 10

## 摘要
[2-3 sentence overview]

## 幻灯片概览
| # | 标题 | 主要内容 |
|---|------|---------|
| 1 | 标题页 | ... |
...

## 主要贡献
- ...

## 📂 打开演示文稿
[▶ 开始播放](slide_01.html)

HTML Slide Spec

Each slide is a standalone HTML file — full <html>…</html> with embedded CSS only.

Canvas: fixed 1280 × 720 px, overflow: hidden — nothing scrolls.

Consistent design across all slides:

Choose a visual style that fits the document's domain and tone — no fixed palette or font required
If the user specifies a style, follow it exactly; otherwise infer from the content (e.g. a ML paper → clean modern; a historical report → editorial serif; a product pitch → bold and branded)
Same fonts, colors, and spacing system applied uniformly to every slide
Every slide shows: slide title, page counter (bottom-right corner), presentation title (subtle footer)

Navigation on each slide:

Two transparent click areas cover the full slide height: left 50% → previous slide, right 50% → next slide
On slide 1 the left area is inert; on the last slide the right area is inert
Keyboard ← / → arrows also navigate
No visible buttons needed — optionally show a subtle ‹ / › hint at the edges that fades in on hover

Layout patterns:

title-card — centered hero, large title, authors/venue below
text-only — structured bullet points, max 5–6 items, generous whitespace
text + image — image right or left, text opposite
full-image — image fills canvas, minimal text overlay
grid — 2×2 or 3-column figures with captions

Images:

Use relative paths: crops/<name>_crop.png
Add style="object-fit: contain; max-width: 100%; max-height: 100%;"
Add captions below in small italic text

Do NOT:

Use external JS frameworks or icon CDNs
Use placeholder/stock images — only the cropped PDFs
Generate generic purple-gradient-on-white slides
Let content overflow the 720px height

Quality Checklist

Output directory named <pdf_stem>_<timestamp>/
outline.json saved with valid SlidesPlan schema
All crops saved to crops/ (local only, no cloud upload)
Each slide fits within 1280×720, nothing overflows
Consistent theme across all slides
Crop images referenced via relative path crops/<name>_crop.png
Slide number and presentation title visible on every slide
Left/right click-area navigation works, keyboard arrows work
summary.md written in the correct language, links to slide_01.html

Language

Match the PDF language. Chinese PDF → Chinese slides and summary. English → English. No mixing.

GLM-V-PDF-to-PPT

Install

PDF → HTML PPT Skill

Dependencies

When to Use

Output Directory Convention

Input

Workflow

Phase 0 — Create Output Directory

Phase 1 — Convert PDF Pages to Images (DPI 120)

Phase 2 — Read All Pages in Order

Phase 3 — Plan Outline & Save outline.json

Phase 4 — Crop Required Images (Grounding + Subagent)

Phase 5 — Measure Cropped Image Dimensions

Phase 6 — Generate Slides One by One

Phase 7 — Generate summary.md

HTML Slide Spec

Quality Checklist

Language

Related skills