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Slides Grab Image

OrganizationPopular
NomaDamas
slides-grab-image

Image-native presentation pipeline usable in Codex and Claude Code. Generate whole-slide raster images one slide at a time with slides-grab image, passing the reference template page as --reference so the model copies the layout and style and only swaps the content. Use when visual fidelity to an existing template matters more than editable text.

Overview

PublisherNomaDamas
Repositoryslides-grab
Skill nameslides-grab-image
Stars
1.2K
Forks
135
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by NomaDamas on GitHub. Read the source before you install it.

Installation

Install the Slides Grab Image AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/NomaDamas/slides-grab.git /tmp/slides-grab
mkdir -p .claude/skills
cp -r /tmp/slides-grab/skills/slides-grab-image .claude/skills/slides-grab-image
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Slides Grab Image in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Slides Grab Image on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Slides Grab Image is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

slides-grab Image Skill

Generate whole-slide raster images where the generated PNG is the slide. Each slide is generated individually with slides-grab image, passing the matching reference template page image(s) via --reference so the model reproduces the template's layout, colors, fonts, and density — only swapping in the new slide's content. Use this pipeline when the user wants to match an existing corporate template, filled deck, or brand form visually.

Mode signal

The plan stage records mode: image-native in slide-outline.md. The user typically provides a reference template (PPTX, PDF, HTML examples) and says "make slides in this form."

Core rule: reference-guided per-slide generation

  • Generate one slide at a time with slides-grab image. Do NOT use slides-grab generate-images (the batch command auto-assembles garbage prompts from markdown and produces inconsistent output).
  • Always pass at least one reference template page image via --reference <path> (repeatable). The reference page(s) must be the same slide-type (cover, content, closing) from the source template. The model must copy the reference's layout, color bands, typography hierarchy, whitespace, and density — only the text content and any embedded photos change.
  • The prompt must explicitly instruct: "Follow the reference image's layout, composition, color palette, and typography hierarchy exactly. Keep the same band positions, spacing, and visual rhythm. Only replace the text content and photos with the new slide's content below."
  • Pass any real photos/assets to embed as additional --reference images so the model can place them into the template layout.

Prompt structure (use this pattern)

slides-grab image \
  --prompt "Follow the reference image layout exactly — same color bands, same typography hierarchy, same spacing and density. This is a [cover/content/closing] slide. Replace the text with: Title: '<new title>'. Body: '<new body text>'. Bullets: <bullet list>. Keep the template's visual style, fonts, and color palette. Render all text sharp and legible. 16:9, 720pt × 405pt." \
  --slides-dir <path> \
  --reference <template-page-cover.png> \
  --reference <real-photo-to-embed.png>

Key prompt principles:

  • Describe the content to inject, not the visual design — the reference image defines the design.
  • Name the slide type (cover, content, closing, stat) so the model picks the right reference density.
  • List the exact title, body, and bullets as literal strings the model should render.
  • If embedding real photos, name them in the prompt ("place photo-1 in the left grid cell") and pass them as --reference.
  • Require sharp, legible, correctly spelled text.

Workflow

Stage 1 — Plan (template-driven dense outline)

Use the installed slides-grab-plan skill with image-native outline density rules.

  1. Take topic, audience, and tone.
  2. If the user provides a reference template/PDF/PPTX, import it with slides-grab import-template --input <path>. Also render the template pages to PNG (e.g. pdftoppm -png -r 150 <input>.pdf <prefix>) and store under <slides-dir>/.slides-grab/template-previews/. These page renders are the --reference images for Stage 2.
  3. Read the template pages in order (page-01, page-02, ...). For each page, identify its layout type and information density — how many text blocks, data points, bullets, captions, and visual elements it carries. This is the critical step: the outline must match the template's per-slide density, not write a thin HTML-style summary.
  4. Write a dense outline: one outline slide per template page, carrying the full content the user wants — titles, subtitles, body paragraphs, full bullet lists, stat values, table data, captions, and image placement notes — at the volume the template page demonstrates. Image-native slides are single rasters with no HTML structure limits, so they hold more text and data per slide than semantic HTML. Do not under-fill.
  5. Tag each outline slide with template-page: NN so Stage 2 knows which reference image to pass.
  6. Record mode: image-native and style: template-pack in slide-outline.md meta.
  7. Present outline, revise until approved.

Stage 2 — Design (per-slide image generation)

  1. Read approved slide-outline.md. For each slide, read its template-page: NN tag and pick that exact template page render from .slides-grab/template-previews/page-NN.png as the primary --reference. If the slide has no tag, pick 1–2 layout-similar pages by slide type (cover, content, stat, closing).
  2. For each slide, run slides-grab image with:
    • --prompt containing ALL the content from the outline for that slide — the full title, subtitle, body paragraphs, every bullet, every stat value, every caption — not a summary. Precede the content with "Follow the reference image layout exactly — same color bands, same typography hierarchy, same spacing and density. Only replace the text content with the content below."
    • --reference pointing to the matching template page render(s)
    • --reference for any real photos to embed (member photos, product shots, logos)
    • --slides-dir <path> and --name slide-XX (so the output is assets/slide-XX.png)
    • --image-native so the command also writes the editor-compatible slide-XX.html wrapper and .slides-grab/image-native/slide-XX.json regeneration metadata
    • --provider codex (default; reuses ~/.codex/auth.json, run codex login once, no API key). Alternatives: --provider openai (OPENAI_API_KEY, gpt-image-2), --provider nano-banana (GOOGLE_API_KEY/GEMINI_API_KEY).
  3. Confirm each command reports Created image-native slide wrapper: slide-XX.html. Do not hand-write the wrapper; --image-native keeps the PNG, wrapper, and regeneration metadata contract aligned for slides-grab edit-image.
  4. Run slides-grab validate --slides-dir <path>. Auto-fix failures until it passes.
  5. Run the design gate (../slides-grab-design/references/design-gate.md): capture PNG evidence, run Pass A + Pass B, synthesize verdict. Resolve all Critical findings, re-generate the failing slide, re-render, re-review until Proceed. Record with slides-grab design-gate --slides-dir <path> --verdict proceed --pass-a-report <a.md> --pass-b-report <b.md>.
  6. For revisions, regenerate the specific slide with slides-grab image (same --reference, adjusted --prompt), or launch the image-native editor with slides-grab edit-image --slides-dir <path>.

Stage 3 — Export

Use the installed slides-grab-export skill. Requires a fresh Proceed gate receipt.

  1. Widescreen → slides-grab pdf --slides-dir <path> --output <name>.pdf.
  2. Per-slide PNG → slides-grab png --slides-dir <path> --output-dir <path>/out-png --resolution 2160p.
  3. PPTX/Figma (experimental / unstable) → slides-grab convert / slides-grab figma.

Rules

  • Keep slide size 720pt × 405pt (16:9).
  • Always pass --reference template page images — never generate image-native slides from a text-only prompt. The reference defines the visual design; the prompt defines the content.
  • Generate one slide at a time with slides-grab image. Do not use slides-grab generate-images.
  • slides-grab image --image-native --name slide-XX writes the wrapper and regeneration metadata. Do not hand-write semantic text into wrapper slides.
  • Put generated assets under <slides-dir>/assets/, reference as ./assets/<file>.
  • Allow data: URLs when a slide must be self-contained; never leave remote http(s):// image URLs in saved HTML.
  • Do not present slides for review until slides-grab validate passes.
  • Do not advance to export while any Critical design-gate finding is unresolved.

Reference

  • ../slides-grab-plan/SKILL.md and ../slides-grab-plan/references/
  • ../slides-grab-design/SKILL.md and ../slides-grab-design/references/
  • ../slides-grab-export/SKILL.md and ../slides-grab-export/references/

Frequently asked questions

What does the Slides Grab Image AI skill do?

Image-native presentation pipeline usable in Codex and Claude Code. Generate whole-slide raster images one slide at a time with slides-grab image, passing the reference template page as --reference so the model copies the layout and style and only swaps the content. Use when visual fidelity to an existing template matters more than editable text.

Why use Slides Grab Image on TypingMind?

Because you install it once and use it with any model. Slides Grab Image is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Slides Grab Image in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/NomaDamas/slides-grab/tree/main/skills/slides-grab-image. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Slides Grab Image?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Slides Grab Image?

As many as you like. As long as a model supports skills, you can use Slides Grab Image with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Slides Grab Image AI skill free?

Yes. It is published on GitHub by NomaDamas under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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