What does this skill do, and when should you use it?
slides-grab-image is one of 7 skills bundled in the slides-grab monorepo, located at skills/slides-grab-image/SKILL.md, and implements the image-native presentation pipeline. Instead of generating editable HTML slides, the generated PNG itself is the slide: each page is produced with slides-grab image, passing a matching template page via --reference so the model copies the template's layout, color bands, typography hierarchy, and density, swapping only text and photos. The workflow has three stages: a slides-grab-plan outline matched to the template's per-slide density, per-slide generation gated by a design review, and export via slides-grab-export to PDF or 2160p PNG. It is aimed at cases where visual fidelity matters more than downstream editability.
1) Plan stage: reads the user's reference template (PPTX/PDF/HTML), imports it with slides-grab import-template, and renders template pages to PNG (e.g. pdftoppm) under .slides-grab/template-previews/; it identifies each page's layout type and information density, writes a dense outline with one slide per template page tagged template-page: NN, and records mode: image-native and style: template-pack in slide-outline.md. 2) Design stage: for each slide it runs slides-grab image --image-native --name slide-XX with the matching template page and real photos as --reference inputs; the prompt describes only the content to inject while requiring exact copying of the reference's visual design. The command writes the PNG, a slide-XX.html wrapper, and regeneration metadata under .slides-grab/image-native/. It then runs slides-grab validate and the design gate (PNG evidence, Pass A + Pass B review), regenerating until all Critical findings are resolved and a Proceed receipt is recorded. 3) Export stage: with a fresh Proceed receipt, runs slides-grab pdf or slides-grab png --resolution 2160p (PPTX/Figma conversion is experimental/unstable). Revisions regenerate a specific slide with the same --reference and adjusted --prompt, or open the image-native editor with slides-grab edit-image.
- Marketing or brand teams who hold a corporate PPT template or a filled example deck and need new AI-generated slides to match its layout, colors, and typography hierarchy exactly
- Content creators embedding real product shots or member photos into a fixed brand layout and wanting the model to place them automatically
- High-information-density slide needs (many bullets, stats, table data), since a single raster has no HTML semantic structure limits
- Image-led decks or rapid visual exploration where composition matters more than later text editing
- Team workflows that require per-slide design-gate review (PNG evidence + Pass A/B) before PDF/PNG export
How do you install this skill?
- The default provider reuses local ~/.codex/auth. and sends prompts to a private backend; use --provider openai or nano-banana with your own API key if unacceptable.
- Image-generation quality (text spelling, layout fidelity) is independently unverified; manually review every PNG before use — the README itself admits some backends may be unsupported.
- Core function depends entirely on overseas image-generation APIs; may be unreachable from mainland-China networks without a proxy and offers no offline fallback.
- Dependencies include the pinned god-tibo-imagen package of unclear provenance and audit status; review before installing.
- PPTX/Figma exports are experimental and may require manual cleanup.
- Shell / CLI
- Network access
- Local filesystem
Node.js >= 20slides-grab CLI (npm)Playwright + ChromiumCodex CLI login (~/.codex/auth.) or OPENAI_API_KEY or GOOGLE_API_KEY/GEMINI_API_KEYpdftoppm (for template PDF rendering)
Install the whole slides-grab collection (this skill ships with it): npm install slides-grab && npx playwright install chromium && npx slides-grab install-skills --target all --scope user. Requires Node.js >= 20. The default image provider, codex-imagen, reuses your local Codex login (run codex login once; no separate API key). Optional providers: --provider openai (OPENAI_API_KEY) or --provider nano-banana (GOOGLE_API_KEY/GEMINI_API_KEY). The README does not document installing skills/slides-grab-image in isolation, excluding the sibling skills.
tmp="$(mktemp -d)"
git clone --depth 1 https://github.com/NomaDamas/slides-grab.git "$tmp"
mkdir -p ~/.claude/skills
cp -R "$tmp/skills/slides-grab-image" ~/.claude/skills/
rm -rf "$tmp"Generated from the source repository and skill path; it copies only this skill's folder. If the author's install steps above differ, follow those first. To scope it to one project, replace ~/.claude/skills with that project's .claude/skills.
How do you use this skill?
Once installed, send your agent any of these to trigger it:
- Make a 10-page quarterly product review in the exact visual style of references/acme-qbr.pptx.
- Follow the reference image layout exactly... Replace the text with: Title...
After installing via docs/installation/claude.md or docs/installation/codex.md in Claude Code or Codex, trigger the skill with a prompt that includes the reference template, e.g.: "Make a 10-page quarterly product review in the exact visual style of references/acme-qbr.pptx." The skill renders template pages to PNG and writes an outline (mode: image-native); after you approve it, each slide is generated like: slides-grab image --image-native --name slide-01 --prompt "Follow the reference image layout exactly... Replace the text with: Title..." --slides-dir decks/my-deck --reference <template-page.png> --reference <real-photo.png>. Then run slides-grab validate, clear the design gate, and export with slides-grab pdf or slides-grab png --resolution 2160p. Slides are fixed at 720pt × 405pt (16:9).
What are this skill's strengths and limitations?
- Reference template pages define the visual design, giving fidelity to existing corporate templates and brand layouts that HTML pipelines struggle to match
- A single raster carries more text and data per slide than semantic HTML, with no structural limits
- Default codex-imagen provider reuses the local Codex login — no separate API key required
- --image-native writes the PNG, HTML wrapper, and regeneration metadata together, staying in contract with slides-grab edit-image for revisions
- Enforced validate + design gate (A/B review, Proceed receipt) before export adds a quality process
- Image-native slides are far less editable than HTML: no direct semantic text edits; revisions require slides-grab edit-image or full slide regeneration
- The PNG is the slide — text is not searchable, selectable, or accessible; the README explicitly recommends HTML mode when editability, accessibility, or searchability matters
- codex-imagen calls an unsupported private Codex backend that may break without notice and requires a ChatGPT account entitled to image generation
- Batch generation (generate-images) is forbidden; one-at-a-time generation is slow for large decks
- PPTX and Figma exports are officially experimental/unstable and need manual cleanup
- Export is blocked by the design-gate receipt — skipping review means no pdf/convert/figma
How does this skill compare with similar options?
Side by side with related skills; every score comes from the same FSRS standard.
| Skill | FS score | Stars | Last updated | License |
|---|---|---|---|---|
| Slides Grab Image-Native Skill this page | 53 · Use with care | ★ 1.2k | 1mo ago | MIT |
| Slides Grab HTML Skill | 60 · Recommended | ★ 1.2k | 1mo ago | MIT |
| Slides Grab Design Skill | 51 · Use with care | ★ 1.2k | 1mo ago | MIT |
| baoyu-design: Run Claude Design Locally as an Agent Skill | 53 · Use with care | ★ 4.4k | 1mo ago | MIT |
| Huashu Design | 42 · Not recommended | ★ 25k | 12d ago | MIT |
The source positions slides-grab overall as a "Claude Design Open Source Alternative." Within the repo, this skill's counterpart is the semantic HTML slide pipeline (the HTML mode of sibling skills slides-grab-plan/design/export): HTML mode is editable, searchable, and easy to convert, while image-native prioritizes visual composition over editability. No other specific competitors are named in the source.
How did FollowSkills review this skill?
The skill is pure workflow instruction with no destructive commands; asset rules forbid remote http(s) image URLs, scope work to the slides-dir, and data flow (template PNG → reference → generated PNG/wrapper) is disclosed, with three provider options documented. Deductions: default provider reuses ~/.codex/auth. to send prompts/images to a (per README possibly unsupported) private backend without per-run user confirmation; reliance on the unaudited god-tibo-imagen package; no rollback/cleanup mechanism described.
SKILL.md is self-consistent, explicitly bans the batch command known to yield poor output, and gates on validate and the design gate; the repo contains codex-imagen, image-contract and design-gate tests covering image-generation contract and export gating, with tested error messages (codex login hints). Deductions: static review only; the real success path (OpenAI gpt-image-2 / private backend) cannot be verified statically and may fail on backend unavailability.
Scenario and boundaries are well declared: for 'visual fidelity to a template over editable text', with explicit trade-offs vs HTML mode and experimental PPTX/Figma exports. Deductions: core image generation depends entirely on overseas APIs (OpenAI/Codex private backend/Google) with no mainland-China reachability statement; README warns some paths rely on unsupported private backends; no Chinese-language support noted.
Documentation is well layered: SKILL.md focuses on one pipeline and references plan/design/export sibling skills; repo has MIT license, npm versioning, CI (pages/publish workflows), bilingual README and contribution flow. Deductions: publisher identity (vkehfdl1 / Jeffrey) unknown to the registry; the skill itself has no changelog or known-limitations list, and the pinned god-tibo-imagen dependency has unclear provenance.
The workflow (template pages rendered as references, per-slide generation, design-gate gating) logically targets the 'template fidelity' task and a live showcase exists. Deductions: static review cannot verify the actual usability and text-rendering correctness of generated PNGs (high spelling/layout risk for image models); README implies non-trivial review effort; comparative-benefit evidence is limited.
The repo provides a real test suite (codex-imagen.test.js verifying provider fallback and error messages, design-gate.test.js verifying export gating and template fidelity) plus npm publish/pages CI — auditable primary evidence. Deductions: static cap of 5 applies; no independent third-party execution evidence; image-generation tests use mock providers and do not cover real render quality.
Open a dimension to read why it scored that way
Evidence confidence：Low — Mostly static review, author material or a limited demo; useful for discovery, not high-risk decisions.
See the full review method →