What does this skill do, and when should you use it?
This Claude Code skill turns Claude into a full media pipeline built on PixelBin: generate, store (DAM), transform, and deliver images and videos in one flow. It wraps PixelBin's 85+ AI APIs (image generation, video generation, background removal, watermark removal, upscaling, restoration, and more) plus 60+ chainable URL-based image transformations. The skill ships runnable Node.js scripts with concurrency-safe, resumable batch runners. It fits teams that need product imagery processed at scale for e-commerce and marketing.
Accepts images the user pasted, linked, or referenced by local path and saves/uploads them itself; calls pixelbin.predictions.createAndWait to invoke nanoBanana image models and video models such as Sora 2, Veo 3, and Kling 3; uses pixelbin.assets to upload files or URLs and obtain permanent CDN URLs; builds free transformation URLs on cdn.pixelbin.io (e.g. t.resize~t.toFormat~t.compress); runs the scripts under scripts/ (generate-image, generate-video, upload, transform, seo-content, build-page); produces SEO brief. and page-spec. and assembles a landing-page HTML; surfaces pricing upgrade links on quota errors.
- An e-commerce seller with dozens of product photos who needs white-background, 4K, marketplace-sized variants for Amazon/Shopify/Instagram in one prompt
- A content team that wants text-prompt hero images or short videos via Sora 2/Veo 3 with permanent, ready-to-publish CDN links
- An operations person who needs bulk watermark or background removal without touching images one by one
- A site builder who wants SEO content and an AI-illustrated landing page generated from a keyword plus a brand reference site
- A developer who wants to call the bundled scripts/CLI directly for batch jobs of 500–10,000 images
How do you install this skill?
- Default public-read publishes uploaded assets; switch to private for sensitive images
- Core function depends entirely on overseas pixelbin.io services; mainland-China reachability and latency undisclosed
- All effectiveness claims are static documentation, unverified; no tests or CI evidence
- Unverified publisher with no maintenance-ownership or update-path commitment
- Quantitative claims (85+ APIs, 0 vulnerabilities) are author statements, independently unverified
- Shell / CLI
- Network access
- Local filesystem
Node.js@pixelbin/admindotenvPixelBin API token and cloud name (.env)
1) git clone https://github.com/anandpareek-hub/pixelbin-claude-skill.git and cd into the folder; 2) npm install; 3) cp .env.example .env and fill in PIXELBIN_API_TOKEN and PIXELBIN_CLOUD_NAME (both from console.pixelbin.io). Load it one of three ways: A) via Claude Code plugin marketplace — /plugin marketplace add anandpareek-hub/pixelbin-claude-skill then /plugin install pixelbin@pixelbin-claude-skill; B) manual symlink — ln -s "$(pwd)/skills/pixelbin" ~/.claude/skills/pixelbin; C) run scripts directly with node scripts/xxx.js, no Claude needed. The README mentions npx usage once published to npm, but publication status is not evidenced in the source.
tmp="$(mktemp -d)"
git clone --depth 1 https://github.com/anandpareek-hub/pixelbin-claude-skill.git "$tmp"
mkdir -p ~/.claude/skills
cp -R "$tmp/skills/pixelbin" ~/.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:
- I have 12 product photos in ./products/. Generate Amazon-, Shopify-, and Instagram-ready versions for each: white background, 4K upscale, square crop for marketplaces, 9:16 for Instagram Reels. Output a JSON of CDN URLs.
After setup, just chat in plain English, e.g.: "I have 12 product photos in ./products/. Generate Amazon-, Shopify-, and Instagram-ready versions for each: white background, 4K upscale, square crop for marketplaces, 9:16 for Instagram Reels. Output a JSON of CDN URLs." The skill orchestrates upload → bg-remove → upscale → multi-aspect transforms → CDN URLs. You can also invoke explicitly with /pixelbin; when running scripts directly, pass job files from examples/.
What are this skill's strengths and limitations?
- Full pipeline coverage — generate, store (DAM), transform, deliver — not just one-off API calls
- Explicitly cost-aware design: prefers free URL transforms and reserves expensive generation for when it's actually needed, with a clear decision tree
- Batch runners are concurrency-safe and resumable, suitable for hundreds to tens of thousands of images
- Thoughtful handling of user-supplied images (pasted, URL, local path) and a chat-first UX that hides CLI details by default
- MIT licensed; scripts work standalone without Claude
- Hard dependency on the commercial PixelBin service; generation and AI ops consume metered credits, and video generation is the most expensive operation
- Some AI plugin transforms (e.g. plugin-based erase_bg) require manual activation in the PixelBin console, otherwise the skill must fall back to the predictions API
- Repository has no topics set, and the source provides no test suite or third-party validation
- SEO/landing-page features require a brand reference site or design files; Claude must ask rather than guess the design
- AI prediction outputs are temp URLs (~30-day retention) and must be re-uploaded to the DAM to persist
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 |
|---|---|---|---|---|
| PixelBin Media Pipeline Skill this page | 50 · Use with care | ★ 16 | 5mo ago | MIT |
| deAPI AI Media Skill | 45 · Not recommended | ★ 21 | 3mo ago | MIT |
| 9Router Image Generation Skill | 52 · Use with care | ★ 31k | 17d ago | MIT |
| Azure OpenAI for .NET ✓ Microsoft · Official | 47 · Use with care | ★ 2.7k | 3mo ago | MIT |
| VideoDB Video Perception Skill | 57 · Use with care | ★ 129 | 1mo ago | MIT |
The README distinguishes it from generic AI-media skills (single one-off API calls, no CDN, no bulk pipelines): PixelBin provides a full generate → store → transform → deliver stack, free URL-param transforms, and a built-in CDN plus DAM.
How did FollowSkills review this skill?
Credentials via .env, explicit prohibition on exposing tokens, a 'what NOT to do' list (no scraping, no real-person generation without consent), and confirmation before costly ops. But: assets default to access:'public-read', environments are 'auto-fixed silently', uploads go to a third-party cloud with no rollback/deletion guidance, publisher identity unverified, and data-flow disclosure (images leaving the machine into PixelBin cloud) is limited.
Docs are self-consistent with an error-handling table, retry/concurrency conventions, a cost decision tree, and resumable batch conventions; but scripts/references are not in evidence, there are no test suites or CI evidence, key paths are claimed not reproduced, and failure-feedback quality on abnormal input is unverifiable.
Trigger conditions are clear (keywords, scenario list), boundaries declared, cost-path logic sound; but core function depends entirely on overseas services (pixelbin.io, console/cdn.pixelbin.io) whose reachability from mainland China is questionable, and there is no Chinese-language support disclosure — deducted here.
Well-layered docs (SKILL/INTRO/references/examples), MIT license, .env.example, pinned dependency overrides; but no changelog/versioning, no maintenance-ownership or update-path statement, unverified publisher, and the npm CLI is promised rather than published ('Once published'). Governance is incomplete.
The declared generate→store→transform→deliver workflow is coherent with genuinely useful cost-optimization guidance; but representative outputs are marketing images and sample URLs only, with no independently verifiable end-to-end proof that output is directly usable, and comparative-benefit claims ('zero product drift') are author assertions.
Only in-repo primary material (SDK usage, transform syntax, license); no tests, no CI-run evidence, no third-party verification, and quantitative claims (85+ APIs, 0 vulnerabilities, 17-image showcase) cannot be confirmed in a static read.
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 →