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Recreate

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INSANE0777
recreate

Recreate a website from a link — evidence-driven reconstruction with the awwwards MCP. Use when the user says "recreate this site", "clone this page", "rebuild <url>", or gives a URL and asks to build it. Produces a faithful hand-built implementation (HTML/CSS/JS + GSAP/Lenis as the reference demands), QA-verified against the live site, then optionally converted into a Vite/Astro/Next project.

Overview

PublisherINSANE0777
RepositoryAwwwards-mcp
Skill namerecreate
Stars
355
Forks
18
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 INSANE0777 on GitHub. Read the source before you install it.

Installation

Install the Recreate 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.
    https://github.com/INSANE0777/Awwwards-mcp/tree/main/skills/recreate
  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/INSANE0777/Awwwards-mcp.git /tmp/Awwwards-mcp
mkdir -p .claude/skills
cp -r /tmp/Awwwards-mcp/skills/recreate .claude/skills/recreate
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Recreate 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 Recreate 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 Recreate 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.

Recreate a website from a link

Input: one URL ($ARGUMENTS). Follow the phases in order; do not skip the evidence steps.

Phase 0 — Study-build terms (mandatory, every run)

This is a local study build: recreate the reference exactly — layout, text, images, fonts, audio, motion. In exchange, every output MUST ship the disclaimer:

  1. README.md in the build must state, prominently: the design, copy, images, fonts and audio belong to the original owners; this build is a private study/reference recreation; it must not be deployed, published, sold, or presented as your own work; publishing requires replacing every borrowed asset (list them) and obtaining licenses.
  2. Header comment in the HTML repeats the same notice in one line.
  3. Provenance list: every borrowed asset path + source URL enumerated in the README.
  4. If the user later wants to publish, that is a separate brief: replace assets → publish. Never quietly drop the disclaimer.

Scope: whole page or specific sections? A continuous scroll-narrative (fixed stage + long spacer) is one unit — do not force per-section pages onto it. Target: single-file build by default; conversion is offered after QA, never assumed (see Phase 4).

Phase 1 — Evidence (free MCP, all read-only)

Any URL works, but check Awwwards first. Step 1 is always search_sites with the site's domain/name. If the site is listed → get_site_details for its recorded DNA (palette, tech, elements, awards) and carry that into the report. If no match → say so once and continue: only the search/index tools are Awwwards-sourced; every capture tool below runs on any public link, and the live evidence still gives you everything.

  • analyze_stack <url> → framework + CSS + libs (tells you whether the reference is Webflow/Next/static and which motion libs matter).
  • analyze_page_structure <url> → bands (section order + heights). If bands collapse into a giant, capture_component with explicit id/class selectors instead.
  • Per section: capture_component <url> with selector (id/class), maxNodes up to 2000 → geometry, 44 computed style keys, pseudo-elements, inline SVG, text, imgSrc, fonts, colors.
  • Motion: analyze_scroll_map <url> (keyframes + gsapTriggers numerics; scrubProbe on for scrubbed narratives), analyze_hover_census <url> (during-hover activeAnims), get_motion_dna <url>.
  • Canvas/WebGL hero: capture_pose_3d → frames/grid; keep build_pose_stub output as the camera reference.
  • Screenshots: capture_live_site full-page + at each scroll anchor from the band map (this is your QA ground truth, not decoration).
  • Mechanism unknown after all evidence? analyze_bundle <url> (≤40 chunks, 1 req/s, keyword grep, optional webcrack) — understand how, still write your own code.

Phase 2 — Build

  • One HTML file + CSS + JS matching the reference's own approach: vanilla for static sites; GSAP + ScrollTrigger + Lenis when the DNA shows them.
  • Wire motion from the measured values: census durations/easings/triggers, gsapTriggers pixel start/end, keyframe from/to values, scrub curves. Do not invent easings the census didn't show.
  • Continuous-scroll narratives: fixed viewport-size stage + long invisible spacer + one scrubbed master timeline; layer switching and --bg custom property tweens; reduced-motion mode collapses to a calm stacked document (always ship it).
  • Structure: semantic ids per section (#hero, #work), local vendor/ for libs (strict load order: gsap → ScrollTrigger → lenis → app.js), local assets/ for fonts/audio/covers/images — exact assets, copied in.
  • All links target="_blank" rel="noopener", metadata + OG + JSON-LD updated.

Phase 3 — QA gate (do not declare done without it)

  • Playwright: 390, 768, 1024, 1440, 1728 widths; screenshot every scroll anchor (match the reference capture); compare compositions side by side.
  • Zero console errors, zero 404s, zero horizontal overflow; assert element bounds (name width, card count/hrefs, contact block) at 1440 and 390.
  • Reduced-motion pass at 1440: stacked layout, no overflow.
  • If anything drifts from the evidence, fix and re-shoot — one anchor at a time (section-loop discipline).

Phase 4 — Optional project conversion (ask first)

The rebuild is a valid deliverable on its own. After Phase 3 passes, ask the user:

Keep it as a single-file build, or convert it to a project — Vite (plain HTML/CSS/JS, no components), Astro (HTML components), or Next.js (React components)? And if converted, keep the original scripts or switch to authored motion (--authored)?

Do not convert unprompted. Do not pick a framework for them. If they say "just build it", stop at Phase 3 + handoff.

When they choose:

  1. awwwards-build-project <file.html> --target vite|astro|next --out <dir> → native structure: real section components, shell chrome, split CSS (tokens.css + site.css), ordered libs, head metadata/boot replayed.
  2. If they chose authored motion: --authored --motion <census.json> — site logic scripts dropped, head boot + animation libs kept, motion.manifest.json carries measured per-section + page triggers. Then author the motion (see motion-authoring), recording any authored rows explicitly.
  3. Gate: npm install && npm run build per target, serve, re-run Phase 3 anchors plus a 390×844 mobile smoke and reduced-motion; zero errors/404s.

Phase 5 — Handoff

Always deliver a reference report — even if the user only asked for information and no build: detected stack + libs (analyze_stack, get_motion_dna), section map with heights/backgrounds (bands), motion summary (triggers, easings, durations, scrub/pin), asset inventory (fonts, images, audio), and bundle notes when analyze_bundle ran. For non-Awwwards links this report is the value when a build is out of scope.

If a build was produced: README with run instructions, the mandatory study-build disclaimer (copyright belongs to the original owners; no deploy/publish/production use; not presented as your own), full provenance list of borrowed assets, and the replace-before-publish plan if the user later wants to ship. Report: reference URL, evidence tools used, section map, QA matrix results, residual gaps.

Known limits (say them out loud)

  • WebGL/canvas interiors are opacity: reference frames only.
  • Webflow IX2 / JS-driven initial states are recoverable as behavior evidence but not as their runtime — re-implement with your own JS (N2 authored mode proves the shape; vendor-bundle interpretation stays manual).
  • --authored keeps head inline scripts even if they may be site logic (warned when >2 KB or animation-looking) — verify before shipping.
  • Generator pageTriggers cover unattributed census rows; a page-level hook still needs authoring before it animates.
  • Fonts: only ship if owned → self-host; otherwise match the closest licensed face.

Frequently asked questions

What does the Recreate AI skill do?

Recreate a website from a link — evidence-driven reconstruction with the awwwards MCP. Use when the user says "recreate this site", "clone this page", "rebuild <url>", or gives a URL and asks to build it. Produces a faithful hand-built implementation (HTML/CSS/JS + GSAP/Lenis as the reference demands), QA-verified against the live site, then optionally converted into a Vite/Astro/Next project.

Why use Recreate on TypingMind?

Because you install it once and use it with any model. Recreate 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 Recreate in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/INSANE0777/Awwwards-mcp/tree/main/skills/recreate. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Recreate?

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 Recreate?

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

Is the Recreate AI skill free?

Yes. It is published on GitHub by INSANE0777 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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