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Fullstack Web Delivery

Community
KarmenLoop
fullstack-web-delivery

Plan and deliver complete websites with adaptive discovery, industry research, client-specific UI/UX, frontend, backend, authentication, persistent data, local preview, and evidence-based acceptance. Use for new sites or substantial redesigns; keep targeted fixes within their existing scope.

Overview

PublisherKarmenLoop
Repositoryfullstack-web-delivery
Skill namefullstack-web-delivery
Stars
5
Forks
0
Bundled files
26
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.

  • 26 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Fullstack Web Delivery 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/KarmenLoop/fullstack-web-delivery/tree/main/skills/fullstack-web-delivery
  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/KarmenLoop/fullstack-web-delivery.git /tmp/fullstack-web-delivery
mkdir -p .claude/skills
cp -r /tmp/fullstack-web-delivery/skills/fullstack-web-delivery .claude/skills/fullstack-web-delivery
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Fullstack Web Delivery 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 Fullstack Web Delivery 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 Fullstack Web Delivery 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.

Fullstack Web Delivery

Turn a project brief into a runnable, reviewable website. Select only the modules the project needs. Communicate in the user's language. Version: 0.9.1.

1. Establish the scope

Read applicable project rules, current source, supplied material, and existing authorization. Reuse answered requirements. Verify available file, terminal, search, browser, screenshot, and service capabilities. The skill adds workflow instructions, not execution privileges.

Distinguish planning, visual prototyping, and complete implementation. Deliver the complete plan before implementation. Continue authorized local development after that delivery unless the user requested a specific approval gate. Follow platform setup for installation and capability limits.

2. Ask only for decisions that are missing

Use the discovery pool. Ask four to six consequential questions per round, within the host tool's limits. Forty questions form a candidate pool; they are not a quota. Skip answered questions. Inspect technical facts yourself. Use reversible, reasoned defaults when authorized; ask for business facts and material preferences.

Begin with who needs to accomplish what and what success looks like. Record user decisions, verified facts, assumptions, and pending dependencies. For consequential layout, navigation or interaction choices, prepare a visual decision before asking: runnable alternatives where possible, otherwise annotated views or a diagram. Pause only the branch that needs a missing decision. Stop general discovery once a complete plan and acceptance scenarios are possible.

3. Research and deliver the complete plan

Read research and client-specific design. Start with the client's industry, audience, and tasks; study relevant mature businesses, then design principles, then GitHub implementation candidates. Record sources, observed scope, adoption, rejection, licenses, and integration cost. Mark unavailable research honestly.

Before implementation, deliver the complete site plan: layer responsibilities, sitemap, page-count basis, menus, page templates, copy logic, company facts, form-to-admin flow, APIs, roles, data, milestones, delivery, and acceptance. Apply content and commercial boundaries when relevant.

Automatically turn the answered brief into the project specification pack in the same handoff. Include a filled UI/UX implementation prompt, page audiences/content/actions, state and motion contracts, relevant engineering/module specifications and requirement-to-acceptance traceability. Use concrete project decisions, not empty templates. The optional Python renderer is a convenience; hosts can write the same documents directly. Preserve authored revisions and mark unexecuted acceptance NOT_RUN.

Map each requirement to a task and a verification method. Define legal business states and access boundaries before interfaces and screens. Order work with business-flow decisions; revise the plan when evidence changes the route.

4. Compare three layouts and resolve the client's design

Read client-specific design and design and motion. Derive composition from the brief, real content, user tasks, and brand constraints. Translate references into one coherent system, with concrete adoption and rejection decisions.

For a new site or substantial visual redesign, follow visual decisions: build three materially different layouts for a representative page and critical interaction using the same realistic content and brand constraints. Open the working comparison in the user's browser and provide identifiable option links, matched desktop/phone views, differences and tradeoffs. Gather selection, combination or revision feedback before expanding the dependent templates, unless the user explicitly delegated the choice. A selected palette alone does not select a layout. Reuse a specifically chosen layout; keep targeted fixes in scope.

Record the actual selection source and update the UI/UX prompt and design contracts. Check visual quality and task usability separately. Verify that promised content is rendered and phone users can discover key states and actions. Record triggers, states, feedback, failure recovery, keyboard/touch behavior, APIs, and motion for each applicable control.

Match typography, nested radii, grouped entrances, heading wrapping and elevation to actual page elements with UI fit and finish. After the brief, generate UI-MATCH-SPEC.md with adopt/defer/omit decisions, task-specific reasons, implementation, states, fallbacks and verification; include it in the UI/UX prompt. For requested reflective cards, read metallic surfaces and choose CSS or a justified GPU route with an explicit environment source, coupled motion and a readable fallback. Specify light direction/intensity, face and bevel highlights, shadow mechanism, and how each responds to pose; distinguish GPU lighting from a CSS shadow approximation. Verify actual stage pixels and context recovery before claiming a working GPU material.

Every enabled control must act and every link must reach a supported destination. Start feedback at input; show success only after the actual operation confirms it. Derive linked responses from one state. Make animation interruptible and retain keyboard, touch, focus and reduced-motion paths. Select advanced patterns by purpose; do not add all six effects to every site. Learn reference principles within their scope: marketing composition, operational usability and a client's existing brand are different constraints.

For scroll effects, video references or coordinated feedback, use motion placement to name the actual driver and assign it to a page/action/task. Persist MOTION-MAP.md with interruption, reduced-motion and verification contracts. Read mechanism recipes for scroll, authenticated morphs, counts, command search, relationship paths, inline layout and appearance. Select relevant effects; preserve source-backed business state and a complete recovery path.

5. Build complete business increments

Read implementation and preview. Preserve an appropriate existing stack. Establish a runnable baseline, then select a flow whose dependencies are satisfied. Prioritize blocking dependencies, business value, and consequential uncertainty. Connect UI, API boundaries, business rules, authorization, storage, and feedback in that increment.

Use real authentication, server-side authorization, and persistent data where required. Commit state and its audit event together. Isolate test accounts and data. Keep mock, sandbox, and real integrations distinct. Preview and verify each flow before expanding the next one.

6. Verify, repair, and hand over

Read delivery. Verify normal, failure, access, data, mobile, and interaction scenarios per flow, then regress the site, production build, and agreed restart persistence. Use the same business identifier to match the receipt, database, admin details, and history.

Measure actual CSS viewport and screenshot scale. Check error, success, reset, and settled-exit focus. No page overflow alone does not establish phone task usability. Preserve failures; repair and recheck affected scope. Browser operation, API checks, screenshots, source review, and independent acceptance are different evidence.

After two attempts without new evidence or a plausible repair, stop blind retries in that branch, retain reproduction and recovery conditions, and continue independent work. For uncertain external side effects, inspect the result before retrying.

Deliver source, startup, preview/admin entry points, plan, API/data notes, credentials retrieval method, acceptance evidence, known limits, and release conditions. Use PASS, FAIL, NOT_RUN, BLOCKED, and NOT_APPLICABLE. Apply the user's existing authorization to publication, external messages, payments, and production changes; prepare reviewable work before any required final approval.

Continuity

Maintain a durable task/decision/evidence record and one accountable integration path. Work sequentially unless the user or applicable instructions authorize delegation. Before resuming, verify current source, process ownership, tools, and authorization. Scheduling requires the user's request and a real scheduling capability.

Use behavior evaluations to maintain this skill. Field-trial evidence separates observed local behavior from untested platforms and scenarios. The source-available license covers these instructions; independent outputs are addressed in the included LICENSE.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Fullstack Web Delivery AI skill do?

Plan and deliver complete websites with adaptive discovery, industry research, client-specific UI/UX, frontend, backend, authentication, persistent data, local preview, and evidence-based acceptance. Use for new sites or substantial redesigns; keep targeted fixes within their existing scope.

Why use Fullstack Web Delivery on TypingMind?

Because you install it once and use it with any model. Fullstack Web Delivery 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 Fullstack Web Delivery in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/KarmenLoop/fullstack-web-delivery/tree/main/skills/fullstack-web-delivery. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Fullstack Web Delivery?

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 Fullstack Web Delivery?

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

Is the Fullstack Web Delivery AI skill free?

It is published on GitHub by KarmenLoop. Check the repository for licensing terms. 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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