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Website Clone Report

Community
luongnv89
website-clone-report

Generate a plain-language report from website-analyzer JSON and save it only after explicit approval. Use for non-technical summaries. Don't use for developer audits, raw SEO analysis, penetration testing, or unapproved persistence.

Overview

Publisherluongnv89
Repositoryskills
Skill namewebsite-clone-report
Stars
124
Forks
18
Bundled files
1
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.

  • 1 bundled files

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

  • Open source

    Published by luongnv89 on GitHub. Read the source before you install it.

Installation

Install the Website Clone Report 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/luongnv89/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/website-cloner/website-clone-report .claude/skills/website-clone-report
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Website Clone Report 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 Website Clone Report 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 Website Clone Report 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.

Website Clone Report

Converts structured website analysis into a comprehensive, plain-language report for non-technical readers. Approval gate: persists only after explicit user validation.

When to Use

Trigger when the user asks to:

  • Create a report from website analysis results
  • Translate technical website metrics into plain language
  • Produce an end-user summary of a site assessment

Do not use for technical audit reports targeting developers — those belong to the analyzer skill.

Prerequisites

  1. Require valid website-analyzer JSON and a proposed output path.
  2. Read references/api_reference.md when validating fields or translating metrics; use only the needed reference mappings to protect the context budget.
  3. Confirm the user can review the draft and explicitly approve persistence.
  4. Stop with a descriptive error when the JSON is invalid or contains an analyzer error variant.

Workflow

1. Read the analyzer output (JSON)
2. Translate each dimension into plain language
3. Draft the report for non-technical readers
4. Present to user for review
5. Incorporate edits (loop until approved)
6. Persist final report to file

Report Structure

Write the report in plain language. Technical metrics are translated, not just listed.

markdown
# Website Analysis Report: <site name>
**URL:** <url>
**Date:** <date>

---

## At a Glance

A plain-language summary: what this site is, who it's for, and its overall health.
Example: "This is a SaaS landing page targeting small businesses. It looks polished and modern,
but loads slowly on mobile connections and is missing key SEO elements that would help it
rank in search results."

## How It Looks and Works

Translate UI/UX findings into plain language:
- Layout style (e.g., "clean single-column layout with a large hero image")
- What draws attention first
- Any friction points (e.g., "the sign-up button is hidden below the fold")
- Responsive behavior

## What Kind of Site This Is

Category description in plain terms:
- "This is an e-commerce store selling handcrafted furniture"
- "This is a documentation site for a developer tool"

## Design and Style

Describe the visual identity accessibly:
- Typography feel (e.g., "modern sans-serif fonts that feel clean and professional")
- Color palette (e.g., "cool blues and grays with orange accents for calls to action")
- Spacing and density
- Motion and interactivity feel

## Performance

Translate metrics to plain language:
- **How fast content appears:** "The main content takes about X seconds to appear.
  For comparison, sites that load in under 2 seconds tend to keep visitors engaged."
- **Visual stability:** "The page layout is mostly stable while loading.
  You're unlikely to notice elements jumping around."
- **How quickly the server responds:** "The estimated server response delay is X seconds."
- **How much data it uses:** "The page weighs about X KB, roughly equivalent to
  loading Y average-sized images."
- **Number of resources:** "The page makes about X requests to load."

## Security Overview

Surface-level observations only, in plain language:
- "The site uses HTTPS, which means data between your browser and the site is encrypted."
- "The site sends a few security signals to browsers, but could strengthen them."

Always note this is not a full security audit.

## Search Engine Visibility

SEO findings translated:
- Overall score with context: "SEO score: X/100 — [excellent/good/fair/poor]"
- "The page has a title and description that search engines can read."
- "The heading structure could be improved to help search engines understand the content."
- "Images are missing alternative text, which helps with accessibility and search."

## Summary and Next Steps

A brief section with actionable takeaways:
- What's working well (2–3 points)
- What needs attention (2–3 points)
- What could be improved (2–3 points)

---

*This report was generated from automated analysis. All findings are based on a single-page crawl
and may not reflect the full site.*

Step 1: Read Analyzer Output

Read the JSON analysis file (or content passed via stdin/argument):

Read file <path-to-analysis.json>

If no file is provided and $ARGUMENTS contains a URL, note that this skill requires pre-existing analysis output (produced by website-analyzer), not raw URLs. The orchestrator should have already run Phase 1.

Step 2: Translate to Plain Language

For each dimension:

  • UI/UX: Describe layout and friction in terms a non-technical person understands. Avoid jargon like "visual hierarchy" — say "what catches the eye first."
  • Category: Explain what kind of site it is in plain terms.
  • Style: Describe the feel and look without needing CSS knowledge.
  • Performance: Translate numbers to relatable comparisons:
    • lcp_estimate_seconds: "how fast the main content appears" (estimated seconds)
    • cls_estimate: "how stable the page feels while loading" (unitless)
    • ttfb_estimate_seconds: "how quickly the server responds" (estimated seconds)
    • Page weight: "how much data the page uses"
    • Request count: "how many pieces the page needs to load"
  • Security: Surface-level observations only, no technical headers jargon.
  • SEO: Explain each finding in terms of "helping people find this site on Google."

Step 3: Draft the Report

Assemble the translated content into the report structure above.

Step 4: Present for Review

Present the draft report to the user. Ask:

"Here is the analysis report. Please review it and let me know:

  1. Approve — it looks good, save it
  2. Edit — I'd like to change something (specify what)
  3. Regenerate — start over with different focus"

Step 5: Incorporate Edits (loop)

If the user requests edits:

  • Update the report accordingly
  • Re-present for review
  • Repeat until approved

Do not persist the file until explicit approval.

Step 6: Persist Final Report

Once approved, persist the report using the Write tool — do not shell out to echo or cat redirection.

  • Path: the value passed via --output <path>. If absent, fall back to $PROJECT_DIR/report.md when the orchestrator set $PROJECT_DIR, otherwise to report.md in the current working directory.
  • Content: the approved markdown report assembled in Step 3, with any edits from Step 5 applied.

After the Write call returns, confirm to the user:

Report saved to: <absolute-path>

Acceptance Criteria and Expected Output

Verify the approved report before saving:

  • It covers UI/UX, category, style, performance, surface security, SEO, and next steps, or names each unavailable dimension.
  • Every metric and observation traces to the analyzer JSON; assert that no unsupported benchmark or claim was invented.
  • Language is understandable without developer terminology, while the security and single-page-crawl caveats remain explicit.
  • The expected result is a non-empty approved markdown file at the resolved path, written only after explicit approval such as Approve.
  • Re-read the saved file state or Write result and report its absolute path.

Edge Cases and Error Handling

FailureBehavior
No analyzer input providedAsk for the analysis JSON file path
Invalid JSONReport error and ask for valid input
Analyzer error variantSurface its error and detail; do not draft a health report
Missing or null dimensionOmit unsupported specifics and identify the gap in next steps
User never approvesKeep the loop going; do not auto-save

Step Completion Report

text
◆ Website Clone Report
··································································
  Analyzer JSON:        √ pass | × fail ([reason])
  Six dimensions:      √ translated | × partial ([missing])
  Draft reviewed:      √ pass
  User approved:       √ pass | × pending
  Report saved:        √ pass ([absolute path]) | — not approved
  Result:              PASS | BLOCKED | FAIL

Never report PASS before both explicit approval and a successful Write result.

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 Website Clone Report AI skill do?

Generate a plain-language report from website-analyzer JSON and save it only after explicit approval. Use for non-technical summaries. Don't use for developer audits, raw SEO analysis, penetration testing, or unapproved persistence.

Why use Website Clone Report on TypingMind?

Because you install it once and use it with any model. Website Clone Report 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 Website Clone Report in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/luongnv89/skills/tree/main/skills/website-cloner/website-clone-report. 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 Website Clone Report?

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 Website Clone Report?

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

Is the Website Clone Report AI skill free?

Yes. It is published on GitHub by luongnv89 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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