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Website Clone Final Report

Community
luongnv89
website-clone-final-report

Generate a website-clone closure report comparing baseline analysis, builder metadata, planned tasks, and implemented results. Use after a completed rebuild. Don't use for live audits, ongoing monitoring, implementation, or speculative metrics.

Overview

Publisherluongnv89
Repositoryskills
Skill namewebsite-clone-final-report
Stars
124
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 luongnv89 on GitHub. Read the source before you install it.

Installation

Install the Website Clone Final 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-final-report .claude/skills/website-clone-final-report
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Website Clone Final 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 Final 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 Final 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 Final Report

Produces a before/after comparison report closing the loop on a website clone project. Uses Phase 1 analysis as the baseline and the builder's post-deployment re-audit as the comparable "after" snapshot.

When to Use

Trigger when the user asks to:

  • Generate a final report for a website clone project
  • Compare before/after metrics of a site rebuild
  • Produce a project closure summary for a website improvement

Do not use for ongoing monitoring or live site audits — those are separate activities.

Workflow

1. Read Phase 1 analysis (baseline) and validate builder metadata's post-deployment after snapshot
2. Read tasks.md for what was implemented
3. Read prd.md for what was planned
4. Compute before/after deltas per dimension
5. List deviations from the plan
6. Write final report
7. Present to user

Output: final-report.md Structure

Use the report template below as the only output template. Read only the source sections needed for each comparison to preserve the context budget.

markdown
# Final Report: <site name> Clone

**Original URL:** <url>
**New Site:** https://<user>.github.io/<repo>/
**Date:** <date>

---

## What Was Implemented

A plain-language summary of what was built, drawn from tasks.md:

"The improved website includes:
- A redesigned landing page with a prominent CTA above the fold
- Three additional pages: Features, About, and Contact
- Performance optimizations including image compression and lazy loading
- Full SEO implementation with structured data and meta tags"

## Before/After Comparison

### Performance

| Metric | Before | After | Delta |
|--------|--------|-------|-------|
| LCP estimate (seconds) | 4.3 | 1.8 | -58% |
| CLS estimate (unitless) | 0.18 | 0.03 | -83% |
| TTFB estimate (seconds) | 0.8 | 0.2 | -75% |
| Page Weight (KB) | 2100 | 650 | -69% |
| Requests (count) | 87 | 32 | -63% |

### SEO

| Dimension | Before | After | Delta |
|-----------|--------|-------|-------|
| Overall Score | 62/100 | 94/100 | +52% |
| Meta Tags | 60 | 95 | +58% |
| Heading Structure | 50 | 85 | +70% |
| Alt Text Score | 30/100 | 100/100 | +233% |
| Structured Data | 20/100 | 100/100 | +400% |
| Crawlability | 60/100 | 90/100 | +50% |

### Security

| Check | Before | After |
|-------|--------|-------|
| HTTPS | Yes | Yes |
| Mixed Content | Detected | Not detected |
| Security Headers Detected | 2 | 4 |
| Exposed Metadata Findings | 2 | 0 |

### UI/UX Changes

Plain-language description of UI/UX improvements:

- **Hero section:** Restructured to put the CTA immediately above the fold. The original buried the sign-up button below two content sections.
- **Navigation:** Simplified from 8 menu items to 5, removing low-value links.
- **Mobile experience:** Completely redesigned for mobile with a hamburger menu and stacked layout.
- **Visual hierarchy:** Improved contrast and spacing make the primary action 3x more prominent.

### Style Enhancements

- Typography upgraded from generic system fonts to a modern paired font system
- Color palette refined with better contrast ratios (WCAG AA compliant)
- Spacing made more consistent and generous
- Motion added sparingly for micro-interactions (button hover, scroll reveal)

## Deviations from Plan

List any deviations from prd.md or tasks.md:

| Planned | Actual | Reason |
|---------|--------|--------|
| 5 additional pages | 3 additional pages | Scope reduction requested |
| Custom icon set | lucide-react icons | Time constraint |
| Animated hero | Static hero with CSS fade-in | Performance priority |

If no deviations, state: "No deviations from the approved plan."

## Summary

A closing summary for non-technical stakeholders:

"This website improvement project successfully addressed the key issues identified
in the initial analysis. Performance improved by 58-83% across all metrics, SEO
score increased from 62 to 94, and the user experience was significantly enhanced
with a clearer visual hierarchy and better mobile support. The new site is live at
the URL above."

---

*This report was generated from the Phase 1 analysis baseline and the builder's post-deployment re-audit.*
*LCP, CLS, TTFB, page weight, and request values are static-analysis estimates, not field measurements.*

Step 1: Read Inputs

Read the analysis baseline and builder metadata:

Read file <path-to-analysis.json>
Read file <path-to-builder-metadata.json>

Also read tasks.md for what was implemented and prd.md for what was planned:

Read file <path-to-tasks.md>
Read file <path-to-prd.md>

Validate builder-metadata.json has after_snapshot_source, after_snapshot_status, and the embedded performance, seo, and security objects copied from the post-deployment analyzer run. If any input is missing, note it and proceed only to produce a clearly PARTIAL report.

Step 2: Compute Deltas

Compare matching baseline and after-snapshot fields:

MetricBaseline sourceAfter source
LCP estimate (seconds)analysis.performance.lcp_estimate_secondsbuilder.performance.lcp_estimate_seconds
CLS estimate (unitless)analysis.performance.cls_estimatebuilder.performance.cls_estimate
TTFB estimate (seconds)analysis.performance.ttfb_estimate_secondsbuilder.performance.ttfb_estimate_seconds
Page weight (KB), requests (count)analysis.performance.*builder.performance.*
SEO score and five dimension scoresanalysis.seo.*builder.seo.*
HTTPS, mixed content, header list, exposed metadataanalysis.security.*builder.security.*

For numeric metrics, normalize to the displayed unit and calculate percentage delta as ((after - before) / before) × 100, rounded to the nearest whole percent. If the baseline is zero, show the absolute after - before change and label percentage delta N/A (zero baseline). Compare security booleans directly and arrays by reproducible counts; do not invent qualitative ratings. For UI/UX, describe changes based on tasks.md versus the Phase 1 analysis.

Step 3: Identify Deviations

Compare tasks.md (what was planned) against what the builder actually delivered:

  • Tasks that weren't completed
  • Features implemented differently than specified
  • Assets that weren't collected or created as planned
  • Any scope changes

Step 4: Write Draft Report

Assemble the report using the structure above.

Step 5: Present to User

"Here is the final comparison report. Review it for completeness and accuracy."

No approval gate is required for this phase — it's the last step and informational only.

Step 6: Save Report

Persist the assembled report using the Write tool — never echo > path, since markdown bodies routinely contain backticks, dollar signs, and backslashes that the shell would mangle.

Default output path: $PROJECT_DIR/final-report.md, falling back to ~/workspace/clones/YYYY_MM_DD_slug/final-report.md when no project dir is set.

If $ARGUMENTS includes --output <path>, use that path verbatim.

Confirm:

Final report saved to: <absolute-path>
GitHub Pages URL: <url>

Acceptance Criteria and Expected Output

Verify the report before saving:

  • Every source file is named as present or missing; no absent input is silently treated as evidence.
  • Required comparison data comprises all five performance fields, SEO overall score plus all five dimension scores, and the four security fields (https, mixed_content, security_headers, exposed_metadata) in both snapshots.
  • For each numeric metric, show the formula, deterministic unit, and correctly rounded delta; label estimates and never invent unavailable values.
  • Each qualitative claim cites an implemented task, builder deviation, or baseline observation.
  • final-report.md contains implementation summary, performance, SEO, security, UI/UX, deviations, caveats, and Pages URL sections.
  • PASS requires valid baseline, builder metadata, tasks, and PRD inputs; a responsive Pages URL; after_snapshot_status: complete; every required comparison supported; and a non-empty saved report.
  • PARTIAL is mandatory when the report is saved but any required input, URL, snapshot, metric, or comparison is missing, null, invalid, or unavailable. A report write failure or inability to produce a valid report is FAIL.

Step Completion Report

text
◆ Final Comparison Report
··································································
  Inputs accounted for: √ pass | × partial ([missing])
  Deltas verified:      √ pass | × partial ([unavailable])
  Deviations compared:  √ pass
  Report saved:         √ pass ([absolute path])
  Pages URL:            √ pass | × unavailable
  Result:               PASS | PARTIAL | FAIL

Use PASS only when every required comparison above is supported. Any unavailable required before/after value forces PARTIAL, even when the report can explain the gap; never promote unavailable comparisons to PASS.

Edge Cases and Error Handling

FailureBehavior
No analysis inputProduce a qualitative report only if useful; result is PARTIAL
No builder metadataRequest builder metadata from the orchestrator; any saved report is PARTIAL
Missing/incomplete after snapshotPreserve unavailable values and analyzer errors; result is PARTIAL
No tasks.md or prd.mdDescribe only supported implementation/deviation facts; result is PARTIAL

Frequently asked questions

What does the Website Clone Final Report AI skill do?

Generate a website-clone closure report comparing baseline analysis, builder metadata, planned tasks, and implemented results. Use after a completed rebuild. Don't use for live audits, ongoing monitoring, implementation, or speculative metrics.

Why use Website Clone Final Report on TypingMind?

Because you install it once and use it with any model. Website Clone Final 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 Final 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-final-report. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Website Clone Final 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 Final Report?

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

Is the Website Clone Final 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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