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Browser4 Web Miner

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platonai
browser4-web-miner

Groups similar web pages together and produces an interactive HTML report with clusters of related pages, plus Excel spreadsheets for analysis. Use when the user wants to cluster downloaded HTML files, convert detail web pages into interactive views, or analyze a folder of web pages locally.

Overview

Publisherplatonai
RepositoryBrowser4
Skill namebrowser4-web-miner
Stars
1.2K
Forks
152
Bundled files
Instructions only
LicenseApache-2.0
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 platonai on GitHub. Read the source before you install it.

Installation

Install the Browser4 Web Miner 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/platonai/Browser4/tree/main/skills/browser4-web-miner
  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/platonai/Browser4.git /tmp/Browser4
mkdir -p .claude/skills
cp -r /tmp/Browser4/skills/browser4-web-miner .claude/skills/browser4-web-miner
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Browser4 Web Miner 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 Browser4 Web Miner 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 Browser4 Web Miner 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.

WebMiner — Convert Detail Web Pages into Interactive Views

Quick Start

bash
browser4-cli webminer install            # one-time install (Java 17+ auto-detected)
browser4-cli webminer all <html-dir>     # full pipeline: encode → cluster → views

WebMiner groups similar web pages together and produces an interactive HTML report with clusters of related pages — plus Excel spreadsheets for further analysis. Give it a folder of downloaded HTML files, and it handles the rest. Everything runs locally; no data leaves your machine.

When to Use

Use WebMiner when you have a folder of downloaded HTML pages and want to cluster them into interactive views and Excel reports — fully local, no LLM tokens. It complements rather than replaces browser4-cli crawl/swarm (which acquire pages): WebMiner analyzes pages you already have. Not for single-page extraction — use htmlsnapshot for that.

How It Works

WebMiner runs a three-stage local pipeline: encode converts each HTML page into a 69-dimension feature vector, cluster groups similar pages with SMILE KMeans (k auto-detected), and views renders an interactive HTML report plus Excel spreadsheets. Everything runs locally on your machine — no data leaves it, and no LLM tokens are consumed.

Patterns

1. Full pipeline on a folder of pages

bash
browser4-cli webminer all <html-dir>

2. Rebuild views from an existing run

bash
browser4-cli webminer views <result-dir>

3. Try it on the sample dataset

bash
browser4-cli webminer run-example

Flags

FlagApplies toDescription
--max-files <n>webminer allLimit the number of HTML files processed (default 40)
--output <dir>webminer allOverride the output directory
--resume [<project-id>]webminer allResume a previous run

Errors & Recovery

SymptomCauseFix
webminer install failsNo Java 17+ on PATHInstall JDK 17+ or point JAVA_HOME at it
webminer all finds no pagesDirectory has no .html filesCheck the input directory path and file extensions
Pipeline crashes on large corporaFree tier limit (< 1,000 pages)Reduce the corpus or use --max-files; see the commercial Spark tier for scale
Views land in an unexpected temp dirThe views stage uses the app task-output rootUse webminer views <result-dir> to rebuild beside the result dir

Using from the Browser4 CLI

WebMiner is a first-class Browser4 citizen: the browser4-cli webminer command installs, updates, and runs the tool natively (no PowerShell needed — the CLI locates a Java 17+ installation, preferring the JRE bundled with the Browser4 runtime, and launches scent-miner.jar directly). The JAR and its release metadata are installed to ~/.scent/webminer/.

bash
browser4-cli webminer install            # Download and install the latest release
browser4-cli webminer update             # Check for and install the latest release
browser4-cli webminer version            # Show installed and latest available versions
browser4-cli webminer uninstall          # Remove the installed release
browser4-cli webminer run-example        # Sample dataset + full pipeline (needs 7-Zip)
browser4-cli webminer all <html-dir>     # Full pipeline (encode → cluster → views)
browser4-cli webminer views <result-dir> # Rebuild views from an existing run
  • webminer all <dir> accepts the pipeline options directly (--max-files <n>, --output <dir>, --resume [<project-id>]).
  • Any other command is forwarded verbatim to scent-miner.jar, e.g. browser4-cli webminer encode <dir>.
  • Runs started through the CLI set -Dapp.name=webminer, so the views task-output root is %TEMP%\webminer-<user>\ml\tasks\... (<user> is the OS user name; see Output).
  • The bare webminer panel and webminer version keep the update check quiet: the GitHub → OSS-mirror fallback notices (rate limit, HTTP status, unreachable) are suppressed, and the Published line is omitted entirely when the release carries no published_at. webminer install / update still report the fallback.

Installing WebMiner

browser4-cli webminer install downloads, verifies, and installs the latest release (GitHub Releases with an Aliyun OSS mirror fallback; works on Windows, Linux, and macOS — no PowerShell needed):

bash
browser4-cli webminer install            # Download and install the latest release
browser4-cli webminer update             # Check for and install the latest release
browser4-cli webminer version            # Show installed and latest available versions
browser4-cli webminer uninstall          # Remove the installed release

Releases are installed to ~/.scent/webminer/ and checked against https://github.com/platonai/web-miner/releases. SHA-256 checksums are verified automatically on download.

You can also use the JAR directly if it's already available:

bash
java -jar scent-miner.jar <command> <args>

Converting Pages to Views

Running the Example

The run-example command downloads a pre-uploaded test dataset of real web pages, extracts it, and runs the full pipeline — no manual setup required beyond Java 17 and 7-Zip:

bash
browser4-cli webminer run-example

The dataset is cached at ~/.scent/test-data/amazon.com/ so subsequent runs skip the download.

Running on Your Own Pages

bash
# Full pipeline (one-shot)
browser4-cli webminer all /path/to/html/files

# Or with the JAR directly
java -jar scent-miner.jar all /path/to/html/files

The cluster count is always auto-detected from the data — this produces better results than guessing a number.

Options

FlagDefaultPurpose
--max-files <n>40Maximum number of HTML files to process
--output <dir><html-dir>-ml-outputWhere to write the clustered results (CSV + clustering info; the views stage uses the app temp root — see Output)
--resume [<project-id>]—Pick up where a previous run left off. If no project ID is given, the most recent project is used.

Building Views from an Existing Run

If clustering has already completed and you just need to (re)build the views:

bash
java -jar scent-miner.jar views <html-dir>-ml-output/kmeans-result/p<timestamp>

Output

all produces two kinds of artifacts in two different places:

  1. Clustered results — written to <html-dir>-ml-output/kmeans-result/p<timestamp>/ (or wherever --output points): one result.csv per feature view (predictionAnd{Final,Minimal,Original}Features/result.csv) plus clusteringInfo.txt.
  2. Views (interactive HTML report + Excel + JSON) — the views stage of all writes them to the application's temp task-output root, NOT under <html-dir>-ml-output: %TEMP%\<app>-<user>\ml\tasks\unsupervised\result\p<timestamp>\predictionAndMinimalFeatures.views\ on Windows, and <java.io.tmpdir>/<app>-<user>/ml/tasks/unsupervised/result/p<timestamp>/predictionAndMinimalFeatures.views/ on Linux/macOS (/tmp/... on Linux, $TMPDIR on macOS) — the <app> prefix follows -Dapp.name (webminer when launched through browser4-cli webminer, pulsar for a direct java -jar run) and <user> is the OS user name. The end of the run prints the resolved absolute views path.

So after java -jar scent-miner.jar all ./html-pages/ the clustered results look like:

html-pages-ml-output/
  └── kmeans-result/
      └── p<timestamp>/
          ├── predictionAndFinalFeatures/result.csv
          ├── predictionAndMinimalFeatures/result.csv
          ├── predictionAndOriginalFeatures/result.csv
          └── clusteringInfo.txt

and the views (<project>.html, *.xlsx, *.json) live in the temp task-output directory printed by the run.

The real report is <project>.html (e.g. p<timestamp>.html), not index.html. The index.html inside the views directory is an auto-generated directory listing ("Index of predictionAndMinimalFeatures.views") — opening it shows a file list, not the interactive clustering report. Open <project>.html instead.

To place the views beside the clustered results (e.g. to archive them with the project), rebuild them from the result directory:

bash
browser4-cli webminer views <html-dir>-ml-output/kmeans-result/p<timestamp>
# (equivalent to: java -jar scent-miner.jar views <html-dir>-ml-output/kmeans-result/p<timestamp>)

This writes predictionAndMinimalFeatures.views/ inside the given result directory — the recommended way to locate artifacts, since the output path is explicit instead of an opaque temp path. Open the generated <project>.html in a browser to explore the clustering results. The .xlsx files can be opened in Excel for sorting, filtering, or further analysis.

Tips

  • Input files — only *.html and *.htm files are processed. Other files in the directory are ignored.
  • Resume interrupted runs — if a pipeline stops partway through, use --resume to continue from the last completed stage instead of starting over.
  • Offline only — WebMiner works with pre-downloaded HTML files. Use a browser, wget, or a crawler to fetch pages first.
  • Java 17 is required. Make sure java is on your PATH.

Frequently asked questions

What does the Browser4 Web Miner AI skill do?

Groups similar web pages together and produces an interactive HTML report with clusters of related pages, plus Excel spreadsheets for analysis. Use when the user wants to cluster downloaded HTML files, convert detail web pages into interactive views, or analyze a folder of web pages locally.

Why use Browser4 Web Miner on TypingMind?

Because you install it once and use it with any model. Browser4 Web Miner 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 Browser4 Web Miner in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/platonai/Browser4/tree/main/skills/browser4-web-miner. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Browser4 Web Miner?

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 Browser4 Web Miner?

As many as you like. As long as a model supports skills, you can use Browser4 Web Miner with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Browser4 Web Miner AI skill free?

Yes. It is published on GitHub by platonai under the Apache-2.0 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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