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Dynamic Content Extraction

Community
AtlasOmnia
dynamic-content-extraction

dynamic-content-extraction — Extract structured data from JavaScript-heavy sites where prices, text, or key fields don't appear in the standard accessibility tree. Covers browser_snapshot(full=true), TreeWalker text-node extraction, lazy-load handling, and React split-text recovery.

Overview

PublisherAtlasOmnia
Repositorydonna-starter
Skill namedynamic-content-extraction
Stars
131
Forks
9
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 AtlasOmnia on GitHub. Read the source before you install it.

Installation

Install the Dynamic Content Extraction 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/AtlasOmnia/donna-starter/tree/main/skills/research/dynamic-content-extraction
  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/AtlasOmnia/donna-starter.git /tmp/donna-starter
mkdir -p .claude/skills
cp -r /tmp/donna-starter/skills/research/dynamic-content-extraction .claude/skills/dynamic-content-extraction
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Dynamic Content Extraction 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 Dynamic Content Extraction 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 Dynamic Content Extraction 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.

Dynamic Content Extraction

Extract structured data (prices, listings, product details) from JavaScript-heavy sites where key data is rendered by React/Vue/Angular and doesn't appear in compact accessibility snapshots.

When to Use

  • Extracting prices, inventory, or listings from booking/retail/travel sites (IHG, Marriott, Booking.com, Amazon, etc.)
  • Data is visible on screen but absent from browser_snapshot (compact mode)
  • web_extract times out or returns empty content
  • Direct API calls are blocked by CDN/WAF (Akamai, Cloudflare)
  • Vision model reads are inconsistent for exact numeric data
  • the user asks for browser/search/internet tool inventory, fallback order, or says to check the vault for web tooling
  • Estimating market-wide keyword demand when the request is phrased as “check Google Analytics” or a chart/API is rendered only in page JavaScript

References:

  • If a browser/search/internet tool inventory or fallback order is needed, inspect the environment's own tool catalog at runtime rather than relying on a static vault note.
  • distinguishes Analytics, Search Console, Keyword Planner, Trends, and SEO volume datasets; it includes same-origin Google Trends widget extraction and concise reporting guidance.
  • documents the live OpenRouter model-performance endpoint, extraction code, metric interpretation, specialty-model screening, and Nitro routing.

Workflow

1. Start with full snapshot

browser_snapshot(full=true) — compact mode (full=false, the default) often omits dynamically-injected prices and numbers. Full mode surfaces text from the complete accessibility tree.

Pitfall: Full snapshots on listing pages are often truncated (300-500 lines). Accept this and scroll + re-extract in batches.

2. Scroll to trigger lazy loading

Modern sites (IHG, Airbnb, Expedia) only render ~5-15 cards initially. Scroll 4-6 times before extraction:

browser_scroll(direction='down') # repeat 3-6 times

Then wait 2-3 seconds for React to hydrate new cards.

3. Extract via TreeWalker (React split-text recovery)

React and similar frameworks often split text across sibling elements. Example from IHG:

html
<span><span>156</span><span> USD</span></span>

The number "156" and "USD" are separate text nodes — regex on parent textContent works but may merge adjacent values.

Preferred pattern — TreeWalker for leaf text nodes:

js
(() => {
 const walker = document.createTreeWalker(document.body, NodeFilter.SHOW_TEXT);
 const results = [];
 let node;
 while (node = walker.nextNode()) {
 const text = node.textContent.trim();
 // Match data patterns (prices, ratings, etc.)
 if (/^\d{2,4}$/.test(text) && node.parentElement.textContent.includes('USD')) {
 const price = parseInt(text);
 // Walk up to find container card
 let card = node.parentElement;
 for (let i = 0; i < 15 && card; i++) {
 const h2 = card.querySelector('h2');
 if (h2) {
 results.push({ name: h2.textContent.trim(), price });
 break;
 }
 card = card.parentElement;
 }
 }
 }
 return results;
})()

4. Walk up from data nodes to cards

Data nodes (price, rating) live deep inside React component trees. Walk up 10-15 levels to find the card container, then query inside it for other fields (name, distance, reviews).

Pattern:

price text node → parent → ... (walk up) → card container → querySelector('h2')

5. Cross-validate with snapshot data

Use browser_snapshot to cross-check hotel names and amenities, since the accessibility tree reliably captures headings and static text labels — just not the prices.

6. Discover and query same-origin frontend data APIs

When the page offers a useful sort/filter but the accessibility tree omits the underlying numbers:

  1. Inspect performance.getEntriesByType('resource') for same-origin frontend/API requests.
  2. If the UI's internal sort value is unclear, search loaded JavaScript chunks for the visible option label and recover its enum or query parameter.
  3. Reproduce the request with fetch() inside browser_console, inspect the response shape, then join records through stable IDs rather than parsing rendered cards.
  4. Preserve metric window, percentile, sample count, provider, and units in the result. Live rankings without those fields are easy to overstate.
  5. Classify specialized entries before recommending them; a raw throughput leader may be a moderation, image, code-apply, or multi-agent model rather than a general assistant.

Pitfalls

  • Confirm the active browser target on multi-tab/social sites. browser_console can evaluate against the wrong tab or a browser new-tab surface after redirects/login flows. If the snapshot shows the target page but console reads chrome://new-tab-page/ or unrelated content, call browser_cdp(method='Target.getTargets'), pick the type='page' target with the desired URL, then run Runtime.evaluate against that target_id to extract document.body.innerText.
  • For login-gated social pages, separate access verification from management actions. After the user signs in, first verify the resolved URL, visible page title/name, follower/like counts, description/about text, and whether management controls are present. Do not switch profiles, edit settings, create ads, send messages, or otherwise mutate the account unless the user explicitly asks.
  • Don't trust vision for exact numbers. Vision models conflate similar hotel names (avid ↔ EVEN), misread prices, and hallucinate hotels that aren't on screen. Use vision only as a last resort for layout questions.
  • web_extract fails on SPAs. JS-heavy booking sites routinely time out (60s). Don't retry — go straight to browser tools.
  • CDP Network domain may be unavailable. Don't assume Network.getAllCookies or Network.getResponseBody work in all browser sessions. Fall back to in-console fetch() calls from the same origin.
  • Direct API calls get Akamai/Cloudflare blocks. Don't waste time on curl to booking-site APIs. Use the browser's authenticated session via browser_console fetch().
  • Sort dropdowns may not respond to browser_click. React-controlled custom dropdowns often ignore synthetic clicks. Try URL parameter manipulation instead (look for qSrt, sort, order params).
  • Lazy loading means one extraction never gets everything. Scroll → wait → extract in a loop. Accept partial coverage and report how many of the total were captured.
  • textContent merging corrupts adjacent fields. When "rating 4.5" and "2920 reviews" appear in sibling elements, textContent returns "rating 4.52920 reviews". Extract from leaf text nodes individually instead.

Frequently asked questions

What does the Dynamic Content Extraction AI skill do?

dynamic-content-extraction — Extract structured data from JavaScript-heavy sites where prices, text, or key fields don't appear in the standard accessibility tree. Covers browser_snapshot(full=true), TreeWalker text-node extraction, lazy-load handling, and React split-text recovery.

Why use Dynamic Content Extraction on TypingMind?

Because you install it once and use it with any model. Dynamic Content Extraction 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 Dynamic Content Extraction in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/AtlasOmnia/donna-starter/tree/main/skills/research/dynamic-content-extraction. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Dynamic Content Extraction?

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 Dynamic Content Extraction?

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

Is the Dynamic Content Extraction AI skill free?

Yes. It is published on GitHub by AtlasOmnia 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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