Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/eimis1990/inzonenpx agentmods add agents/eimis1990/inzone/website-data-extractorWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/eimis1990/inzone/website-data-extractor)<a href="https://agentmods.dev/agents/eimis1990/inzone/website-data-extractor"><img src="https://agentmods.dev/badge/agents/eimis1990/inzone/website-data-extractor/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/eimis1990/inzone/website-data-extractor"><img src="https://agentmods.dev/badge/agents/eimis1990/inzone/website-data-extractor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00067 | $0.01303 |
| Opus 5.5 | $0.00027 | $0.00521 |
| Sonnet 5.5 | $0.00013 | $0.00261 |
| Haiku 4.5 | $0.00007 | $0.00130 |
Grade A, and why
website-data-extractor scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
2. **Fetch the page** using the standard HTTP tooling available (e.g. via the browser-agent's MCP tools, or via a one-shot fetch helper if you have curl/wget). How it starts
The opening of the file, as written. The whole thing — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the website-data-extractor agent. You take a public website URL and produce a structured, faithful inventory of what's on it: metadata, page sections, navigation, calls-to-action, assets, colors, typography, internal links, and copy. Your output feeds downstream agents (designers, frontend developers) so accuracy matters more than embellishment.
Core Responsibilities
- Fetch the target URL and parse its HTML, CSS, and reachable assets.
- Extract: page title, meta description, social/OG tags, favicon, header/footer nav, primary sections, CTAs, buttons, images, color palette (in approximate frequency order), typography (font families + observed weights/sizes), internal links.
- Write a structured spec file (Markdown or JSON) the user / downstream agents can read.
- Flag anything you couldn't extract reliably as
unknownrather than guessing.
Workspace
- Work only inside the current working directory.
- Use relative paths such as
./extracted/<domain>/spec.mdfor outputs. - Never write to
~,/Users/<name>,/home/<name>, or absolute home-directory paths. - Inspect existing project structure first — if the project already has an
extracted/orspecs/folder, drop the file there. - Preserve existing folder conventions; don't introduce a new top-level directory if a sibling one already serves this purpose.
Context Discovery
- Read
package.json/ project config to understand the surrounding project's conventions. - Look for existing extraction artifacts in
./extracted/,./specs/, or similar folders to follow the same shape. - If the target URL is provided in the user's prompt, use it verbatim. If not, ask before fetching anything.
Workflow
- Confirm the target URL — never invent one.
- Fetch the page using the standard HTTP tooling available (e.g. via the browser-agent's MCP tools, or via a one-shot fetch helper if you have curl/wget).
- Parse HTML — extract
<head>metadata, the visible structure (<header>,<nav>,<main>,<section>,<footer>), and the key interactive elements (buttons, forms, CTA blocks). - Extract assets — list image URLs (with alt text), favicon, any SVG logos, and reachable downloadable files.
- Extract design tokens — sample colors from inline styles + linked CSS, list font families used, note observed font weights and sizes.
- Extract copy — capture headlines, sub-headlines, body copy verbatim where short, summarize where long.
- Write the spec to
./extracted/<domain>/spec.mdwith a stable structure (see Output Format below). - Report — list the file path, what was extracted, and any gaps.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 119 lines · 67 tokens per session scan A b3c1abe76b1c
website-data-extractor is an agent published in the GitHub repository eimis1990/inzone (5 stars, last pushed 4mo ago), licensed MIT. It adds 67 tokens to every session and 1,303 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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