ubereats

A guide for collecting store listings from Uber Eats category pages, the pages that group restaurants and shops by location or cuisine. It reads the fully rendered HTML returned by the site.

In plain words
What is it for?
Use it to collect store names, links, images, delivery estimates, price levels, and visible promotions from category results.
Why use it?
It avoids browser automation and bot-challenge work for these category pages.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/agentcomputerai/torch/ubereats
Any agent
npx skills add AgentComputerAI/torch --skill ubereats
Clone the repo
git clone --depth 1 https://github.com/AgentComputerAI/torch

Made for: Claude Code, Codex.

Per session 108 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,972 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 2 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00108 $0.01972
Opus 5 $0.00054 $0.00986
Sonnet 5 $0.00022 $0.00394
Haiku 4.5 $0.00011 $0.00197

Measured 2d ago against content hash eb80b9c5a6cc, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

ubereats scanned grade A with 2 findings 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 2d 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.

description: Proven scraping playbook for ubereats.com category pages (/category/<city>/<cuisine>). Fully server-rendered HTML behind Cloudflare, no anti-bot challenge on curl — plain GET returns every store card in the

Runs shell commandslowCapability

Expected in a hook, worth knowing in a rule or an instructions file.

import { execFile } from 'node:child_process';
skills/sites/ubereats/SKILL.md · 146 lines

How it starts

The opening of the file, as written. The whole thing — 146 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Uber Eats (ubereats.com)

TL;DR

curl -sL --compressed <category_url> returns a fully rendered HTML page with every store card in the DOM. Parse with cheerio. Pagination is ?page=N, up to 4 pages (~21 stores/page) in a typical city/cuisine combo. No anti-bot defeats required.

Detection

Signal Value
CDN Cloudflare (cf-ray, cf-cache-status: HIT)
Framework Custom SSR — React hydration payload in <script type="application/json" id="__REDUX_STATE__">
Anti-bot None on /category/ pages via curl. __cf_bm cookie is set but not enforced.
Auth Not required for SEO category pages
robots.txt Allows /category/

Architecture

Uber Eats ships a React SPA, but the /category// routes are fully server-rendered for SEO. Every store card is in the initial HTML as:

<a data-testid="store-card" href="/store/<slug>/<storeUuid>">
  <h3>Store Name</h3>
  ...
</a>

The parent div wraps: image (+srcset), name, ETA ("25 min"), price tier ("$", "$$"), optional promo badge ("Spend $35, Save $7"), and a "New" label (see Gotchas).

A window.__REDUX_STATE__ blob is also present but avoid it — it's embedded as a JSON-encoded string (with \u0022 for quotes) and contains a nested metaJson field that uses URL-encoding (%5C for backslash) which trips any stock JSON parser. Cheerio on the DOM is far cleaner.

Strategy used

  • Phase 0 (curl) ✅ — HTTP 200, 1.2 MB HTML, all 21 store cards per page rendered inline. Done.
  • Phase 1 — skipped (Phase 0 gate).
  • Phase 2 — not needed.

Fetch — the one gotcha

Uber's content-security-policy header is ~40KB of whitelisted domains. Node's built-in fetch (undici) throws UND_ERR_HEADERS_OVERFLOW because its default header size cap is 16 KB. Two options:

  1. Shell out to curl (simplest, what this skill uses).
  2. Or use undici with a custom Agent({ maxHeaderSize: 65536 }).
import { execFile } from 'node:child_process';
import { promisify } from 'node:util';
const execFileP = promisify(execFile);

async function fetchPage(url) {
  const { stdout } = await execFileP(
    'curl',
    ['-sL', '--compressed', '-A', UA, '-H', 'accept-language: en-US,en;q=0.9', url],
    { maxBuffer: 32 * 1024 * 1024 }
  );
  return cheerio.load(stdout);
}

Read the full file on GitHub · 146 lines

Changes

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.

  1. 2d ago First seen · 146 lines · 108 tokens per session scan A eb80b9c5a6cc

Subscribe to this mod's changes

ubereats is a skill published in the GitHub repository AgentComputerAI/torch (5 stars, last pushed 4mo ago), licensed MIT. It adds 108 tokens to every session and 1,972 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 2 findings (makes network calls, runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Related

Other skills, from other repositories

tiny-web-crawler

Crawl from one or more starting web pages, fetch readable content, search within pages, follow relevant links, and stop when the requested information is found or a bounded limit is reached.

leon-ai/leon · 42 tokens

playwright-cli

Automates browser interactions for web testing, form filling, screenshots, and data extraction. Use when the user needs to navigate websites, interact with web pages, fill forms, take screenshots, test web applications, or extract information from web pages.

VoltAgent/voltagent · 52 tokens

computer-use

Read and drive native desktop applications through the accessibility layer — list on-screen apps, snapshot one window as a numbered element tree, then click / type / set a value / scroll / drag / run a named action, by element index or by screen coordinates. Use for work in a desktop app rather than a web page. Full…

kirodotdev/KiroCrew · 105 tokens

feature-demo-recording

Record a demo video of a web feature from a real browser. Two modes -- a NARRATED film where measured voiceover drives the timeline (designed slides, subtitles, punch-in camera, rendered from an HTML timeline), and a SILENT evidence clip for a PR or a QA pass. Use when the user asks to record a video, demo, or screen…

kirodotdev/KiroCrew · 90 tokens

browserstack

../../../engineering-team/playwright-pro/skills/browserstack/SKILL.md.

alirezarezvani/claude-skills · 0 tokens

browser-recording

Record a browser flow as a video/GIF for evidence — animations, transitions, and multi-step interactions that a still screenshot cannot prove. Drives the project's own Playwright through a bundled runner, then converts to mp4 + GIF via ffmpeg. Use when the user asks to record a demo, capture a GIF or video of the UI…

kirodotdev/KiroCrew · 85 tokens