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.
npx skills add Kilo-Org/kilo-marketplace --skill cheerio-parsinggit clone --depth 1 https://github.com/Kilo-Org/kilo-marketplaceWrote 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/skills/kilo-org/kilo-marketplace/cheerio-parsing)<a href="https://agentmods.dev/skills/kilo-org/kilo-marketplace/cheerio-parsing"><img src="https://agentmods.dev/badge/skills/kilo-org/kilo-marketplace/cheerio-parsing/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/skills/kilo-org/kilo-marketplace/cheerio-parsing"><img src="https://agentmods.dev/badge/skills/kilo-org/kilo-marketplace/cheerio-parsing.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 | $0.00032 | $0.02192 |
| Opus 5 | $0.00016 | $0.01096 |
| Sonnet 5 | $0.00006 | $0.00438 |
| Haiku 4.5 | $0.00003 | $0.00219 |
Grade A, and why
cheerio-parsing 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 12d 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.
const response = await axios.get(url); This is a copy
88% identical to cheerio-parsing — 12 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 385 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cheerio HTML Parsing
You are an expert in Cheerio, Node.js HTML parsing, DOM manipulation, and building efficient data extraction pipelines for web scraping.
Core Expertise
- Cheerio API and jQuery-like syntax
- CSS selector optimization
- DOM traversal and manipulation
- HTML/XML parsing strategies
- Integration with HTTP clients (axios, got, node-fetch)
- Memory-efficient processing of large documents
- Data extraction patterns and best practices
Key Principles
- Write clean, modular extraction functions
- Use efficient selectors to minimize parsing overhead
- Handle malformed HTML gracefully
- Implement proper error handling for missing elements
- Design reusable scraping utilities
- Follow functional programming patterns where appropriate
Basic Setup
npm install cheerio axios
Loading HTML
const cheerio = require('cheerio');
const axios = require('axios');
// Load from string
const $ = cheerio.load('<html><body><h1>Hello</h1></body></html>');
// Load with options
const $ = cheerio.load(html, {
xmlMode: false, // Parse as XML
decodeEntities: true, // Decode HTML entities
lowerCaseTags: false, // Keep tag case
lowerCaseAttributeNames: false
});
// Fetch and parse
async function fetchAndParse(url) {
const response = await axios.get(url);
return cheerio.load(response.data);
}
Selecting Elements
CSS Selectors
// By tag
$('h1')
// By class
$('.article')
// By ID
$('#main-content')
// By attribute
$('[data-id="123"]')
$('a[href^="https://"]') // Starts with
$('a[href$=".pdf"]') // Ends with
$('a[href*="example"]') // Contains
// Combinations
$('div.article > h2') // Direct child
$('div.article h2') // Any descendant
$('h2 + p') // Adjacent sibling
$('h2 ~ p') // General sibling
// Pseudo-selectors
$('li:first-child')
$('li:last-child')
$('li:nth-child(2)')
$('li:nth-child(odd)')
$('tr:even')
$('input:not([type="hidden"])')
$('p:contains("specific text")')
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 12d ago First seen · 385 lines · 32 tokens per session scan A 1c4350249675
cheerio-parsing is a skill published in the GitHub repository Kilo-Org/kilo-marketplace (175 stars, last pushed 22d ago), licensed Apache-2.0. It adds 32 tokens to every session and 2,192 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 88% identical to cheerio-parsing, differing in 12 lines, and is treated as a copy.
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