SKILLEMALL.ai

AC crawlee

Expert guide for building web scrapers and crawlers using Crawlee (JavaScript/TypeScript and Python). Use this skill whenever the user wants to: scrape a website, build a web crawler, extract data from web pages, automate browser navigation, handle anti-bot blocking, manage proxies or sessions for scraping, use Playwright/Puppeteer/Cheerio/BeautifulSoup for web data extraction, crawl sitemaps, download files from URLs, or deploy a scraper to the cloud. Trigger even for loosely related phrases like "get data from a website", "automate browser", "scrape prices", "extract links", "crawl URLs", or "bypass bot detection". Covers CheerioCrawler, PlaywrightCrawler, PuppeteerCrawler, HttpCrawler, JSDOMCrawler (JS), and BeautifulSoupCrawler, ParselCrawler, PlaywrightCrawler (Python).

ClawHub Agent Skills author: Yash Kavaiya v1.0.0 MIT-0 4 files body ≈ 4 516 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process C 53/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

GeneratorPlaywrightSoftware developmentInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
89
Run on models
none yet
Process rating
C
53/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 0

    ✓ No critical or high findings

    Files scanned: 4. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 53/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 5 mutating operations with no state check
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 70Execution cost. Instruction body is 4516 tokens
    • 100Steps. 20 steps
    • 100Consistency. Name and required fields are in place
    • low 18 top-level sections: this looks like several domains in one skill

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • -2localhost URLs: will not work for another user
    • -213 emoji in the instructions: noise for the model
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 6 example trigger phrases
    • +3Description length 785: enough signal without eating the budget
    • +4Structure: 43 headings
    • +3Step-by-step instructions: 20 items
    • +4Has examples (39 code blocks)
    • +4Reference files are cited in the instructions (2 of 2)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.

    External checks

    ClawHub: clean
    This is a disclosed Crawlee web-scraping guide, with some caution needed around anti-blocking and proxy guidance.
    LLM: benign (high) · VirusTotal: · 29 May 2026