Normalize and filter noisy URL lists before crawling or queueing
Uses Courlan to clean, normalize, de-track, and language-filter raw URL inventories before a crawler, scraper, or analyst queue touches them. Best when an agent already has too many candidate links and needs a smaller, cleaner frontier, not a full crawling stack.
npx skills add agentskillexchange/skills --skill normalize-and-filter-noisy-url-lists-before-crawling-or-queueing
This skill uses Courlan, the Python and command-line URL cleaning toolkit from the adbar/courlan project, to turn messy link inventories into a crawl-ready set of URLs. An agent invokes it when it has already collected a large batch of candidate links from sitemaps, search results, archives, scraped HTML, or exports and needs to normalize them before any expensive follow-up work begins. Courlan is especially useful for stripping trackers, normalizing domains and paths, filtering bogus or low-value URLs, applying language-aware heuristics, and reducing duplicate queue entries before a crawler or enrichment pipeline burns bandwidth on the wrong pages.
What this skill actually does
The job-to-be-done here is narrow on purpose: the agent is not acting as a general crawler, ranking system, or SEO platform. It is acting as a URL frontier cleaner. That scope boundary is what keeps this entry skill-shaped instead of turning it into a generic library card. If the user needs page rendering, extraction, login handling, screenshotting, or broad crawling orchestration, this is the wrong tool. If the user already has candidate URLs and needs a trustworthy cleanup pass before queueing or sampling them, this is the right tool.
Typical integrations include placing Courlan between sitemap discovery and page fetch, between search result collection and downstream scraping, or before deduped review queues are handed to summarizers and classifiers. Upstream evidence is strong: the official GitHub repository exists, PyPI package metadata exists, the project exposes a license, and the repository remains actively maintained. Installation from upstream is straightforward with pip install courlan.