Extract resource recommendations from HackerNews threads into a sequenced learning path.
npx skills add https://github.com/Elijah-J/InfoScraper --skill hn-rabbit-hole
Please help me install this Agent Skill. Skill: hn-rabbit-hole Source: https://github.com/Elijah-J/InfoScraper/tree/main/.agents/skills/hn-rabbit-hole Command: npx skills add https://github.com/Elijah-J/InfoScraper --skill hn-rabbit-hole
Build a structured, topic-specific learning path by mining HackerNews discussions for community-recommended resources and sequencing them by prerequisites.
Provide a topic; the system will fetch relevant HN threads, extract recommended resources, and assemble a sequenced learning path.
High-intent search queries and answers about installing and using this skill.
To build a learning path from HackerNews threads, provide a topic and the system archives 5-8 substantive threads, fetches full comment trees, and extracts URLs to sequence resources by prerequisites. Resources are ranked by authority signals to form a structured path.
Resource curation from community-sourced discussions works by extracting URLs and resource names from high-signal comments, tagging each with its source thread ID and author, and filtering out low-signal, self-promotional, or dead links based on currency and subtopic coverage.
Yes, you can extract learning resources from HackerNews comments for specific subtopics. The system respects subtopic coverage when archiving threads and extracts resource recommendations from top-level and high-signal comments to ensure relevant material is captured.
The best way to rank extracted resources by prerequisites is to evaluate authority signals from the comments and cross-reference findings against local docs. This rates resources to form a sequenced learning path adaptable to different knowledge levels.
During thread analysis, dead links and self-promotional content are avoided entirely. The system filters low-signal comments and respects currency to ensure only substantive, community-recommended resources are archived and included in the learning path.
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