feed-recommend

Unify multiple content streams into a single ranked feed.

Updated Aug 23, 2026
One-click install
npx skills add https://github.com/leonardofhy/openclaw-workspace --skill feed-recommend
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: feed-recommend
Source: https://github.com/leonardofhy/openclaw-workspace/tree/main/skills/feed-recommend
Command: npx skills add https://github.com/leonardofhy/openclaw-workspace --skill feed-recommend

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

The plugin-based multi-source feed recommender consolidates articles from Hacker News, Alignment Forum, LessWrong, arXiv, and custom sources, scoring them against Leo's interest profile to produce a unified, ranked feed.

Core Features & Use Cases

  • Plugin-based sources: each source is a small Python module that can be dropped in to extend the feed with new content.
  • Cross-source dedup and feedback loop: prevents duplicates and learns from user feedback to improve relevance.
  • Use Case: researchers monitor AI alignment and ML literature from multiple sources in a single feed.

Quick Start

Run the feed command to fetch, score, and view top recommendations across enabled sources.

Frequently Asked Questions about feed-recommend

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I aggregate multiple content streams into a single ranked feed?▼

You can aggregate multiple content streams into a single ranked feed by using a plugin-based recommender that fetches articles from sources like Hacker News, arXiv, and LessWrong, then scores them against a user profile. It applies cross-source deduplication to prevent duplicate entries.

Can I add a custom content source to a multi-source feed recommender?▼

Yes, you can add a custom content source by dropping a small Python plugin module into the sources directory. This extends the feed recommender to fetch and score new content streams alongside existing sources.

How does a scoring pipeline rank feed items against a user profile?▼

The scoring pipeline ranks feed items by evaluating fetched articles against a defined user interest profile. It leverages this profile to filter and order content, producing a unified feed tailored to specific research topics like AI alignment.

What is the best way to monitor AI alignment literature across HN, AF, LW, and arXiv?▼

The best way to monitor AI alignment literature is to use a unified feed recommender that consolidates streams from Hacker News, Alignment Forum, LessWrong, and arXiv. It scores items against your interest profile and learns from feedback to improve relevance.

Do I need Python to run a plugin-based feed recommender?▼

Yes, you need Python installed to run the plugin-based feed recommender. Each content source operates as a Python module, and the scoring pipeline requires a Python environment to fetch, deduplicate, and rank feed items.