Collect, filter, summarize, and narrate daily news articles into podcasts.
npx skills add https://github.com/cnbison/lbc-skills --skill daily-news-cnbison
Please help me install this Agent Skill. Skill: daily-news Source: https://github.com/cnbison/lbc-skills/tree/main/skills/daily-news Command: npx skills add https://github.com/cnbison/lbc-skills --skill daily-news-cnbison
This Skill streamlines the process of collecting, filtering, summarizing, and producing daily news content automatically.
Invoke this Skill to generate a complete daily news report with an audio version by executing the full pipeline; simply tell your assistant "生成日报" or "run daily news".
High-intent search queries and answers about installing and using this skill.
Automated RSS news collection and podcast generation uses Python scripts with feedparser to gather and filter news feeds, AI models for content summarization, and edge-tts to synthesize audio, streamlining daily content creation into a single workflow.
Converting summarized news articles into natural dialogue podcast audio requires generating podcast scripts with AI agents, then synthesizing the audio output using edge-tts and pydub for audio processing and generation.
The automated news pipeline supports keyword and time window filtering to gather relevant content from multiple RSS sources. It uses feedparser and beautifulsoup4 to parse and filter news data before passing it to AI summarization agents.
Running the daily news report pipeline requires a Python environment with dependencies like requests, feedparser, beautifulsoup4, pyyaml, edge-tts, and pydub installed. Invoke the assistant with 'run daily news' to execute the full automated pipeline.
Creating daily news reports with an audio version automatically is best achieved by executing a full pipeline that collects RSS feeds, filters content by keywords, summarizes with AI agents, and generates podcast audio with TTS, reducing manual effort and increasing output efficiency.
TTS technology for producing news podcasts works by converting written articles and generated podcast scripts into engaging audio. The pipeline uses edge-tts to synthesize natural-sounding speech from summarized text, integrated with pydub for audio processing.
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