No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by evandempsey · Agent Tool · ★ 151
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is podcast-llm safe to install? View the security audit →
Podcast-LLM: AI-Powered Podcast Generation An intelligent system that automatically generates engaging podcast conversations using LLMs and text-to-speech technology. View Documentation Features Two modes of operation: Research mode: Automated research and content gathering using Tavily search Context mode: Generate podcasts from provided source materials (URLs and files) Dynamic podcast outline generation Natural conversational script writing with multiple Q&A rounds High-quality text-to-speech synthesis using Google Cloud or ElevenLabs Checkpoint system to save progress and resume generation Configurable
| Stars | 151 |
| Forks | 28 |
| Language | Python |
| Category | Agent Tool |
| Quality Score | 69.1003528534119/100 |
| Open Issues | 2 |
| Last Updated | 2025-07-28 |
| Created | 2024-10-28 |
| Platforms | gemini, python |
| Est. Tokens | ~422k |
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podcast-llm is Automatically generate engaging AI podcasts from nothing but an episode title.. It is categorized as a Agent Tool with 151 GitHub stars.
podcast-llm is primarily written in Python. It covers topics such as anthropic, elevenlabs, gemini.
You can find installation instructions and usage details in the podcast-llm GitHub repository at github.com/evandempsey/podcast-llm. The project has 151 stars and 28 forks, indicating an active community.
The top alternatives to podcast-llm on Agent Skills Hub include OpenReels, UnrealGenAISupport, c4-genai-suite. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: