# Scientific Podcast Summary

> Automatically summarize scientific podcasts like Huberman Lab and Nature Podcast

- Skill: `majiayu000/scientific-podcast-summary` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds add majiayu000/scientific-podcast-summary`
- Raw SKILL.md: https://api.skillmd.com/api/skills/majiayu000/scientific-podcast-summary/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Research & Search
- License: MIT
- Author: majiayu000 (https://skillmd.com/u/majiayu000)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/majiayu000/scientific-podcast-summary

---


# Scientific Podcast Summary

**ID:** 189  
**Version:** 1.0.0  
**Description:** Automatically summarizes core content from Huberman Lab or Nature Podcast, generating text briefings.

---

## Usage

```bash
# Summarize latest episode
python skills/scientific-podcast-summary/scripts/main.py --podcast huberman

# Specify episode URL
python skills/scientific-podcast-summary/scripts/main.py --url "https://..."

# Save to file
python skills/scientific-podcast-summary/scripts/main.py --podcast nature --output ./summary.md
```

## Arguments

| Argument | Required | Default | Description |
|----------|----------|---------|-------------|
| `--podcast` | Optional | huberman | Select podcast source: `huberman` or `nature` |
| `--url` | Optional | - | Directly provide podcast page URL |
| `--output` | Optional | - | Output file path |
| `--format` | Optional | markdown | Output format: `markdown`, `json` |

## Output Format

Generated briefing contains:
- 🎙️ Podcast title and release date
- 👤 Host and guest information
- 📝 Core topic overview
- 🔬 Key scientific findings/points (3-5 items)
- 💡 Practical advice/action guidelines
- 📚 Related resource links

## Dependencies

- Python 3.8+
- requests
- beautifulsoup4
- openai (or compatible API)

## Installation

```bash
pip install requests beautifulsoup4 openai
```

## Environment Variables

| Variable | Required | Description |
|----------|----------|-------------|
| `OPENAI_API_KEY` | Yes | LLM API Key |
| `OPENAI_BASE_URL` | No | Custom API Base URL |
| `OPENAI_MODEL` | No | Model name, default `gpt-4o-mini` |

## Example Output

```markdown
# 🎙️ Huberman Lab: The Science of Sleep

**Release Date:** 2024-01-15  
**Guest:** Dr. Matthew Walker

## 📝 Core Topic

This episode delves into the neuroscience mechanisms of sleep...

## 🔬 Key Points

1. **Sleep Cycles** - Humans experience 4-6 90-minute sleep cycles each night...
2. **Importance of Deep Sleep** - During deep sleep, the brain clears metabolic waste...

## 💡 Practical Advice

- Maintain regular sleep schedule
- Avoid blue light exposure before bed
- Keep room temperature at 18-20°C
```

---

## Changelog

### v1.0.0 (2024-02-06)
- Initial release
- Support for Huberman Lab and Nature Podcast
- Support for Markdown/JSON output formats

## Risk Assessment

| Risk Indicator | Assessment | Level |
|----------------|------------|-------|
| Code Execution | Python scripts with tools | High |
| Network Access | External API calls | High |
| File System Access | Read/write data | Medium |
| Instruction Tampering | Standard prompt guidelines | Low |
| Data Exposure | Data handled securely | Medium |

## Security Checklist

- [ ] No hardcoded credentials or API keys
- [ ] No unauthorized file system access (../)
- [ ] Output does not expose sensitive information
- [ ] Prompt injection protections in place
- [ ] API requests use HTTPS only
- [ ] Input validated against allowed patterns
- [ ] API timeout and retry mechanisms implemented
- [ ] Output directory restricted to workspace
- [ ] Script execution in sandboxed environment
- [ ] Error messages sanitized (no internal paths exposed)
- [ ] Dependencies audited
- [ ] No exposure of internal service architecture
## Prerequisites

```bash
# Python dependencies
pip install -r requirements.txt
```

## Evaluation Criteria

### Success Metrics
- [ ] Successfully executes main functionality
- [ ] Output meets quality standards
- [ ] Handles edge cases gracefully
- [ ] Performance is acceptable

### Test Cases
1. **Basic Functionality**: Standard input → Expected output
2. **Edge Case**: Invalid input → Graceful error handling
3. **Performance**: Large dataset → Acceptable processing time

## Lifecycle Status

- **Current Stage**: Draft
- **Next Review Date**: 2026-03-06
- **Known Issues**: None
- **Planned Improvements**: 
  - Performance optimization
  - Additional feature support