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Scientific Podcast Summary

作者 AIpoch · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ⚠ suspicious
91
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0
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当前安装
1
版本数
在 OpenClaw 中安装
/install scientific-podcast-summary
功能描述
Automatically summarize scientific podcasts like Huberman Lab and Nature.
安全使用建议
Key things to consider before installing or running: - The script requires an LLM API key (OPENAI_API_KEY) even though the registry metadata lists no required env vars — treat the mismatch as a red flag and confirm the author/publisher before supplying credentials. - Running the skill will fetch and scrape podcast pages and send up to ~15k characters of text to the configured LLM endpoint. Do not supply sensitive or private content as input or allow the script to run on internal links. - Review scripts/main.py locally (it is included) to verify there are no hidden endpoints or surprises. Running python -m py_compile scripts/main.py and python scripts/main.py --help are reasonable sanity checks (both are suggested by the package). - Consider running the tool in an isolated environment (container or VM) and use a scoped API key with limited quota if possible. If your organization can provide an allow-listed endpoint or an audit log for the API key, prefer that. - Confirm whether you want the default OPENAI_BASE_URL (https://api.openai.com/v1) or a custom base URL; the script will send content to whatever OPENAI_BASE_URL you set. If you need a safer test: run the script with a fake OPENAI_API_KEY to confirm it fails cleanly, and inspect the output/fallback behavior. If you plan to use it in production, request the author to fix the registry metadata so required env vars are declared and consider adding explicit confirmation prompts before sending data to the LLM.
功能分析
Type: OpenClaw Skill Name: scientific-podcast-summary Version: 1.0.0 The skill bundle is a legitimate tool designed to summarize scientific podcasts from Huberman Lab and Nature. The core logic in `scripts/main.py` uses standard libraries (requests, BeautifulSoup, and openai) to fetch web content and process it via a user-configured LLM API. There is no evidence of malicious intent, data exfiltration, or unauthorized system access; the documentation in `SKILL.md` even includes proactive security checklists and scope-limiting instructions to prevent misuse.
能力评估
Purpose & Capability
Name/description (summarize scientific podcasts) match the implementation: the package contains parsers for Huberman and Nature and a summary workflow that calls an LLM. However the registry metadata claims no required environment variables while SKILL.md and scripts/main.py require an OPENAI_API_KEY (and optionally OPENAI_BASE_URL / OPENAI_MODEL). That mismatch is an incoherence between declaration and actual capability.
Instruction Scope
SKILL.md instructs running scripts/main.py and documents the OPENAI_* env vars; the script scrapes remote podcast pages, reads environment variables, and sends scraped text to an external LLM API. That scope is appropriate for a summarizer, but SKILL.md and the code allow sending up to ~15k characters of scraped content to the LLM — a data-exfiltration / privacy risk if the content contains sensitive material. Also SKILL.md claims required env vars absent from registry metadata, granting the agent understated privileges.
Install Mechanism
No install spec in the registry (instruction-only) and dependencies are standard Python packages (requests, beautifulsoup4, openai). This is low install risk compared to arbitrary downloads or extract/install steps.
Credentials
The script legitimately needs an API key for an LLM (OPENAI_API_KEY). That single credential is proportionate to the stated purpose, but the registry says 'no required env vars' while SKILL.md and scripts/main.py require OPENAI_API_KEY (and optionally OPENAI_BASE_URL and OPENAI_MODEL). This omission is a notable inconsistency and could mislead users about what secrets the skill will use. No other unrelated credentials are requested.
Persistence & Privilege
The skill is not marked always:true and does not request persistent system-wide privileges. It is an on-demand script that reads/writes files (output path) and performs network access to scrape pages and call an LLM; these are normal for its purpose.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install scientific-podcast-summary
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /scientific-podcast-summary 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.0
scientific-podcast-summary v1.0.0 - Initial release of scientific podcast summarizer. - Supports Huberman Lab and Nature Podcast as input sources. - Generates structured summaries in markdown or JSON format. - Extracts key episode information: title, date, host/guest, main topics, scientific findings, practical advice, and resource links. - Includes command-line argument support for podcast choice, direct URL input, output file path, and output format.
元数据
Slug scientific-podcast-summary
版本 1.0.0
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 1
常见问题

Scientific Podcast Summary 是什么?

Automatically summarize scientific podcasts like Huberman Lab and Nature. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 91 次。

如何安装 Scientific Podcast Summary?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install scientific-podcast-summary」即可一键安装,无需额外配置。

Scientific Podcast Summary 是免费的吗?

是的,Scientific Podcast Summary 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Scientific Podcast Summary 支持哪些平台?

Scientific Podcast Summary 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 Scientific Podcast Summary?

由 AIpoch(@aipoch-ai)开发并维护,当前版本 v1.0.0。

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