一键导入
summarize
Summarize or extract text/transcripts from URLs, podcasts, and local files (great fallback for “transcribe this YouTube/video”).
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
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Summarize or extract text/transcripts from URLs, podcasts, and local files (great fallback for “transcribe this YouTube/video”).
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
为 acadclaw 调度整条 ECE 学术研究链路,决定何时调用检索、综述、研究空白、实现、实验、写作、引用和评审类 skills。Full-chain pipeline with concrete tool calls for literature search, RAG, experiments, statistics, plotting, writing, review, and citation management.
Specialized in writing research experiment code. Use when spawning a subagent to implement simulations, numerical models, data processing, or statistical analysis. Produces well-structured, documented Python code with proper error handling and result persistence.
学术关系构建指南。指导撰写 Cold Email、会议社交以及建立学术影响力。
Specialized in reviewing academic papers and providing structured feedback. Use when spawning a subagent for peer review, quality assessment, or iterative improvement of academic writing. Triggers: review, 审稿, feedback, critique, improve, 迭代, revise.
面向中文管理类硕士学位论文的多轮评审技能,尤其适用于 MBA、MEM、MPA,也可扩展到相近的专业型与应用研究型论文。
用于润色面向顶级计算机学术会议的研究写作(如 NeurIPS、ICLR、ICML、AAAI、IJCAI、ACL、EMNLP、NAACL、CVPR、WWW、KDD、SIGIR、CIKM 等)。当用户要求改进、润色、精修、编辑或校对学术写作时触发,包括论文草稿、摘要、引言、相关工作、方法描述、实验部分或结论部分;当用户粘贴 LaTeX 内容并请求写作帮助,或提到 camera-ready、rebuttal、paper revision、具体学术会议名称时也应触发。该技能同时支持整篇论文润色与分章节编辑。
| name | summarize |
| description | Summarize or extract text/transcripts from URLs, podcasts, and local files (great fallback for “transcribe this YouTube/video”). |
| homepage | https://summarize.sh |
| metadata | {"acadclaw":{"emoji":"🧾","requires":{"bins":["summarize"]},"install":[{"id":"brew","kind":"brew","formula":"steipete/tap/summarize","bins":["summarize"],"label":"Install summarize (brew)"}]}} |
Fast CLI to summarize URLs, local files, and YouTube links.
Use this skill immediately when the user asks any of:
yt-dlp needed)summarize "https://example.com" --model google/gemini-3-flash-preview
summarize "/path/to/file.pdf" --model google/gemini-3-flash-preview
summarize "https://youtu.be/dQw4w9WgXcQ" --youtube auto
Best-effort transcript (URLs only):
summarize "https://youtu.be/dQw4w9WgXcQ" --youtube auto --extract-only
If the user asked for a transcript but it’s huge, return a tight summary first, then ask which section/time range to expand.
Set the API key for your chosen provider:
OPENAI_API_KEYANTHROPIC_API_KEYXAI_API_KEYGEMINI_API_KEY (aliases: GOOGLE_GENERATIVE_AI_API_KEY, GOOGLE_API_KEY)Default model is google/gemini-3-flash-preview if none is set.
--length short|medium|long|xl|xxl|<chars>--max-output-tokens <count>--extract-only (URLs only)--json (machine readable)--firecrawl auto|off|always (fallback extraction)--youtube auto (Apify fallback if APIFY_API_TOKEN set)Optional config file: ~/.summarize/config.json
{ "model": "openai/gpt-5.2" }
Optional services:
FIRECRAWL_API_KEY for blocked sitesAPIFY_API_TOKEN for YouTube fallback