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Summarize or extract text/transcripts from URLs, podcasts, and local files (great fallback for “transcribe this YouTube/video”).
Instalar con Codex o Claude Copia este prompt, pégalo en Codex, Claude u otro asistente, y deja que revise la página de la skill y la instale por ti.
Basado en la clasificación ocupacional SOC
| 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 | {"clawphd":{"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 fallbackCLI tool (arxivterminal) for fetching, searching, and managing arXiv papers locally. Use when working with arXiv papers using the arxivterminal command - fetching new papers by category, searching the local database, viewing papers from specific dates, or managing the local paper database.
Fetch arXiv papers by date range and topics, rank them for research value, and produce introduction digests. Use when the user wants a literature sweep, daily or weekly paper triage, or a written overview of the best papers in a niche — without relying on the arxivterminal local database.
Search and analyze research papers, find related work, summarize key ideas. Use when user says "find papers", "related work", "literature review", "what does this paper say", or needs to understand academic papers.
AI paper reviewer. Use when the user says 'review my paper', 'help me review this paper', '审稿', 'give me feedback on my paper', 'check my manuscript', 'evaluate this paper for NeurIPS/ICLR/EuroSys'. Accepts PDF files and produces structured narrative reviews with venue-specific dimensional scores and Accept/Reject recommendation.
Convert a local paper PDF to structured Markdown and export all figures as PNG + SVG + drawio. Attempts editable figure reconstruction via the built-in autofigure pipeline (SAM3 → RMBG-2.0 → VLM → SVG), falling back to a layered-SVG wrapper when API keys are unavailable. Use when the user wants to parse a paper PDF, extract its text as Markdown, or get editable/exportable figure assets.
Convert raster figure images into editable DrawIO files using SAM3 segmentation, RMBG-2.0 background removal, and multimodal LLM drawio generation.