Fast Summarization CLI
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Please install @user_7d510643/meta-v1 according to https://skillhub.cn/install/skillhub.md
About this skill
Problem
meta-v2 targets a narrow workflow: you have a web page, a local file, or a YouTube link and need a readable summary without manually opening, copying, and pasting the content into a model. It bundles content retrieval, model selection, and output length control into a CLI, which fits scripts, note pipelines, or batch research curation.
How It Works
The core path is to use an external LLM for summarization:
- Model setup: set an environment variable such as OPENAI_API_KEY, ANTHROPIC_API_KEY, XAI_API_KEY, or GEMINI_API_KEY; if none is set, the default is google/gemini-3-flash-preview.
- Inputs: supports URLs, local files, and YouTube links; --extract-only limits processing to URL extraction.
- Output control: use --length for summary length, --max-output-tokens to cap output, and --json for machine-readable results.
- Fallbacks: --firecrawl can enable Firecrawl when page extraction is blocked, and --youtube can use Apify when APIFY_API_TOKEN is set.
An optional config file is ~/.summarize/config.json.
Boundaries
It depends on LLM API keys and is not a fully offline tool. YouTube and protected sites may require extra service tokens. The default model includes preview, so production use should track model availability.
Use Cases
- Turn multiple technical blog URLs into short summaries to decide what to read next.
- Summarize local README files, logs, or exported text into weekly update points.
- Generate study summaries from YouTube technical talks for review, archiving, and sharing.
- Extract web page content into JSON in scripts for data collection and cleanup.
Best For
- Technical writers who compress reference links and local drafts into outlines.
- Product managers comparing competitors from multiple web pages into digestible points.
- Automation engineers extracting web pages and emitting JSON in pipelines.
- Learners turning YouTube lectures into readable study notes.
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