hf Summarize

    2

    Fast, reliable text summarization using the specialized BART-large-CNN model via Hugging Face.

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    Works with the AI tools you already use

    Claude CodeClaude CodeCursorCursorCodex CLICodex CLIGitHub CopilotGitHub CopilotGemini CLIGemini CLI+17 more

    See it in action

    You say

    Summarize the documentation on BART-large-CNN performance benefits and save the result to my local history.

    Your agent does

    --- SUMMARY --- The BART-large-CNN model effectively condenses complex technical documentation into readable summaries while maintaining factual integrity.

    Saved to: ~/.hf-summarize/summary_20231027_143005.json

    What you get

    Condense lengthy technical documentation into executive summariesArchive summarized research for easy retrieval and local loggingReduce context window usage by pre-summarizing large text filesAutomate high-quality summary generation for news or articles

    About this skill

    What it does

    This skill provides a high-performance summarization interface for your AI agent. It offloads heavy NLP processing to Hugging Face's dedicated BART-large-CNN model, allowing your agent to condense long technical documents or articles into concise summaries without consuming its own context window unnecessarily.

    Why use this skill

    Standard LLM prompts for summarization can be expensive and prone to "hallucinating" details from their own training data. This skill utilizes a specialized sequence-to-sequence model designed specifically for abstractive summarization. It offers granular control over output length via flags and automatically archives every summary into a local JSON store for historical reference and auditability.

    Supported tools

    • Python Requests: Communicates with the Hugging Face Inference API.
    • Local Storage: Automatically manages summary logs in a dedicated hidden directory.
    • File System: Directly reads local text files for bulk processing.

    The output includes the generated summary, a comparison of original vs. summary length, and the specific parameters used for the generation.

    How to install

    Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.

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