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 Agents you already use

    Claude CodeClaude CodeCursorCursorCodex CLICodex CLIMuseMuseDotsDots+21 more

    hf-summarize

    Example session with this skill installed

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

    • Read your context and instructions
    • Compiled the hf-summarize

    --- 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

    Connects securely to your tools. The creator never sees your data.

    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granular control over output length via flags and automatically archives every summary into a local JSON store for historical reference and auditability.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.

    • Requires active HF_TOKEN environment variable.
    • 503 errors occur if model is cold-starting.
    • Hugging Face free tier rate limits and input token windows apply.

    How to use

    1. 1

      Connect your agent to the Agensi MCP

      Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup

    2. 2

      Sign in to Agensi

      Your agent shows a sign-in link. Sign in and click Allow.

    3. 3

      Ask for what you need

      Your agent loads this skill and uses it on your task. It always asks before any payment.

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    Permissions

    Terminal / Shell
    Network Access
    Environment Variables
    Listed7 months ago

    What's inside

    Frequently Asked Questions

    This skill summarizes long documents using a specialized BART model, which reduces token usage in your main LLM's context window and minimizes the hallucination risks often found in general-purpose models.

    The skill is designed for ease of use via Python and is compatible with any AI agent or framework capable of making HTTP requests and interacting with a local file system.

    You will receive the core integration script, a comprehensive setup guide for connecting your Hugging Face API key, and the local storage module for archiving your summaries.

    All summary outputs are automatically logged to a hidden local JSON directory, providing a permanent, searchable audit trail of your processed documents without requiring an external database.

    Yes, the skill allows you to pass specific flags to define minimum and maximum length constraints, giving you granular control over how concise or detailed the final output will be.

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