hf Zero Shot

    2

    Classify any text or file into custom categories using Hugging Face's BART-MNLI model with no training required.

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    Claude CodeClaude CodeCursorCursorCodex CLICodex CLIMuseMuseDotsDots+21 more

    hf-zero-shot

    Example session with this skill installed

    Classify "The quarterly earnings beat expectations" using labels: finance, politics, sports, entertainment.

    • Read your context and instructions
    • Compiled the hf-zero-shot

    TEXT: The quarterly earnings beat expectations
    TOP: finance (98.2%)
    finance 98.2% ###################
    politics 1.1%
    sports 0.4%
    entertainment 0.3%

    Results saved to: ~/.hf-zero-shot/zero_shot_20231027_120000.json

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

    About this skill

    Automated Text Classification via BART-MNLI

    This skill provides a robust, zero-shot text classification engine for AI agents. By leveraging the Facebook BART-Large-MNLI model via Hugging Face's Inference API, it solves the problem of categorizing unstructured data without the need for custom training data or fine-tuning. It allows developers to define dynamic taxonomies on the fly and receive confidence-scored results instantly.

    What it does

    At a high level, the skill takes input text or files and maps them against a customizable list of labels. It handles API communication, model loading states, and result persistence. Unlike raw prompting, which can be inconsistent or hallucinate labels, this skill uses a specialized NLI (Natural Language Inference) model specifically architected for cross-label entailment.

    Why use this skill

    • Consistency: Returns structured, mathematical confidence scores for every label provided.
    • Scale: Processes individual strings or batch processes entire text files via a simple flag.
    • Persistence: Automatically logs every classification run to a local JSON database (~/.hf-zero-shot/) for audit trails or further analysis.
    • Efficiency: Uses specialized inference endpoints rather than general LLM tokens for classification tasks.

    Supported Tools

    • Hugging Face Inference API (BART-Large-MNLI)
    • Python-based execution for local data security
    • JSON-based structured output

    • Requires active internet for HF Inference API.
    • Rate limits apply to free tokens.
    • Cold starts may cause 503 errors (20-30s delay).

    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
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    Listed7 months ago

    What's inside

    Frequently Asked Questions

    Unlike general LLMs that can hallucinate or change formats, this skill uses a specialized BART-MNLI model that provides mathematical confidence scores for fixed labels, ensuring consistent and structured data output every time.

    No, that is the core benefit of this skill; you can provide any custom text labels on the fly, and the model will intelligently determine the best fit based on semantic meaning without any prior training.

    Installation is straightforward via your terminal, requiring only a Hugging Face API key and a local Python environment to handle the data processing and local JSON logging.

    Every classification event is automatically recorded to a structured JSON database in a local directory, allowing you to audit results, track confidence scores over time, or export data for further analysis.

    The skill is designed with a flag that allows it to switch between processing individual text strings and scanning entire files, making it ideal for both real-time queries and bulk document processing.

    Your purchase includes the full skill execution logic, integration scripts for the Hugging Face Inference API, and lifetime access to any future performance updates or model compatibility patches.

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