hf Sentiment

    3

    Automated sentiment analysis and batch text classification using Hugging Face's Inference API.

    $12

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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

    Analyze the sentiment for these three reviews: 'Build quality is amazing.', 'The software update ruined the battery life.', and 'Fast shipping and great support.'

    Your agent does

    [1] POSITIVE (99.8%) — Build quality is amazing. [2] NEGATIVE (85.4%) — The software update ruined the battery life. [3] POSITIVE (92.1%) — Fast shipping and great support.

    Results saved to: ~/.hf-sentiment/sentiment_20231024_120000.json

    What you get

    Classify customer feedback sentiment in bulk from local log files.Automate sentiment scoring for social media mentions or review datasets.Generate structured JSON datasets of text sentiment for machine learning.Perform fast, model-backed sentiment checks within local dev workflows.

    About this skill

    What it does

    This skill provides a high-performance sentiment analysis interface powered by Hugging Face's Inference API. It allows your AI agent to classify text as positive or negative with confidence scores, moving beyond simple guess-work to use industry-standard transformer models (DistilBERT).

    Why use this skill

    While LLMs can estimate sentiment, they are often inconsistent and expensive for high-volume tasks. This skill provides a structured, reproducible way to process data by offloading the heavy lifting to specialized sentiment models. It handles the API overhead, batch processing logic, and local storage of results, making it ideal for developers building data pipelines or monitoring tools.

    Supported features

    • Single & Batch Processing: Analyze individual strings or process multiple inputs in a single request for efficiency.
    • File-Based Workflows: Point the skill to a text file for line-by-line automated analysis.
    • Persistent Storage: All results are automatically versioned and saved as JSON in a local directory for later retrieval or auditing.
    • Low Latency: Utilizes the DistilBERT-SST-2 model for fast, accurate results without the overhead of a full GPT-4 call.

    How to install

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

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