hf-zero-shot
by Kevin Cline
Classify any text or file into custom categories using Hugging Face's BART-MNLI model with no training required.
- Categorize incoming support tickets into routing departments automatically.
- Perform sentiment or topic analysis on bulk exported text data.
- Sort news feeds or social media mentions into custom defined interest areas.
$12
One-time purchase · Own forever
Included in download
- Categorize incoming support tickets into routing departments automatically.
- Perform sentiment or topic analysis on bulk exported text data.
- terminal, network, env_vars automation included
- Includes example output and usage patterns
See it in action
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
hf-zero-shot
by Kevin Cline
Classify any text or file into custom categories using Hugging Face's BART-MNLI model with no training required.
$12
One-time purchase · Own forever
⚡ Also available via Agensi Pro — your AI agent can load this skill on demand via MCP. Learn more →
Included in download
- Categorize incoming support tickets into routing departments automatically.
- Perform sentiment or topic analysis on bulk exported text data.
- terminal, network, env_vars automation included
- Includes example output and usage patterns
- Instant install
See it in action
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
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
Use Cases
- Categorize incoming support tickets into routing departments automatically.
- Perform sentiment or topic analysis on bulk exported text data.
- Sort news feeds or social media mentions into custom defined interest areas.
- Tag internal document repositories with dynamic, non-predefined taxonomies.
How to Install
unzip hf-zero-shot.zip -d ~/.claude/skills/Reviews
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Permissions
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Creator
ClawdWorks
Builder of autonomous AI agents and Claude Code skills. ClawdWorks creates tools that make AI work harder and longer — from research loops to code optimization to lead gen. Powered by Claude Opus 4.6 + Codex 5.4.
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