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Hugging Face Papers Lookup

hugging-face-papers

sickn33/agentic-awesome-skills

Look up Hugging Face paper pages as markdown and query the papers API for structured metadata including authors, linked models, datasets, Spaces, GitHub repos, and project pages. Ideal for summarizing or analyzing AI research papers from hf.co or arXiv URLs and IDs.

ML Experiment Tracking with Trackio

hugging-face-trackio

sickn33/agentic-awesome-skills

Trackio is a library for logging and visualizing machine learning training metrics. It supports Python API for logging metrics and firing alerts during training, and CLI for retrieving data. Metrics sync to Hugging Face Spaces for real-time dashboards. Ideal for autonomous ML workflows and experiment management.

Hugging Face Vision Model Trainer

hugging-face-vision-trainer

sickn33/agentic-awesome-skills

Train object detection, image classification, and SAM/SAM2 segmentation models locally or on Hugging Face Jobs with dataset validation and Hub saving.

HuggingFace Best Model Finder

huggingface-best

sickn33/agentic-awesome-skills

This skill helps users find the best AI models for specific tasks by querying Hugging Face benchmark leaderboards. It analyzes hardware constraints like GPU VRAM or CPU memory to filter compatible models. The output includes a comparison table with benchmark scores, parameter sizes, licenses, and device compatibility status. It also suggests whether models can run locally or require API access.

Hugging Face Local Model Evaluation

huggingface-community-evals

sickn33/agentic-awesome-skills

This skill guides users on running evaluations against models on the Hugging Face Hub using local hardware. It covers using inspect-ai and lighteval frameworks with local inference backends like vllm, Transformers, or accelerate. It includes task selection, smoke testing, and hardware guidance for different model sizes. It does not cover remote orchestration or publishing results to the community workflow.

Hugging Face Local Models

huggingface-local-models

sickn33/agentic-awesome-skills

This skill helps you select and run large language models locally using llama.cpp and GGUF formats. It covers finding compatible models on the Hugging Face Hub, choosing quantization levels for different hardware like CPU, Mac Metal, CUDA, or ROCm, and launching servers via llama-cli or llama-server. It also includes instructions for converting weights and verifying OpenAI-compatible local serving endpoints.

Gradio LoRA Space Builder

huggingface-lora-space-builder

sickn33/agentic-awesome-skills

Build and publish a Gradio demo on Hugging Face Spaces for a user-provided LoRA. The skill generates a custom interactive app with the right controls, loading, and inference recipe, then publishes it as a private Space.

Hugging Face Spaces Deployment

huggingface-spaces

sickn33/agentic-awesome-skills

Build, deploy, and maintain machine-learning applications on Hugging Face Spaces using Gradio, Docker, or Static SDKs, with ZeroGPU and dedicated hardware, model loading, debugging, and inference providers.

Hugging Face ZeroGPU

huggingface-zerogpu

sickn33/agentic-awesome-skills

Rules and patterns for building AI demos on Hugging Face Spaces with ZeroGPU hardware, covering @spaces.GPU, quota tuning, CUDA constraints, and concurrency safety.

Human Agent Trust Exploit Detection

human-agent-trust-exploit-detection

Tencent/AI-Infra-Guard

This skill detects when AI agents exploit human trust through social engineering, deceptive guidance, or false assurances. It identifies prompts that induce unsafe actions, request sensitive credentials, or impersonate authority figures. Use this when evaluating agent safety, security advice, or decision-influencing workflows. Skip for static information.

LLM AI Feature Bug Hunting

sickn33/agentic-awesome-skills

This skill focuses on identifying security vulnerabilities in Large Language Model (LLM) and AI features. It covers prompt injection, system prompt leakage, cross-tenant access, and data exfiltration techniques. Users must verify findings against false positives using reproducibility checks and out-of-band callbacks before reporting.

Hybrid Search Implementation Guide

hybrid-search-implementation

sickn33/agentic-awesome-skills

This skill provides structured patterns for combining vector similarity search with traditional keyword-based search. It is essential for building advanced Retrieval-Augmented Generation (RAG) systems, sophisticated search engines, or any retrieval task requiring high recall. Use this when pure semantic understanding is insufficient, and precise matching of specific terms (like names or codes) is necessary.

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