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Hugging Face Hub CLI

sickn33/agentic-awesome-skills

Official guidance for the `hf` command-line tool that manages the Hugging Face Hub. Covers authentication, downloading and uploading models, datasets and Spaces, bucket storage sync, local cache maintenance, collection management and CLI updates.

Hugging Face Community Evals

hugging-face-community-evals

sickn33/agentic-awesome-skills

Run evaluations for Hugging Face Hub models using inspect-ai and lighteval on local hardware. Handles backend selection, local GPU evals, and choosing between vLLM, Transformers, and accelerate.

Hugging Face Jobs Runner

hugging-face-jobs

sickn33/agentic-awesome-skills

Run Python workloads on Hugging Face Jobs with managed CPUs, GPUs, TPUs, secrets, and Hub persistence. Execute batch inference, training, or recurring tasks without local infrastructure.

Hugging Face Model Trainer

hugging-face-model-trainer

sickn33/agentic-awesome-skills

Train or fine-tune language and vision models using TRL or Unsloth on Hugging Face Jobs infrastructure. Supports SFT, DPO, GRPO methods and GGUF conversion for local deployment. Requires Pro account and HF_TOKEN for Hub saving. Optimized for cloud GPU training without local setup.

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.

Hunt LFI RFI Path Traversal

sickn33/agentic-awesome-skills

Guides authorized security testers through hunting Local File Inclusion, Remote File Inclusion, and Path Traversal vulnerabilities, including blind confirmation, PHP filter-chain RCE, log poisoning, wrappers, and safe OOB validation.

Internationalization and Localization Guide

i18n-localization

sickn33/agentic-awesome-skills

A comprehensive guide covering Internationalization (i18n) and Localization (L10n) best practices. It details how to build applications that can support multiple languages, regions, and text directions (RTL). Covers implementation patterns for React, Next.js, and Python, along with essential guidelines for handling date/number formats and managing translation keys.

Imagen AI Image Generation

sickn33/agentic-awesome-skills

Generate images with Google Gemini's image model straight from a Claude Code session. Create UI placeholders, icons, documentation illustrations, diagrams and concept visuals, saved locally as PNG files with a returned file path.

Instructree Agent Instruction Auditor

sickn33/agentic-awesome-skills

Maps, explains, and lints repository-scoped coding-agent instruction files such as AGENTS.md, CLAUDE.md, Copilot and Cursor rules. Run local read-only scans to inventory instructions, audit recursive imports, and export JSON or SARIF diagnostics before changing code.

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