Skill UI

Trackio is an essential library for logging, visualizing, and analyzing machine learning training experiments. It provides multi-interface support: a Python API for real-time metric logging and alert firing during training; a Command Line Interface (CLI) for retrieving historical metrics and alerts; and synchronization capabilities to Hugging Face Spaces for persistent dashboards. This tool is crucial for debugging, diagnosing training issues (like loss spikes), and automating ML workflow iteration.

Prepare and maintain venue-compliant rebuttals to external peer reviews. Parses reviewer feedback, ensures full issue coverage without fabrication, drafts safe text-only responses under character limits, and manages follow-up rounds for venues like ICML and OpenReview.

Transformers.js allows developers to run state-of-the-art machine learning models directly within JavaScript/TypeScript environments (browser, Node.js). It supports a comprehensive range of tasks including NLP (text generation, summarization, translation), Computer Vision (image classification, object detection), and Audio processing. This capability enables building fully client-side, multimodal AI applications without requiring a dedicated Python backend or server infrastructure.

This skill automates the process of renting, managing, and terminating cloud GPU instances on vast.ai. It analyzes your AI/ML task—determining VRAM, required GPU count, and estimated runtime—and then searches for the most cost-effective and reliable GPU offers, presenting optimized options for your training workflow.

Assesses AI/ML systems for prompt injection, jailbreak, model inversion, data poisoning, and agent tool abuse using MITRE ATLAS mapping, signature detection, and scoring metrics before deployment.

A sophisticated skill for detecting Command and Control (C2) traffic tunneled through DNS protocols. It utilizes advanced techniques like Shannon entropy analysis of subdomains, Machine Learning for DGA classification, and correlation with network logs from Zeek/Suricata. Ideal for SOC analysts and threat hunters investigating data exfiltration, DNS beaconing, or suspicious DNS activity.

Collection of battle-tested PyTorch training recipes covering architecture selection, optimizers, LR scheduling, precision tips, and debugging to train or fine-tune LLMs, vision, diffusion, and biomedical models with high GPU throughput.

Utilize Modal, a serverless GPU cloud, for running complex machine learning workloads, including training, fine-tuning, and inference. This tool provides a zero-config environment, eliminating the need for SSH setup or Docker management. It features automatic scale-to-zero billing and local-first execution, making high-powered GPU compute accessible for debugging and small-scale experiments.

This skill provides comprehensive automation for managing GitHub workflows across the entire development lifecycle. It covers detailed issue triage, PR review status checks, CI/CD failure debugging, official release management, and security monitoring using the GitHub CLI. Ideal for maintaining open-source project health and streamlining team development processes.

This skill provides a robust system for tracking all code modifications using structured JSON records and enforcing a defined state machine (e.g., planned -> deployed). It is critical for AI coding workflows, ensuring that if a session expires or is abandoned, the next session receives the full context, progress summary, and blockers to resume seamlessly. Ideal for complex, multi-stage development projects and onboarding new codebases.

A placeholder skill entry that explains the lack of a YAML frontmatter, useful for tracking or validation workflows that detect missing metadata.

A comprehensive guide to achieving high code quality in Go projects using `golangci-lint`. This skill covers the entire development lifecycle, including setting up the essential `.golangci.yml` configuration, running automated linters (style, security, complexity), automatically fixing issues, interpreting detailed lint output, and correctly suppressing false positives using `//nolint` directives. Ideal for enforcing best practices in CI/CD pipelines and maintaining legacy codebases.