Skill UI

DefectDojo is an open-source platform for comprehensive vulnerability management. It aggregates findings from over 200 security scanners (e.g., Nessus, ZAP), automatically deduplicates results, and provides detailed dashboards for tracking remediation progress. It integrates seamlessly with CI/CD pipelines, Jira for ticketing, and Slack for notifications, making it ideal for automated security reporting and compliance tracking in a DevSecOps environment.

Build a production-ready, AI-powered data collection agent for any public source (job boards, prices, news, etc.). This agent automatically scrapes data, enriches it using free LLMs (like Gemini Flash), and stores the results in databases such as Notion, Sheets, or Supabase. It runs on a free schedule using GitHub Actions and learns from user feedback, making it ideal for continuous data monitoring and tracking.

This guide details how to implement AWS Config managed and custom rules to establish continuous compliance monitoring for AWS resources. It helps detect configuration drift against standards like CIS and PCI DSS, enabling automated remediation using services like SSM Automation. It is ideal for building centralized compliance dashboards and aggregating audit evidence across multiple AWS accounts via AWS Organizations.

This comprehensive guide details how to deploy and configure AWS Security Hub across multi-account AWS Organizations. It aggregates security findings from various AWS services (GuardDuty, Inspector, Macie, etc.) to enforce continuous compliance against industry standards like CIS, PCI DSS, and NIST. Use this skill to build centralized compliance dashboards and automated remediation workflows for robust security posture management.

Guides on using SwanLab for open-source ML experiment tracking, offering local or self-hosted dashboards, rich media logging, and integrations with PyTorch, Transformers, Lightning, or Fastai for comparing runs and monitoring metrics.

This guide details how to build an MCP app, which is an MCP server enhanced to serve interactive UI resources. It allows developers to render complex widgets—such as forms, pickers, dashboards, and confirmation dialogs—directly inline within a chat conversation. Learn about the architecture, host specifics, and when to use a widget versus standard text output.

JobGPT is a comprehensive automation platform for managing your entire career journey. It allows users to search for jobs using advanced filters (location, salary, remote, etc.), auto-apply to openings, generate tailored resumes optimized for specific roles, and track application progress across multiple job boards. Furthermore, it provides valuable salary intelligence, helps find target recruiters, and facilitates professional outreach, making the job hunt process highly efficient.

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.

Plotly is a powerful Python graphing library designed to create highly interactive, publication-quality visualizations. It supports over 40 chart types, including scatter plots, heatmaps, candlestick charts, and geographical maps. Ideal for building sophisticated dashboards, conducting deep exploratory data analysis, and creating rich, web-embeddable presentations with features like zooming, panning, and hover tooltips.

Fetches Feishu cloud document content as Lark-flavored Markdown, extracts media tokens, and guides invoking feishu_doc_media to download images, files, and whiteboards while handling wiki token resolution.

This tool guides the creation of actionable monitoring dashboards for observability platforms like Grafana and SigNoz. Instead of merely displaying raw metrics, it focuses on structuring data to answer critical operational questions: Is the system healthy? Where are the bottlenecks? What changes occurred? And what action should be taken? It transforms raw metrics into decision-making tools.

This utility ensures the prevention of silent floating-point mismatch bugs across all EVM chains. It covers runtime querying of decimals, chain-aware caching, handling precision drift in bridged tokens, and safe normalization for bots, dashboards, and DeFi tools. It is crucial for accurate cross-chain balance comparison.