Skill UI

Provides minimal working examples and comprehensive instructions for querying the Fireflies.ai GraphQL API. Users can learn how to list workspace users, fetch recent meeting transcripts, and retrieve detailed summaries, including action items and keywords, using code examples in TypeScript, Python, and Bash. Ideal for initial API setup, integration testing, or learning GraphQL patterns for meeting data.

Guides setting up modern Python projects with uv, ruff, ty, pytest, prek, and security checks, covering initialization, pyproject configuration, dependency and tool management, quick-start scripts, full project layout, and migrations from legacy tooling.

An advanced skill for Scrum Masters that uses Python scripts to analyze raw sprint data exported from tools like Jira. It provides comprehensive insights including Monte Carlo velocity forecasting, a multi-dimensional team health score (0-100), and tracking of action items and recurring themes from retrospectives. Ideal for process improvement, sprint planning, and stakeholder reporting.

A comprehensive collection of diagnostic scripts designed for troubleshooting Sentry integration failures. This bundle collects SDK versions, runtime configuration details (e.g., DSN, environment), and performs network connectivity checks to the Sentry ingest endpoint. Use it when events are not appearing in Sentry, or when systematically diagnosing complex setup issues across Node.js and Python environments.

A comprehensive meta-skill for validating, testing, and scoring the quality of technical skills within an ecosystem. It performs deep structural checks (SKILL.md compliance), executes Python scripts for syntax, runtime, and output validation, and generates multi-dimensional quality scores using letter grades and tier recommendations. Ideal for new skill authorship, auditing existing components, or integrating rigorous QA gates into CI/CD pipelines.

A comprehensive guide for deploying Machine Learning training jobs and inference services on the Vast.ai GPU cloud. This skill covers the entire deployment lifecycle, including optimizing Docker images for performance, creating automated provisioning scripts using the Vast.ai CLI, implementing robust data transfer strategies (SCP, rsync), and performing post-deployment health checks.

gget is a CLI/Python client that unifies querying over 20+ bioinformatics databases, letting researchers fetch gene info, sequences, BLAST results, structures, and enrichment data interactively or within scripts.

Provides access to USPTO PatentSearch, PEDS, TSDR, and related APIs for patent and trademark searches, examination history, assignments, citations, and litigation data, with helper scripts for Python-based IP research and portfolio insights.

This skill automates the creation of isolated, pre-configured development containers (devcontainers). It scans the project structure to detect supported languages (Python, Node.js, Rust, Go) and intelligently generates the necessary configuration files, VS Code extensions, and setup scripts. It ensures all required tools are installed in a sandboxed environment, making it ideal for starting new multi-language projects or needing consistent, reproducible development workspaces.

This comprehensive guide details the systematic extraction and analysis of Windows Registry hives (SAM, SYSTEM, SOFTWARE, NTUSER.DAT) from forensic images. It demonstrates how to use specialized tools like RegRipper and Python scripts to uncover critical artifacts, including user activity history, persistence mechanisms (autorun keys), installed software records, and evidence of system compromise, which is crucial for digital forensics investigations.

This guide provides comprehensive techniques for detecting Layer 2 ARP poisoning and spoofing attacks. It covers monitoring gratuitous ARP floods, tracking IP-to-MAC mapping changes, and identifying rogue devices. Techniques include deploying ARPWatch, configuring Dynamic ARP Inspection (DAI) on managed switches, and using advanced Wireshark filters or custom Python scripts for robust security monitoring and incident response.

Models cap table dilution, SAFE conversion, and exit waterfalls for venture rounds, generating OCF inputs and running Python scripts to calculate ownership changes and waterfall outcomes.