Skill UI

A comprehensive framework for managing third-party risk (TPRM) throughout the entire vendor lifecycle. This tool guides users through inventory, risk tiering, running due diligence questionnaires (SIG, CAIQ), reviewing security evidence (SOC 2, ISO 27001), codifying requirements in contracts, and establishing continuous monitoring protocols. It is essential for maintaining compliance, managing supply chain risk, and securing data handled by external vendors.

This skill structures a highly controlled, bounded development life cycle for AI agents. It mandates a structured three-phase workflow: Planning (Spec), Implementation (Build), and Verification (Review). It is essential for building complex features from scratch, especially when dealing with ambiguous requirements, high risk, or needing explicit human approval gates to ensure the resulting code meets all defined specifications.

This skill mints tamper-evident, post-quantum-signed receipts for consequential agent actions (e.g., deployment, payment, access grant). It records who, what, and under which policy the action occurred, ensuring the evidence cannot be altered later. By providing verifiable proof from the certificate alone, it is crucial for meeting regulatory compliance requirements, such as the EU AI Act, and building auditable, trustworthy systems.

The `hf-mem` skill estimates the necessary VRAM or system memory required to load Safetensors or GGUF model weights for inference directly from the Hugging Face Hub. It uses efficient HTTP Range requests, avoiding local downloads. Users can determine if a model fits on their GPU or instance, including optional estimations for the KV cache and setting limits like maximum sequence length.

Automates the creation and publication of polished, functional Gradio demos on Hugging Face Spaces for any user-provided LoRA model. This skill handles the complex workflow of designing the optimal UI, selecting the correct inference pipeline (e.g., text-to-image, video-to-video), and generating all necessary files (app.py, requirements.txt, README.md) to deploy a complete, ready-to-use AI playground.

This skill guides the structured development process for implementing new features or fixes based on Product Requirement Documents (PRDs) or predefined issues. It emphasizes best practices, including Test-Driven Development (TDD), regular type-checking, running comprehensive test suites, and completing the cycle with a formal review process.

This comprehensive template provides a structured framework for creating Product Requirements Documents (PRDs). It guides product managers and teams through defining the core problem, setting measurable success metrics (KPIs), defining feature scope, detailing user stories, integrating design notes, and planning the release rollout, ensuring all phases of product development are fully documented.

WGM is a robust, agentic methodology designed to transform vague or rough requirements into working, validated software. It enforces a disciplined workflow across six stages: deep alignment (Grill), structured planning, deterministic iterative building (Loop), mandatory holdout-scenario judging, and final documentation audits. It ensures that the resulting code converges against rigorously tested acceptance criteria rather than relying on single-pass generation.

This tool scaffolds a new Claude Code plugin from scratch, generating a standardized directory structure that meets all architectural requirements. It automatically creates necessary components, including plugin.json, skills, commands, agents, ADR documentation, and smoke tests, ensuring a robust and compliant plugin development environment.

This comprehensive skill provides deep integration with Jira, enabling users to manage the entire issue lifecycle. Features include retrieving ticket details, extracting testable requirements and acceptance criteria, updating issue status (transitions), adding comments, and linking code artifacts (PR/MR). Supports both recommended MCP server approach and direct REST API calls for robust development workflow automation.

This module is designed to document, track, and manage quality nonconformance issues found during manufacturing or process audits. It is essential for root cause analysis, implementing corrective and preventive actions (CAPA), and ensuring compliance with industry quality standards. Use it when recording deviations from specified requirements.

This tool executes the foundational SPARC Specification Phase (Phase 1) for new features or projects. It guides the user through systematically gathering comprehensive requirements, including functional/non-functional needs, defining concrete acceptance criteria (Given/When/Then), identifying technical constraints, and mapping edge cases. It creates a robust, formalized specification document that serves as the blueprint for all subsequent design and development phases.