Skill UI

Aria functions as a System Architect, responsible for establishing the definitive structural foundation of the entire system. She designs comprehensive blueprints covering the data model (schema, relationships, constraints), API contract specifications (endpoints, auth, pagination), modular file structure, and selection of core architectural design patterns. Her output ensures that no code is written without a rigorously defined, approved architectural plan, minimizing ambiguity and technical debt.

Atlas Ledger is a critical companion designed to provide lasting 'memory' to project contracts. It automatically processes detected deviations or 'drift' (e.g., from audits or reviews) and distills these instances into formal, actionable contract clauses (WHEN/DON'T/INSTEAD). These clauses are written to a persistent project memory file, Atlas.md. This mechanism ensures that lessons learned from failure are systematically captured, allowing future contract cycles to incorporate these rules, thereby strengthening overall project governance and reliability.

This skill generates a complete React component structure following the industry-standard Container/Presentation pattern. It automatically scaffolds necessary files, including TypeScript, Storybook, and SCSS styles, ensuring adherence to project conventions. Use this when building new, scalable components, allowing you to properly classify whether the component is UI-focused (Presentation) or logic-heavy (Container).

This skill provides deep security review for GitHub Actions workflow files. It focuses on the unique Actions threat model—identifying vulnerabilities like script injection via ${{ }} interpolation, privilege escalation from triggers (e.g., pull_request_target), and ensuring least-privilege permissions for CI/CD pipelines. Use it to audit, secure, or harden existing workflows, preventing attacks from untrusted inputs and supply chain risks.

This skill guides you through a comprehensive, multi-phase interview to capture every necessary detail of a new project idea. It is designed to run BEFORE any code is written, forcing detailed specification gathering across purpose, features, data models, and technical constraints. The output is a clean, self-contained prompt.md file, ensuring subsequent development is based on solid, unambiguous requirements and preventing costly mistakes.

This skill transforms raw API specifications, Postman collections, or verbal descriptions into clean, developer-facing documentation. It produces comprehensive guides including detailed endpoints, parameters, request/response schemas, standardized error codes, and runnable code examples, making it ideal for developer portals or README files.

This skill generates comprehensive, academic-style survey papers as a single, self-contained HTML file. By reading an anchor resource and curating research data, it structures the content and utilizes advanced LLMs (like Kimi K2.6 via Fireworks) to generate all prose, figures, and bibliographies, suitable for rapid academic literature review.

This skill guides the deployment of decoy files, or "canary tokens," across critical file systems. These files act as tripwires, monitoring for unauthorized changes (modification, deletion, renaming). By tracking these high-value decoys, systems can detect ransomware encryption activity in real-time, providing a low false-positive, high-fidelity detection layer that complements traditional EDR and backup solutions.

This guide provides a deep dive into the cryptographic techniques used by ransomware, including AES, RSA, ChaCha20, and hybrid schemes. It covers identifying algorithms, analyzing key management patterns (strong, weak, flawed), and reverse engineering file encryption routines to assess decryption feasibility and support recovery efforts.

This guide outlines a comprehensive workflow for detecting and preventing the exposure of sensitive AWS credentials (access keys, session tokens) within source code repositories, CI/CD pipelines, and configuration files. It utilizes TruffleHog for deep historical scanning and integrates git-secrets for robust pre-commit hooks, significantly reducing the risk of credential theft and unauthorized account access.

This skill guides the deployment and strategic use of honeytokens and canarytokens (decoy credentials, files, URLs, etc.). These high-fidelity decoys are designed to detect unauthorized access, insider threats, or adversary reconnaissance by triggering alerts upon any interaction, providing early warning deep within the kill chain.

This advanced protocol forces the AI to conduct a thorough context audit before attempting any complex task. It mandates reading every relevant file, identifying architectural gaps, and providing a detailed orientation statement. It is essential for multi-file modifications, system design, and debugging to prevent acting on assumptions and maintain high code accuracy. The AI cannot proceed until all unknown variables are resolved.