Skill UI

This skill systematically analyzes an existing codebase to generate seven comprehensive technical documents. It covers the technology stack, architectural patterns, directory structure, coding conventions, external integrations, testing strategies, and technical concerns. It is invaluable for new team members onboarding, conducting project audits, or creating a complete, verifiable system knowledge base.

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.

This comprehensive tool guides the systematic creation of detailed technical specifications. It orchestrates the entire design lifecycle, including context loading, deep discovery and analysis (tailored for new features, extensions, or simple additions), synthesis using established design principles, and generating a boundary-first, structured design draft. Ensures all requirements are mapped to explicit technical components.

Guides research teams through a two-stage manuscript workflow: outline sections with research-lookup, enforce IMRAD structure, citation styles, figures, and reporting guidelines, then convert into polished prose for journals.

A comprehensive set of rules and constraints designed to optimize AI interaction in technical domains. It guides the agent on intelligent input filtering (handling logs, Git, codebases), resolving ambiguity, and generating surgically precise, structured outputs. Ideal for complex debugging, refactoring, and long-session development tasks.

This skill generates comprehensive and structured requirements documents for a new specification. It systematically aggregates context from project files, business rules, and technical constraints, ensuring all generated requirements focus on user-observable behavior (WHAT) rather than implementation details (HOW). It follows established standards like EARS format and includes a rigorous review gate to ensure scope clarity and completeness.

Provides technology-agnostic guidance for architecting and reviewing modular systems. This skill covers core architectural principles such as bounded contexts, state isolation, explicit contracts, and failure containment. Use it when determining service boundaries, assessing coupling between domains, or designing new greenfield architectures without assuming a specific framework, language, or repository structure.

Comprehensive runbook for managing Adobe API integration outages, covering Firefly, PDF Services, and IMS authentication failures. Provides structured procedures for initial triage, determining severity (P1-P4), executing recovery steps (Auth/Rate Limit), implementing fallback modes, and documenting post-incident reports and communication.

A robust, production-ready architecture pattern for integrating with multiple Adobe APIs, including Firefly Services, PDF Services, and I/O Events. This pattern enforces a layered structure (API -> Service -> Client -> Infrastructure), ensuring clean separation of concerns, centralized authentication, robust error handling boundaries, and scalable workflow orchestration for enterprise applications.

This guide provides a reference architecture for automating the design-to-code workflow. It outlines the complete pipeline—from Figma design input to structured React/Vue output—detailing components like Figma API integration, code generation, and crucial post-processing steps (token mapping, normalization). Ideal for establishing scalable design systems and managing large-scale design handoffs in a professional environment.

A comprehensive diagnostic reference for resolving common technical failures encountered when running web scraping and automation tasks on the Apify platform. Covers issues such as Actor run failures, API rate limits (429), authentication errors (401), anti-bot blocks, and memory allocation issues, providing structured fixes and best practices for robust scraping pipelines.

This utility provides a structured way to manage and separate notes across multiple accounts and environments within Apple Notes. By utilizing distinct accounts (e.g., iCloud for Production, 'On My Mac' for Development), users can ensure absolute data isolation between development, staging, and live production data. It is crucial for maintaining clean separation in complex development workflows and QA testing scenarios.