Flujo de trabajo de datos de patentes y marcas registradas de la USPTO para consultas de registros oficiales, búsquedas PatentSearch, verificaciones TSDR, datos de asignaciones y registros de investigación de propiedad intelectual (IP) reproducibles.
Este skill proporciona un flujo de trabajo integral para interactuar con la base de datos de la USPTO (Oficina de Patentes y Marcas de los Estados Unidos). Permite a los desarrolladores y agentes de IA realizar consultas estructuradas y reproducibles sobre patentes, marcas registradas, asignaciones (assignment data) y estados de solicitudes TSDR (Trademark Status & Document Retrieval). Es ideal para investigadores de propiedad intelectual, abogados de patentes, analistas de mercado y equipos de innovación que necesitan automatizar la recuperación de datos oficiales. El skill está diseñado para ser utilizado en entornos de desarrollo como Claude Code, Cursor o Codex, proporcionando una interfaz programática para buscar patentes mediante PatentSearch, verificar el estado de marcas a través de TSDR, y obtener detalles de asignaciones de derechos de propiedad intelectual. Todo esto se almacena en un registro reproducible, permitiendo auditorías y seguimiento de cambios en el estatus de patentes y marcas. La integración se realiza a través de comandos CLI simples (por ejemplo, `uspto search -q "query"`) y admite salida en múltiples formatos como JSON y Markdown, facilitando la documentación y el análisis posterior.
npx skills add https://github.com/affaan-m/everything-claude-code/tree/main/skills/uspto-databaseInstalled? Explore more Investigación y análisis de datos skills: affaan-m/scholar-evaluation, affaan-m/literature-review, affaan-m/everything-claude-code · View all 6 →
Flujo de trabajo de datos de patentes y marcas registradas de la USPTO para consultas de registros oficiales, búsquedas PatentSearch, verificaciones TSDR, datos de asignaciones y registros de investigación de propiedad intelectual (IP) reproducibles.
This repo contains 20 individual skills — each has its own dedicated page.
Design, implement, and audit inclusive digital products using WCAG 2.2 Level AA. Use when building or auditing UI that must meet WCAG 2.2 Level AA, or when reviewing a change for keyboard, contrast, or screen-reader support.
Anthropic Claude API patterns for Python and TypeScript. Covers Messages API, streaming, tool use, vision, extended thinking, batches, prompt caching, and Claude Agent SDK. Use when building applications with the Claude API or Anthropic SDKs.
End-to-end marketing campaign planning and execution. Covers audience research, positioning, campaign angle definition, landing page copy, email sequences, social posts, ad copy, short-form video scripts, and content calendars. Use as the orchestration layer for multi-channel product launches. Use when planning or executing a multi-channel product launch, or producing landing page, email, social, or ad copy.
Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.
Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.
Create distinctive, production-grade frontend interfaces with high design quality. Use when the user asks to build web components, pages, or applications and the visual direction matters as much as the code quality.
gget CLI and Python workflow for quick genomic database queries, sequence lookup, BLAST-style searches, enrichment checks, and reproducible bioinformatics evidence logs.
Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.
Production-ready UI motion system for React/Next.js. Use when implementing animations, transitions, or motion patterns.
Example project-specific skill template based on a real production application.
Direct PubMed and NCBI E-utilities search workflows for biomedical literature, MeSH queries, PMID lookup, citation retrieval, and API-backed literature monitoring.
Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.
Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature. Use when an agent or LLM feature misbehaves and the failing layer is unknown, or before shipping an agent stack.
Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics. Use when choosing between coding agents, or when a change to an agent setup needs measured pass rate, cost, and time rather than an impression.
Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates. Use when defining or revising an agent
Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Use when planning or executing engineering work that agents will carry out end to end.
Build persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases. Use when building a persistent multi-agent system on Claude Code with its own memory, commands, and scheduling.
Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.
Add x402 payment execution to AI agents with per-task budgets, spending controls, and non-custodial wallets. Supports Base through agentwallet-sdk and X Layer through OKX Payments / OKX Agent Payments Protocol. Use when an agent must pay for something itself and needs per-task budgets, spending controls, and a non-custodial wallet.
Use after completing any non-trivial task. The agent self-rates its output on 5 axes — accuracy, completeness, clarity, actionability, conciseness — with concrete evidence per criterion. Produces a structured 1-5 scorecard with specific improvement suggestions.
Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.
Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.
The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
Agent skill repository discovered by 10x-chat research.
Review backend code for quality, security, maintainability, and best practices based on established checklist rules. Use when the user requests a review, analysis, or improvement of backend files (e.g., `.py`) under the `api/` directory. Do NOT use for frontend files (e.g., `.tsx`, `.ts`, `.js`). Supports pending-change review, code snippets review, and file-focused review.
Production-ready platform for agentic workflow development.