Agente de insultos e verificação de fatos com recibos da web ao vivo via Bright Data MCP — Hackathon Web Data UNLOCKED (Bright Data x lablab.ai)
O BernardUriza/insult-ai é um agente inteligente que combina insultos criativos com verificação rigorosa de fatos, utilizando o Bright Data MCP para buscar evidências ao vivo da web. Ideal para hackathons e projetos de agentes de IA, ele oferece uma maneira divertida e útil de gerar respostas sarcásticas fundamentadas em dados reais. Suporta plataformas como Claude Code, Cursor e Codex, integrando-se facilmente via MCP. O agente ajuda a ilustrar como agentes de IA podem usar fontes web para enriquecer interações, mantendo um tom humorístico mas baseado em fatos.
npx skills add BernardUriza/insult-aiInstalled? Explore more Pesquisa e Análise de Dados skills: affaan-m/uspto-database, affaan-m/scholar-evaluation, affaan-m/literature-review · View all 6 →
Agente de insultos e verificação de fatos com recibos da web ao vivo via Bright Data MCP — Hackathon Web Data UNLOCKED (Bright Data x lablab.ai)
Fonte: https://github.com/BernardUriza/insult-ai
Descoberto durante a caça diária de habilidades de agente em awesomeskills.dev.
USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.
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.