O que rainforest-lab faz?
Framework honesto de pesquisa multiagente — skill, motor Python, produto MCP.
Fonte: https://github.com/KrakenP/rainforest-lab
Descoberto durante a caça diária de skills de agente no awesomeskills.dev.
Framework honesto de pesquisa multiagente — skill, motor Python, produto MCP.
O KrakenP/rainforest-lab é um framework de pesquisa multiagente focado em transparência e honestidade. Ele combina um skill para agentes de IA (como Claude Code, Cursor e Codex), um motor Python para execução de tarefas e um produto MCP (Model Context Protocol) para integração com ferramentas externas. A principal vantagem é permitir que agentes colaborem de forma confiável, rastreando fontes e citações para garantir a veracidade das informações. Ideal para desenvolvedores que precisam de um sistema modular e auditável para pesquisas automatizadas.
npx skills add KrakenP/rainforest-labInstalled? Explore more Pesquisa e Análise de Dados skills: affaan-m/uspto-database, affaan-m/scholar-evaluation, affaan-m/literature-review · View all 6 →
Framework honesto de pesquisa multiagente — skill, motor Python, produto MCP.
Fonte: https://github.com/KrakenP/rainforest-lab
Descoberto durante a caça diária de skills de agente no 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.