Úsalo solo cuando el usuario solicite explícitamente 'investigación profunda', 'exhaustivo', 'informe completo' o 'investigación minuciosa'. Es más lento y costoso que parallel-web-search. Para solicitudes normales de investigación/consulta, usa parallel-web-search. Soporta múltiples turnos: pasa --previous-interaction-id de una investigación o enriquecimiento previo para continuar con contexto.
Este skill, parallel-web/parallel-deep-research, está diseñado para realizar investigaciones profundas, exhaustivas y detalladas cuando el usuario lo indique explícitamente mediante frases clave como 'investigación profunda', 'exhaustivo', 'informe completo' o 'investigación minuciosa'. Es más lento y costoso que el skill parallel-web-search, por lo que no debe usarse para consultas rápidas o búsquedas normales. Una característica clave es su soporte multi-turno: al pasar el parámetro --previous-interaction-id de una investigación o enriquecimiento anterior, puede continuar con contexto, permitiendo iteraciones sucesivas. Es compatible con plataformas como Claude Code, Cursor, Codex o cualquier agente que admita skills con interfaz de línea de comandos. Su uso adecuado asegura que los recursos se asignen solo a tareas que realmente requieren un análisis profundo, optimizando así el rendimiento y los costos.
npx skills add https://github.com/parallel-web/parallel-agent-skills/tree/main/skills/parallel-deep-researchInstalled? Explore more Investigación y análisis de datos skills: affaan-m/uspto-database, affaan-m/scholar-evaluation, affaan-m/literature-review · View all 6 →
Úsalo solo cuando el usuario solicite explícitamente 'investigación profunda', 'exhaustivo', 'informe completo' o 'investigación minuciosa'. Es más lento y costoso que parallel-web-search. Para solicitudes normales de investigación/consulta, usa parallel-web-search. Soporta múltiples turnos: pasa --previous-interaction-id de una investigación o enriquecimiento previo para continuar con contexto.
This repo contains 8 individual skills — each has its own dedicated page.
Bulk data enrichment. Adds web-sourced fields (CEO names, funding, contact info) to lists of companies, people, or products. Use for enriching CSV files or inline data. Supports multi-turn: pass --previous-interaction-id from a prior research task to carry context forward.
Discover entities (companies, people, products, etc.) matching a natural-language description. Use when the user asks to 'find all X' or 'list every Y that…' — e.g., 'Find AI startups that raised Series A in 2026', 'List roofing companies in Charlotte NC', 'Show me YC W24 dev tools companies'. Different from web-search (which returns webpages) and deep-research (which returns a narrative report). Use this when the user wants a structured list of entities.
Continuously track the web for changes on a recurring cadence. Use when the user asks to 'monitor', 'track changes to', 'watch', or 'alert me when' something on the web changes — e.g., 'Track price changes for iPhone 16', 'Alert me when Tesla files a new 8-K', 'Monitor competitor pricing pages weekly'. Also use to list, inspect, update, or delete existing monitors.
URL content extraction. Use for fetching any URL - webpages, articles, PDFs, JavaScript-heavy sites. Token-efficient: runs in forked context. Prefer over built-in WebFetch.
DEFAULT for all research and web queries. Use for any lookup, research, investigation, or question needing current info. Fast and cost-effective. Only use parallel-deep-research if user explicitly requests 'deep' or 'exhaustive' research.
Get completed research task result by run ID
Set up the Parallel plugin (install CLI)
Check running research task status by run ID
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.