Enriquecimiento masivo de datos. Agrega campos obtenidos de la web (nombres de CEOs, financiamiento, información de contacto) a listas de empresas, personas o productos. Útil para enriquecer archivos CSV o datos en línea. Soporta múltiples turnos: pasa --previous-interaction-id de una tarea de investigación anterior para mantener el contexto.
Esta habilidad permite enriquecer conjuntos de datos de forma masiva, extrayendo información de fuentes web para agregar campos como nombres de CEOs, montos de financiamiento y datos de contacto a listas de empresas, personas o productos. Resulta especialmente útil para complementar archivos CSV o datos en línea con información actualizada del mundo real, ahorrando horas de investigación manual. Es compatible con plataformas como Claude Code, Cursor y Codex, y su diseño modular permite integrarse fácilmente en flujos de trabajo de automatización. Su funcionamiento se basa en consultas paralelas a la web, y soporta múltiples turnos mediante el parámetro --previous-interaction-id, lo que permite encadenar tareas de investigación y mantener el contexto entre ejecuciones sucesivas.
npx skills add https://github.com/parallel-web/parallel-agent-skills/tree/main/skills/parallel-data-enrichmentInstalled? Explore more Investigación y análisis de datos skills: affaan-m/uspto-database, affaan-m/scholar-evaluation, affaan-m/literature-review · View all 6 →
Enriquecimiento masivo de datos. Agrega campos obtenidos de la web (nombres de CEOs, financiamiento, información de contacto) a listas de empresas, personas o productos. Útil para enriquecer archivos CSV o datos en línea. Soporta múltiples turnos: pasa --previous-interaction-id de una tarea de investigación anterior para mantener el contexto.
This repo contains 8 individual skills — each has its own dedicated page.
ONLY use when user explicitly says 'deep research', 'exhaustive', 'comprehensive report', or 'thorough investigation'. Slower and more expensive than parallel-web-search. For normal research/lookup requests, use parallel-web-search instead. Supports multi-turn: pass --previous-interaction-id from a prior research or enrichment to continue with context.
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.