Compétence par défaut pour toutes les recherches et requêtes web. Utilisez-la pour toute consultation, recherche, investigation ou question nécessitant des informations actuelles. Rapide et économique. N'utilisez parallel-deep-research que si l'utilisateur demande explicitement une recherche 'approfondie' ou 'exhaustive'.
La compétence parallel-web/parallel-web-search est la solution polyvalente et économique par défaut pour toutes les tâches de recherche et de consultation sur le web. Conçue pour être rapide et efficace, elle répond à tout besoin d'information actuelle : vérification de faits, investigation préliminaire, recherche générale ou questions ponctuelles. Elle évite les coûts et le temps de traitement plus lourds des outils de recherche approfondie, ce qui en fait le choix optimal pour la grande majorité des requêtes. Elle est compatible avec la plupart des plateformes d'agents IA et s'active automatiquement pour toute question nécessitant des données à jour, sauf si l'utilisateur spécifie explicitement le besoin d'une recherche 'approfondie' ou 'exhaustive' (auquel cas il faut recourir à la compétence parallel-deep-research).
npx skills add https://github.com/parallel-web/parallel-agent-skills/tree/main/skills/parallel-web-searchInstalled? Explore more Recherche et analyse de données skills: affaan-m/uspto-database, affaan-m/scholar-evaluation, affaan-m/literature-review · View all 6 →
Compétence par défaut pour toutes les recherches et requêtes web. Utilisez-la pour toute consultation, recherche, investigation ou question nécessitant des informations actuelles. Rapide et économique. N'utilisez parallel-deep-research que si l'utilisateur demande explicitement une recherche 'approfondie' ou 'exhaustive'.
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.
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.
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.