parallel-deep-research 是做什么的？
仅当用户明确要求“深度研究”、“详尽”、“全面报告”或“彻底调查”时使用。比 parallel-web-search 更慢且更昂贵。对于普通研究/查询请求，请改用 parallel-web-search。支持多轮对话：传递先前研究或丰富过程的 --previous-interaction-id 以在上下文中继续。
仅当用户明确要求“深度研究”、“详尽”、“全面报告”或“彻底调查”时使用。比 parallel-web-search 更慢且更昂贵。对于普通研究/查询请求，请改用 parallel-web-search。支持多轮对话：传递先前研究或丰富过程的 --previous-interaction-id 以在上下文中继续。
parallel-web/parallel-deep-research 是一个专为 AI 代理设计的深度研究技能，适用于需要全面、详尽或彻底调查的场景。它比 parallel-web-search 更慢且成本更高，因此应仅在用户明确要求“深度研究”、“详尽”、“全面报告”或“彻底调查”时触发。该技能支持多轮对话，允许通过传递先前研究或丰富过程的 `--previous-interaction-id` 来在上下文中继续，从而实现更深入的迭代探索。对于普通的研究或查找请求，建议使用更轻量级的 parallel-web-search 以节省时间和成本。该技能旨在处理复杂、多步骤的查询，提供结构化的高信息密度响应。
npx skills add https://github.com/parallel-web/parallel-agent-skills/tree/main/skills/parallel-deep-researchInstalled? Explore more 研究与数据分析 skills: affaan-m/uspto-database, affaan-m/scholar-evaluation, affaan-m/literature-review · View all 6 →
仅当用户明确要求“深度研究”、“详尽”、“全面报告”或“彻底调查”时使用。比 parallel-web-search 更慢且更昂贵。对于普通研究/查询请求，请改用 parallel-web-search。支持多轮对话：传递先前研究或丰富过程的 --previous-interaction-id 以在上下文中继续。
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.