parallel-findall 是做什么的？
发现符合自然语言描述的实体（公司、人物、产品等）。当用户要求“查找所有 X”或“列出每个 Y...”时使用——例如，“查找在 2026 年完成 A 轮融资的 AI 初创公司”、“列出北卡罗来纳州夏洛特市的屋顶公司”、“展示 YC W24 期开发者工具公司”。它与网页搜索（返回网页）和深度研究（返回叙述性报告）不同。当用户想要一个结构化的实体列表时使用。
发现符合自然语言描述的实体（公司、人物、产品等）。当用户要求“查找所有 X”或“列出每个 Y...”时使用——例如，“查找在 2026 年完成 A 轮融资的 AI 初创公司”、“列出北卡罗来纳州夏洛特市的屋顶公司”、“展示 YC W24 期开发者工具公司”。它与网页搜索（返回网页）和深度研究（返回叙述性报告）不同。当用户想要一个结构化的实体列表时使用。
parallel-web/parallel-findall 是一个 AI 代理技能，旨在根据自然语言查询发现和列出结构化实体，例如公司、人物或产品。与返回网页或叙述性报告的通用网络搜索不同，该技能专注于生成结构化的实体列表，使用户能够有效地编译特定类别的信息。它支持多个平台，包括 Claude Code、Cursor 和 Codex，在需要全面编译数据时特别有用，例如“查找在 2026 年完成 A 轮融资的 AI 初创公司”或“列出北卡罗来纳州夏洛特市的屋顶公司”。该技能通过将自然语言描述转换为结构化查询来操作，并利用并行处理来有效地检索和整理相关实体。
npx skills add https://github.com/parallel-web/parallel-agent-skills/tree/main/skills/parallel-findallInstalled? 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 →
发现符合自然语言描述的实体（公司、人物、产品等）。当用户要求“查找所有 X”或“列出每个 Y...”时使用——例如，“查找在 2026 年完成 A 轮融资的 AI 初创公司”、“列出北卡罗来纳州夏洛特市的屋顶公司”、“展示 YC W24 期开发者工具公司”。它与网页搜索（返回网页）和深度研究（返回叙述性报告）不同。当用户想要一个结构化的实体列表时使用。
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.
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.