parallel-findall 是做什麼的？
用自然語言描述尋找符合條件的實體（公司、人物、產品等）。當使用者要求「列出所有 X」或「找出每個滿足…的 Y」時使用，例如：「找出 2026 年完成 A 輪融資的 AI 新創公司」、「列出北卡羅萊納州夏洛特的屋頂公司」或「顯示 YC W24 的開發工具公司」。這與網頁搜尋（回傳網頁）和深度研究（回傳敘述性報告）不同。當使用者想要一個結構化的實體列表時，請使用這個技能。
用自然語言描述尋找符合條件的實體（公司、人物、產品等）。當使用者要求「列出所有 X」或「找出每個滿足…的 Y」時使用。
這個技能「parallel-web/parallel-findall」可以根據自然語言描述，以結構化列表的形式找出符合條件的實體（如公司、人物、產品等）。它與一般的網頁搜尋或深度研究不同：網頁搜尋回傳網頁連結，深度研究回傳敘述性報告，而這個技能則專注於回傳有組織的實體列表。支援的查詢範例包括：「找出 2026 年完成 A 輪融資的 AI 新創公司」、「列出北卡羅萊納州夏洛特的屋頂公司」或「顯示 YC W24 的開發工具公司」。當使用者想要一個乾淨的實體清單而非文章或網頁時，這個技能是首選。它透過平行處理多個搜尋請求來提高效率和準確性，適合開發者與 AI 代理在需要結構化資料的情境下使用。
npx skills add https://github.com/parallel-web/parallel-agent-skills/tree/main/skills/parallel-findallInstalled? Explore more 研究與資料分析 skills: affaan-m/uspto-database, affaan-m/scholar-evaluation, affaan-m/literature-review · View all 6 →
用自然語言描述尋找符合條件的實體（公司、人物、產品等）。當使用者要求「列出所有 X」或「找出每個滿足…的 Y」時使用，例如：「找出 2026 年完成 A 輪融資的 AI 新創公司」、「列出北卡羅萊納州夏洛特的屋頂公司」或「顯示 YC W24 的開發工具公司」。這與網頁搜尋（回傳網頁）和深度研究（回傳敘述性報告）不同。當使用者想要一個結構化的實體列表時，請使用這個技能。
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.