parallel-data-enrichment 是做什么的？
批量数据增强。为列表中的公司、人员或产品添加来自网络的信息（如CEO姓名、融资情况、联系方式）。适用于对CSV文件或内联数据进行增强。支持多轮交互：通过传入先前研究任务的 --previous-interaction-id 参数来携带上下文信息。
批量数据增强。为公司、人员或产品列表添加来自网络的信息（如CEO姓名、融资情况、联系方式）。支持CSV文件或内联数据增强，并可通过传递先前交互ID实现多轮上下文衔接。
parallel-web/parallel-data-enrichment 是一个专为批量数据增强设计的AI技能，适用于Claude Code等AI代理平台。它能够自动从网络来源获取并填充缺失字段，例如CEO姓名、融资信息、联系地址等，从而丰富公司、人员或产品列表的数据内容。该技能支持两种输入模式：直接处理CSV文件或接受内联JSON数据，极大提升了数据清洗与扩充的效率。其核心价值在于将繁琐的手动信息收集流程自动化，特别适合销售线索挖掘、市场调研或竞争对手分析场景。此外，该技能支持多轮交互上下文传递：通过`--previous-interaction-id`参数，可将先前研究任务的结果带入当前任务，实现连续且上下文一致的增强流程，无需重复爬取已获取的信息。
npx skills add https://github.com/parallel-web/parallel-agent-skills/tree/main/skills/parallel-data-enrichmentInstalled? 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 →
批量数据增强。为列表中的公司、人员或产品添加来自网络的信息（如CEO姓名、融资情况、联系方式）。适用于对CSV文件或内联数据进行增强。支持多轮交互：通过传入先前研究任务的 --previous-interaction-id 参数来携带上下文信息。
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.