사용자가 명시적으로 '심층 조사', '철저한', '종합 보고서', '면밀한 조사'라고 말할 때만 사용합니다. 일반적인 검색/조회 요청에는 대신 parallel-web-search를 사용하세요. 다중 턴을 지원합니다: 이전 연구나 정제 작업의 --previous-interaction-id를 전달하여 컨텍스트를 유지하며 계속합니다.
이 스킬은 사용자가 '심층 조사', '철저한', '종합 보고서', '면밀한 조사'와 같은 특정 키워드를 명시적으로 요청할 때만 활성화되어, 보다 포괄적이고 비용이 많이 드는 심층 연구를 수행합니다. 일반적인 웹 검색 스킬(parallel-web-search)보다 느리고 비용이 많이 들기 때문에 평범한 조회나 빠른 정보 확인에는 사용하지 않도록 설계되었습니다. 주로 Claude Code와 같은 AI 에이전트 플랫폼에서 사용되며, 이전 상호작용 ID(--previous-interaction-id)를 전달함으로써 다중 턴 대화를 지원하여 연구 컨텍스트를 유지하고 연속적인 심층 분석을 가능하게 합니다. 따라서 이 스킬은 사용자가 명확히 의도한 심층 조사 작업에만 최적화되어 있어 자원 낭비를 방지하고 효율적인 정보 수집을 도와줍니다.
npx skills add https://github.com/parallel-web/parallel-agent-skills/tree/main/skills/parallel-deep-researchInstalled? Explore more 연구 & 데이터 분석 skills: affaan-m/uspto-database, affaan-m/scholar-evaluation, affaan-m/literature-review · View all 6 →
사용자가 명시적으로 '심층 조사', '철저한', '종합 보고서', '면밀한 조사'라고 말할 때만 사용합니다. parallel-web-search보다 느리고 비용이 많이 듭니다. 일반적인 검색/조회 요청에는 대신 parallel-web-search를 사용하세요. 다중 턴을 지원합니다: 이전 연구나 정제 작업의 --previous-interaction-id를 전달하여 컨텍스트를 유지하며 계속합니다.
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.