8 階段文獻回顧管道，使用 7 個聯邦資料來源，具備去重、排序檢索、引用追蹤、自我批判及 5 種報告原型。
學術深度研究是一個專為 AI 助手設計的 8 階段文獻回顧技能，支援 Claude Code、Cursor、Codex 等平台。它整合來自 7 個聯邦資料來源的學術資源，並透過去重、排序檢索及引用追蹤來確保結果的品質與相關性。該技能還包含自我批判機制，以提升分析的客觀性與準確性，最終產出 5 種不同報告原型，滿足多樣的使用情境。無論是學術研究人員或開發者，都能透過此技能快速完成深入且系統化的文獻探討。
npx skills add Agents365-ai/scholar-deep-researchInstalled? Explore more 研究與資料分析 skills: obra/superpowers, affaan-m/quarkus-verification, affaan-m/uspto-database · View all 6 →
8-phase literature-review pipeline. 7 federated sources, dedup, ranked retrieval, citation chasing, self-critique, 5 report archetypes.
Source: https://github.com/Agents365-ai/scholar-deep-research
Discovered from GitHub repositories pushed in the last 24 hours for agent skills, Claude/Codex/Gemini workflows, MCP tooling, and adjacent AI-agent automation.
Agent skill repository discovered by 10x-chat research.
Verification loop for Quarkus projects: build, static analysis, tests with coverage, security scans, native compilation, and diff review before release or PR.
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.
Evidence-first current-state research workflow for ECC. Use when the user wants fresh facts, comparisons, enrichment, or a recommendation built from current public evidence and any supplied local context.