전체 학술 연구 파이프라인(연구 → 작성 → 무결성 검사 → 검토 → 수정 → 재검토 → 재수정 → 최종 무결성 검사 → 마무리)을 조정하는 오케스트레이터입니다. deep-research, academic-paper, academic-paper-reviewer를 통합하여 10단계 워크플로우를 생성하며, 필수 무결성 검증, 2단계 동료 검토, 재현 가능한 품질 게이트를 제공합니다. 다음 트리거에 반응합니다: academic pipeline, research to paper, full paper workflow, paper pipeline, end-to-end paper, research-to-publication, complete paper workflow.
이 스킬은 학술 연구의 전 과정을 자동화하는 오케스트레이터입니다. 연구 단계에서 최종 마무리까지 10단계 워크플로우를 통해 진행되며, 각 단계별로 무결성 검사와 동료 검토를 필수적으로 포함하여 학술적 엄격성을 보장합니다. Claude Code, Cursor, Codex와 같은 AI 에이전트 플랫폼에서 사용할 수 있도록 설계되었으며, 'deep-research', 'academic-paper', 'academic-paper-reviewer' 스킬을 조율합니다. 사용자가 'academic pipeline', 'research to paper' 등의 트리거 문구를 입력하면 자동으로 전체 파이프라인이 시작되어 연구, 작성, 검토, 수정, 재검토, 재수정, 최종 무결성 검사, 마무리 단계를 순차적으로 실행합니다. 각 단계는 품질 게이트를 통해 통과 여부가 결정되며, 최종 결과물은 학술 논문 형식으로 출력됩니다.
npx skills add https://github.com/imbad0202/academic-research-skills/tree/main/skills/academic-pipelineInstalled? Explore more 연구 & 데이터 분석 skills: affaan-m/uspto-database, affaan-m/scholar-evaluation, affaan-m/literature-review · View all 6 →
전체 학술 연구 파이프라인을 조정하는 오케스트레이터: 연구 -> 작성 -> 무결성 검사 -> 검토 -> 수정 -> 재검토 -> 재수정 -> 최종 무결성 검사 -> 마무리. deep-research, academic-paper, academic-paper-reviewer를 통합하여 필수 무결성 검증, 2단계 동료 검토, 재현 가능한 품질 게이트를 갖춘 10단계 워크플로우를 생성합니다. 다음 트리거에 반응합니다: academic pipeline, research to paper, full paper workflow, paper pipeline, end-to-end paper, research-to-publication, complete paper workflow.
This repo contains 3 individual skills — each has its own dedicated page.
12-agent academic paper writing pipeline. 10 modes (full/plan/outline/revision/revision-coach/abstract/lit-review/format-convert/citation-check/disclosure). 6 paper types, 5 citation formats, bilingual abstracts, LaTeX/DOCX-via-Pandoc/PDF output. Style Calibration + Writing Quality Check + Anti-Patterns with IRON RULE markers. Triggers: write paper, academic paper, guide my paper, parse reviews, AI disclosure, 寫論文, 學術論文, 引導我寫論文, 審查意見.
Multi-perspective academic paper review with dynamic reviewer personas. Simulates 5 independent reviewers (EIC + 3 peer reviewers + Devil's Advocate) with field-specific expertise. Supports full review, re-review (verification), quick assessment, methodology focus, Socratic guided, and calibration modes. Triggers on: review paper, peer review, manuscript review, referee report, review my paper, critique paper, simulate review, editorial review, calibrate reviewer, reviewer calibration, measure reviewer accuracy.
Universal deep research agent team. 13-agent pipeline for rigorous academic research on any topic. 7 modes: full research, quick brief, paper review, lit-review, fact-check, Socratic guided research dialogue, and systematic review with optional meta-analysis. Covers research question formulation, Socratic mentoring, methodology design, systematic literature search, source verification, cross-source synthesis, risk of bias assessment, meta-analysis, APA 7.0 report compilation, editorial review, devil's advocate challenges, ethics review, and post-research literature monitoring. Triggers on: research, deep research, literature review, systematic review, meta-analysis, PRISMA, evidence synthesis, fact-check, guide my research, help me think through, 研究, 深度研究, 文獻回顧, 文獻探討, 系統性回顧, 後設分析, 事實查核, 引導我的研究, 幫我釐清, 幫我想想, 我不確定要研究什麼, 研究方向, 研究主題.
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.