academic-ref-check

学术参考文献的端到端检查、验证与修复。解析原始文献列表，去重，按APA第七版(APA 7th edition)格式化，通过OpenAlex和Semantic Scholar数据库验证每条文献的准确性，自动修复DOI、年份、标题等问题，并生成人工审核清单和Word文档。Use this skill whenever you need to: 参考文献检查, 参考文献格式调整, APA格式化, 文献去重, DOI验证, 参考文献修复, 参考文献整理, reference check, APA formatting, bibliography verification, DOI validation, citation formatting, or reference list cleanup.

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저장소: O0000-code/meta-analysis-skill
최근 소스 활동: 2026년 7월 7일 09:45
감지된 SKILL.md 언어: 중국어
스타: 1
포크: 0

설치 방법

기본적으로 소스를 먼저 확인하는 Prompt가 선택됩니다. 직접 명령으로 전환하거나 로컬 사본을 다운로드할 수도 있습니다.

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Follow https://skillsmp.com/skill-install/prompt.md to install only the Agent Skill at https://github.com/O0000-code/meta-analysis-skill/tree/main/skills/academic-ref-check.

직접 명령은 검토 Prompt를 거치지 않습니다. 실행하기 전에 소스를 확인하세요.

npx skills add https://github.com/O0000-code/meta-analysis-skill --skill academic-ref-check

명령은 한 줄로 유지됩니다. 복사하기 전에 가로로 스크롤해 전체 내용을 확인하세요.

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SKILL.md

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name

academic-ref-check

description

검색으로 돌아가기 설치로 돌아가기

manuscript-typeset

Typeset a finished academic Markdown manuscript into submission-ready and publication-preview outputs — WITHOUT altering any number, citation, or claim. Produces four artifacts: submission DOCX (pandoc + APA-7 reference-docx), submission PDF (LaTeX apa7 man: double-spaced, line-numbered, tables/figures end-placed), published-look preview PDF (apa7 jou two-column), and the clean Markdown. Fidelity is a hard script gate (exit 2, no --warn-only): every output is token-compared against its source and blocked on any drift. Handles CJK (XeLaTeX + xeCJK, 0 missing chars), faithful reference rendering (no citeproc / no .bib re-conversion), internal-trace stripping (gate codes / corpus IDs), landscape wide tables, and user-placeholder manifests. This is the typesetting exit that writing skills call after a draft is final, and also stands alone. Use when the user says: typeset manuscript, 把 md 学术稿排成投稿版 / 发表版, 出投稿 docx / PDF, submission-ready manuscript, publication-ready, APA 7 manuscript formatting, reference-docx, 学术

2026-07-07

meta-analysis

Quantitative meta-analysis: effect-size extraction, FE/RE pooling, I²/τ²/95% PI, subgroup, meta-regression, publication bias (funnel/Egger/PET-PEESE/p-curve/RoBMA), psychology methods (RVE, three-level, MASEM, Hunter-Schmidt, reliability generalization). Generates R code (metafor, robumeta, clubSandwich, metaSEM, psychmeta, puniform, RoBMA); pairs ssci-plots. APA MARS/PRISMA-2020. Use when user requests meta-analysis, 元分析, 心理学元分析, 量表元分析, effect size pooling, forest plot, random-effects model, meta-regression, reliability generalization, Hunter-Schmidt, RVE, three-level meta-analysis, MASEM, p-curve, PET-PEESE, RoBMA. Use whenever pooling effect sizes from studies. Do NOT use for: qualitative synthesis (use systematic-review); narrative review (use field-overview-review); 50-year history (use field-evolution-review); literature set without pooling (use literature-set-review); theory building (use theoretical-review). SR+MA combined: run systematic-review (L1-L8), then this (L9-L12).

2026-07-07

review-methodology-foundations

Shared methodology base for all academic literature review skills (field-overview-review, field-evolution-review, literature-set-review, systematic-review, meta-analysis, theoretical-review). NOT directly triggered by users — referenced by other review skills via references/. Houses 20 Single Source of Truth (SSOT) documents covering review type decision tree, 12-phase lifecycle, PICO frameworks, search strategy, PRISMA-S/flow diagram, data extraction fields, effect size decision tree, risk-of-bias tool mapping, synthesis methods (meta-analysis + SWiM + narrative), GRADE framework, reporting standards (PRISMA + APA JARS), AMSTAR 2 self-assessment, AI failure defenses, expert methodology insights, psychology supplements, Chinese context, strict/learning mode profiles, wordcount rules. Use only when another review skill needs to load shared methodology — do NOT activate for user-facing review tasks. Other review skills are the user-facing entry points.

2026-07-07

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academic-ref-check

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