10건(Imbad0202의 저장소) · 인기순
Screen study titles, abstracts, and full texts against confirmed eligibility rules. Two AI reviewers assess records separately, with a third resolving conflicts, while outputs document exclusion reasons, selection counts, and the methods used.
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Plan, draft, and revise academic papers with support for literature reviews, bilingual abstracts, citation checks, and responses to reviewers. The skill also formats manuscripts for publication requirements and produces Markdown, LaTeX, DOCX, or PDF outputs.
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Coordinate a research project from topic exploration to a final manuscript. The skill connects research, writing, integrity checks, review, and revision workflows, tracks deliverables, and records progress with user confirmation at stage boundaries.
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Develop a research question, search and verify literature, and combine findings into an academic report. Modes also support guided research discussions, claim checking, paper comparisons, and systematic reviews with optional meta-analysis, which statistically combines study results.
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Simulate peer review through five perspectives covering journal fit, methods, subject expertise, interdisciplinary implications, and counterarguments. Receive separate review reports, an editorial decision, and a revision roadmap, or check whether revisions address earlier comments.
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Support academic work from refining a research question to literature reviews, manuscript drafting, citation checks, and revision. Select workflows for the current stage, including study selection against eligibility criteria, peer review, and experiment planning.
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Experiment executor and monitor for academic research. 2-agent system covering code experiments (ML training, statistical analysis, ETL, simulation) and human studies (surveys, field studies, interviews). 4 modes: run (execute + monitor code), manage (track human studies), validate (statistical interpretation + reproducibility verification), plan (Socratic experiment design). Use when a researcher wants to run or monitor a research experiment, manage or resume a human-subject study, check the statistics or reproducibility of research results, or design an experiment or study, in English or Chinese (e.g., 跑實驗、管理研究、驗證結果、規劃實驗).
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台灣正式文件撰寫助手 — 涵蓋政府公文、政府機關其他文件、法律文件、人民對政府文書四類中文正式文件的撰寫（不含學術論文、商業文書、私人書信等，見下方排除清單）。 根據使用者意圖自動判斷文件類別，載入對應的撰寫規範與格式指引。 觸發此 skill 的情境： - 政府公文：簽、函、書函、公告、令、呈、咨、箋函、便簽、行文、發文、簽辦、擬稿、陳核、簽稿併陳、以稿代簽、先簽後稿、上行文、下行文、平行文 - 政府機關其他文件：會議紀錄、開會通知單、新聞稿、聲明稿、施政報告、出國報告、施政計畫書、業務計畫書、裁處書、訴願決定書、聘函、獎狀、證書 - 法律文件：存證信函、律師函、合約書、備忘錄(MOU)、保密協議(NDA)、聲明書、切結書、委託書、授權書 - 人民對政府文書：陳情書、申請書、訴願書、異議書、申覆書 - 描述需要撰寫正式文件的情境（如「幫我寫一封給教育部的公文」「我要寄存證信函」「幫我寫陳情書」） - 詢問任何正式文件的格式或用語規範 - 公務員考試或受訓的公文練習題 - 公文案件後續：催辦、展期、補正、否准、陳述意見通知、陳情答復、原函更正、會辦、會勘、會議異動、公務電話紀錄 不應觸發的情境： - 商業 email、求職信、履歷 - 學術論文、研究計畫書 - 商業計畫書、簡報 - 私人書信、感謝卡 - 翻譯任務 - 純粹的文章潤飾或改寫
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Trains the HUMAN user's critical thinking through four modes: drill (argument-analysis items with a single defensible answer, judge stance), scene (Socratic exploration of synthetic scenes or user-supplied material, no verdicts on interpretations; includes a configure track — design the information request and verification plan for a decision before any analysis), expedition (guided audit of impossible-tier problems from verified packs), and detective (a runtime-generated multi-layer case worked as an escape room, guide-and-judge stance). Use when the user wants to practice critical thinking, analyze arguments, hunt assumptions, examine bias, train reasoning, or decide what information a decision needs first. Triggers: critical thinking practice, train my thinking, drill, scene, byom, configure, information plan, what would I need to know, spot manipulation tactics, scam literacy, 批判思考練習, 話術辨識, detective, 查案, 破案, 偵探.
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Produces a deep, honest peer-review of how someone uses Claude Code by statistically analyzing their local session data (~/.claude/usage-data/ and ~/.claude/projects/). Trigger whenever the user asks to review, analyze, audit, or critique their own Claude Code usage, workflow, habits, or skill level — including phrases like "analyze my cc usage", "review my cc sessions", "peer review my cc workflow", "deeper than /insights", or any ask for an honest audit of their AI workflow. Also covers portfolio/hiring-manager framings (e.g. "portfolio for Anthropic/OpenAI/xAI"), but ALWAYS ask the user in Step 0 which version to build (self / hr / both) before running — never silently produce an HR report, because that version goes to outsiders and needs explicit privacy setup. The HTML report is laid out ledger-first for SELF (opening band, output/team/leak/trend ledgers, then peer review, 9-dim scoring, try-this-week, case study, claim-indexed evidence). The HR variant is a recruiter version v1: identity card, earned badges (threshold-based, earned-only), allowlist-filtered output ledger, one case study, and a methodology & scope disclosure — no scores, no peer-review memo, no charts.
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