Research & Learning skills

Monitor blogs and RSS/Atom feeds via blogwatcher-cli tool.

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Write ML papers for NeurIPS/ICML/ICLR: design→submit.

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Query Polymarket: markets, prices, orderbooks, history.

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Search arXiv papers by keyword, author, category, or ID.

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Client advisory communication with fiduciary tone, compliance-friendly language, and plain-language financial education

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Guide for authoring specialized AI agents. Use when creating, updating, or improving agents, choosing models, defining focus areas, configuring tools, or learning agent best practices.

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Fresh adversarial code review with binary PASS/FAIL verdicts, evidence citations, and anchoring bias prevention via fresh reviewer spawning.

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Add a new journal to the MedSci Skills profile database. Extracts metadata from author guidelines, generates write-paper (detailed) and find-journal (compact) profiles in canonical format with quality gates.

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Expert knowledge of academic writing standards for peer-reviewed papers, including citation integrity, style compliance, clarity, and scientific writing best practices. Use when reviewing or editing academic manuscripts, papers, or research documentation.

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Detects content similarity, verifies standardized citations and abbreviations, and flags potential academic integrity risks; use it before submission, during academic writing QA, or for compliance reviews.

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Self-review, audit, or verify CS/AI/ML paper drafts as a critical peer reviewer. Three-round review (evidence→argument→style) with Verification Status and debt tracking. Use when: reviewing a paper draft before submission, checking evidence compliance of claims, simulating peer reviewer feedback, verifying citation closure and evidence debts, performing cross-section consistency checks. Triggers on: 审修, self review, 自查, verification, revise, 修订, check draft, 审稿, evidence compliance, peer review, 论文审查, draft audit, 验证论文, 检查引用, cross-section review.

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Creates high-quality content (blog posts, tweets, newsletters, documentation) that matches the user's writing style and voice. Performs web research to find citations and supporting evidence. Use when user requests blog posts, marketing content, newsletters, tweets, or any written content that should sound authentic and be well-researched.

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Technical research methodology with YAGNI/KISS/DRY principles. Phases: scope definition, information gathering, analysis, synthesis, recommendation. Capabilities: technology evaluation, architecture analysis, best practices research, trade-off assessment, solution design. Actions: research, analyze, evaluate, compare, recommend technical solutions. Keywords: research, technology evaluation, best practices, architecture analysis, trade-offs, scalability, security, maintainability, YAGNI, KISS, DRY, technical analysis, solution design, competitive analysis, feasibility study. Use when: researching technologies, evaluating architectures, analyzing best practices, comparing solutions, assessing technical trade-offs, planning scalable/secure systems.

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Expert legal research agent for finding and scraping expungement data state by state. Knows authoritative sources, URL patterns, Firecrawl configuration, and 2026 legal landscape. Activate on "find expungement data", "scrape state laws", "legal research", "court URLs", "statute sources", "Clean Slate laws", "automatic expungement research". NOT for interpreting laws (use national-expungement-expert), building UI, or legal advice.

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ALWAYS use this skill whenever you would otherwise ask the user a question in free-form chat -- for clarifications, confirmations (especially destructive actions), missing parameters, multiple-choice decisions, or structured form input. Elicitations are routed through the Unique AI Platform UI via `unique-cli elicit create` + `elicit wait` (the default polling pattern) or a single supervised `unique-cli elicit ask` call (only when your agent instructions say your environment supervises the wait) so the user gets a proper structured prompt and you get a structured answer back. Do NOT ask the user in plain chat when you can use this skill instead.

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学術論文のサーベイと要約を行うときに使用する。

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入試の志望理由書や総合型選抜書類に生成 AI を使った場合の扱いを決めたい、 出願要項に AI 使用可否をどう書くか悩む、AI 検知ツール導入を検討している、 志望理由書に AI 使用の疑義が生じた時の判断基準を整備したい、 不正認定と教育機会のどちらで運用するか迷う場面で使う入試部門職員向けスキル。 文科省通知を骨格に、3 類型判断・二重確認プロセス・運用語彙選択・ツール未導入大学向け代替案を提供する。

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授業のシラバスに生成 AI の使用ルールを書き込みたい、学生に AI 使用可否と範囲を明示したい、 学科で AI 使用の共通テンプレートを作りたい、同僚教員から「うちの授業でどう書けば」と聞かれた、 教務委員会でシラバス記載ルールを議論する必要がある場面で使うシラバス AI 方針設計スキル。 4 段階テンプレート（自由／場面限定／完全禁止／その他）と科目類型別選択肢カタログで、 教員が 10 分で決められる判断フローを提供する。

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論文投稿時に生成 AI 利用を開示すべきか判断したい、学位論文における AI 使用の許容範囲を学生に伝えたい、 科研費申請で AI を使っていいか相談された、教員から「こういう使い方は OK か」と質問を受ける、 研究倫理セミナーで AI 使用の説明を求められる場面で使う研究支援職員向けスキル。 3 原則（補助ツール／二次的出典／独自視点再構築）と 3 場面別（投稿論文・学位論文・科研費）の 開示判断フローで、禁止事項 6 項目と日本学術振興会書式に合わせた開示テンプレを提供する。

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「学生調査の自由記述回答を AI で分析したい」「授業評価アンケートの傾向を可視化したい」 「大学評価業務で膨大な記述データを整理したい」「新入生アンケートの定性データからカテゴリを抽出したい」 場面で使う IR 担当者向けスキル。匿名化前処理・カテゴリ抽出・クラスタリング・検証の 7 ステップで、 Web 版 AI 禁止ゾーンと運用ラインを構造化する。

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ワーカーClaudeを派遣して作業を委譲する。窓口は司令塔であり、 手を動かす実作業は原則としてワーカーに任せる。 ユーザーから作業の依頼を受けたとき、ファイル編集・実装・調査等の 実作業が発生する場合に発動する。

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顧客企業の情報、業界動向、導入事例を調査し、提案・プリセールス活動に役立つインサイトを構造的にまとめるスキル。

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Microsoft Learn の公式ドキュメントを調査し、正確で最新の情報を構造的にまとめるスキル。Azure サービスの仕様確認、機能比較、ベストプラクティスの調査に使用する。

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システム調査エージェント - コードベースの構造・技術スタック・問題点・DDD適合性を調査。/system-investigation [対象パス] で呼び出し。

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