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概要と使いどころ
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.
日本語の概要は準備中です。原文の説明を表示しています。
SSL/БСП subsystems guidance and search via 1c-mcp_ssl_server MCP. Use when working with standard library subsystems - users, files, print forms, background jobs, email, common utilities.
日本語の概要は準備中です。原文の説明を表示しています。
Technical SEO for AI search engines. Checks AI crawler accessibility, llms.txt compliance, robots.txt configuration, server-side rendering. Outputs problems + fixes (generates llms.txt, robots.txt modifications). Use when user says "AI crawlers", "llms.txt", "GPTBot", "GEO technical".
日本語の概要は準備中です。原文の説明を表示しています。
Content quality audit for both traditional SEO (E-E-A-T) and AI search (citability, structure, authority). Use when user says "content audit", "content quality", "E-E-A-T", "AI citability", "content analysis", or "is my content good for SEO".
日本語の概要は準備中です。原文の説明を表示しています。
Book reservations through Google Maps using browser automation. Searches for businesses, checks availability, and completes bookings via "Reserve with Google" or 3rd-party providers (OpenTable, Resy, Vagaro). Supports restaurants (primary), salons, fitness, healthcare. Use when user wants to book, reserve, or schedule at a business found on Google Maps.
日本語の概要は準備中です。原文の説明を表示しています。
Find or inspect Unity GameObjects, especially objects the user currently selected in the Hierarchy. Use for details, components, tags, layers, or name/path searches.
日本語の概要は準備中です。原文の説明を表示しています。
調査プロジェクトのワークフロー定義。3フェーズ・13ステップで構成。提案書作成から報告書作成・スライド構成設計まで、デスクリサーチとインタビューを組み合わせた調査プロジェクトを管理する。project-managerから呼び出される。
クライアントの与件情報と打ち合わせメモから、意思決定に直結する調査プロジェクト提案書（スライド骨子/Markdown）を一発で作成するスキル。「提案書を作成して」「提案書を作って」「プロポーザルを作成して」などの依頼、または与件ファイル（例：与件.md）を指定して提案書作成を依頼された場合に使用する。単なる章立てではなく、調査の観点・仮説・参入障壁や供給構造・会議転用可能なアウトプットまで落として提案する。インプットや指示からデプスインタビューが必要と推測できる場合のみ、インタビュー前提の設計を含める。
Manages shadcn components and projects — adding, searching, fixing, debugging, styling, and composing UI, including chat interfaces. Provides project context, component docs, and usage examples. Applies when working with shadcn/ui, component registries, presets, --preset codes, or any project with a components.json file. Also triggers for "shadcn init", "create an app with --preset", or "switch to --preset".
日本語の概要は準備中です。原文の説明を表示しています。
Prepare for a Japanese job interview by working from the documents already submitted: find where an interviewer will dig in (claims the applicant cannot back up, work described in the first person plural, outcomes with no measure, gaps or overlaps in the employment history, must-have requirements the documents never answer), and turn them into questions to prepare evidence for. Use when a user has an interview scheduled and wants to know what will be asked and what to have ready, or wants to prepare reverse questions from earlier company research; do not use to predict whether they will pass, to write answers containing experience they did not report, or to schedule or contact anyone.
日本語の概要は準備中です。原文の説明を表示しています。
Sort incoming scout messages and agent-forwarded openings by what they actually disclose: whether the employer is named at all, which of pay, location, employment type, and duties are stated, whether the message refers to the applicant's own history or reads as a mass send, whether a stated salary is an offer or a "up to" figure, and whether the same employer has arrived through more than one route. Use when a user has a backlog of scout messages and wants to know which ones can be researched, what to ask before replying, and what to settle before handing over a resume; do not use to rate how promising an opening is, to judge an employer or an agent, or to reply on the user's behalf.
日本語の概要は準備中です。原文の説明を表示しています。
Inventory a person's work history as facts before any document is written, listing each period of employment and what they actually did, splitting every experience into situation, their own actions, outcome, and stated role, and recording what each claim rests on (public record, their own records, someone who was there, or memory alone). Use when a user is starting a job search, needs material for a rirekisho, shokumu-keirekisho, or entry sheet, or wants to find which periods of their history they have never written down; do not use to judge their strengths, aptitude, or market value, to name a job they are suited for, or to write finished application copy.
日本語の概要は準備中です。原文の説明を表示しています。
Help a user talk through whether to change jobs, stay, or change something in their current work, starting from uncertainty, distress, a vague sense of stagnation, an offer in hand, a counter-offer from the current employer, or pressure from family or colleagues. Clarify what matters, separate deadlines that order the next steps from the decision itself, compare conditional options, and agree on a small next step and a time to revisit it. Use when someone asks whether they should quit, wonders whether leaving is just running away, has second thoughts after receiving an offer or being asked to stay, feels stuck because others say it would be a waste, wants to leave soon after joining, or wants advice before deciding to start a job search; do not make the final stay-or-leave decision, evaluate offer terms, plan the resignation, or predict hiring outcomes.
日本語の概要は準備中です。原文の説明を表示しています。
Analyze a paper draft objectively and strictly. Identifies logic gaps, missing proofs, structural issues, and research weaknesses. Reviewer mode. Use for "analyze this paper", "find weaknesses in draft". 旧名 review-analyze。
日本語の概要は準備中です。原文の説明を表示しています。
Survey related literature for a research topic or paper draft. Finds relevant papers, identifies citation gaps, and suggests key references. Use for "find related work", "survey papers on X", "what papers should I cite". 旧名 survey-literature。
日本語の概要は準備中です。原文の説明を表示しています。
研究文書（概要/梗概/アブストラクト・研究論文・卒業論文）を種別自動判別し、研究の骨子・グローバル一貫性・当てどころ・関連研究・説明不足・弱点・体裁の各面から一括レビューして教員向け統合1ファイルに集約するメタスキル。framing-check(骨子＋一貫性)・aim-check(当てどころ)・articulation-check(5W)・weakness-analyze(弱点)・lit-survey(関連研究)で検出し、shepherd で内容面を整形、format-check(概要は --fixed で紙面配分)で体裁を見る。--student で student-comment に渡し学生向け1枚も生成。論文・概要・卒論・中間発表予稿を「レビューして」「総合的に見て」と言われたときに使う。旧 abstract-review / paper-review / thesis-review / review を統合。
世の中のベストプラクティスをWeb検索・SNS検索・ツール検索で調査し、鮮度・信頼性・多角的視点を 考慮して構造化レポートにまとめる。「ベストプラクティスを調べて」「best practice」「最適な方法」 「推奨される方法」「どうするのが正解か調べて」「世の中ではどうやっているか」等で使用。 技術・ビジネス・教育・デザイン等あらゆるドメインに対応。
Builds an interactive HTML dashboard visualizing the Agent Skills ecosystem by fetching live data from github.com/agentskills/agentskills. Use this skill whenever the user wants to explore, research, or visualize Skills architecture, the SKILL.md spec, ecosystem adoption, repo stats, or any Skills intelligence dashboard. Trigger when the user mentions "skills dashboard", "skills ecosystem", "skills architecture overview", "agentskills repo", or wants to analyze how different tools implement Agent Skills. Optimized for Perplexity Computer — Computer fetches all data live, no static files required.
日本語の概要は準備中です。原文の説明を表示しています。
Read market data from the HoneyComb job position exchange. Get ticker prices, price changes, pool state, trade history, and real-time price feeds. Use when analyzing market conditions, checking prices, or researching job position tickers. No authentication required.
日本語の概要は準備中です。原文の説明を表示しています。
Use OpenAI Codex CLI as a peer collaboration partner — same standing as Claude, not a one-shot tool. Bring Codex in EARLY (ideation, framing decisions, stuck moments), not only for late peer-review. Hand it strategic context (target, research, draft, reasoning), iterate conversationally over ~3-8 turns, converge before surfacing to the human. Invoke when the user says "check / iterate / ideate with Codex" or "ask Codex" — and proactively when stuck between two reasonable framings, pressure-testing a load-bearing claim or structure, or about to ask the human to pick between options two agents could resolve.
日本語の概要は準備中です。原文の説明を表示しています。
Identifies non-coding RNAs (tRNA, rRNA, snoRNA, snRNA, riboswitches, sRNAs) using Infernal covariance-model search against Rfam, tRNAscan-SE 2.0 for tRNA, barrnap for rRNA, and ARAGORN for tmRNA, plus the small-RNA-seq boundary for miRNA and the transcript-assembly boundary for lncRNA. Covers the structure-conserved-not-sequence-conserved principle (why BLAST fails), GA-threshold and clan-competition correctness, tRNAscan-SE domain modes and pseudogene flags, rDNA copy-number collapse, and why homology annotation is a recall floor. Use when performing genome-wide ncRNA annotation, choosing the right tool for an RNA class, or interpreting ncRNA counts.
日本語の概要は準備中です。原文の説明を表示しています。
Performs proximity operations on genomic intervals with bedtools (closest, window, flank, slop) and pybedtools - nearest-feature queries with signed/strand-aware distance, fixed-radius window searches, strand-aware promoter construction, and interval extension. Covers the closest -d/-D a/b/ref/-t/-k/-io/-iu/-id flags, the -D ref strand sign-flip, silent chromosome-end clipping in slop/flank, -t all tie double-counting, and the critical distinction between a geometry answer (nearest TSS) and a biology answer (which gene an element regulates). Use when assigning peaks or variants to genes, defining promoters from a gene model, building distance-to-TSS distributions, finding features within a window, or extending intervals - and when deciding whether nearest-gene is a fair prior (GWAS locus) or a trap (distal enhancer).
日本語の概要は準備中です。原文の説明を表示しています。
Query UniProt's REST API (post-2022 endpoint at rest.uniprot.org) for protein sequences, annotations, GO terms, cross-references, ID mappings, and proteomes. Use when fetching UniProtKB entries, navigating the JSON schema, choosing between UniProtKB/UniRef/UniParc/Proteomes resources, deciding stream vs search endpoint for batch retrieval, running ID-mapping jobs with the async pattern, handling isoform suffixes, or filtering reviewed Swiss-Prot vs auto-annotated TrEMBL. Encodes the legacy URL migration (2022), the new JSON schema layout, and bulk-pull patterns.
日本語の概要は準備中です。原文の説明を表示しています。
Download large datasets from NCBI efficiently using EPost, history server, batching, rate limiting, and retry logic. Use when bulk-fetching tens of thousands of sequences, pulling all results of a large ESearch, designing reproducible pipelines, comparing E-utilities to NCBI Datasets v2 CLI, or implementing checksum-validated downloads. Encodes WebEnv TTL (~8h), EPost 200-ID limit, retmax caps, parallelization design, and integrity verification.
日本語の概要は準備中です。原文の説明を表示しています。