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概要と使いどころ
Orchestrate the full ToolUniverse self-improvement cycle: discover APIs, create tools, test with researcher personas, fix issues, optimize skills, and push via git. References and dispatches to all other devtu skills. Use when asked to: run the self-improvement loop, do a debug/test round, expand tool coverage, improve tool quality, or evolve ToolUniverse.
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。
KEGG-based disease-drug-variant network research. Connects diseases to causal genes, drugs to molecular targets, and variants to pathways using KEGG's editorially curated databases (KEGG Disease, Drug, Network, Variant, Pathway). Use for drug repurposing via shared pathways, mechanistic disease-gene-drug networks, and pathway-based target discovery. Distinguishes direct (binding) vs indirect (pathway co-membership) drug-target relationships.
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。
Optimize ToolUniverse skills for better report quality, evidence handling, and user experience. Apply patterns like tool verification, foundation data layers, disambiguation-first, evidence grading, quantified completeness, and report-only output. Use when reviewing skills, improving existing skills, or creating new ToolUniverse research skills.
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。
Experimental data: discover and acquire raw or near-raw measurements, sample-level observations, and reusable datasets from sequencing, transcriptomics, single-cell, perturbation, proteomics, metabolomics, genomics, and structural-biology repositories. Use for study or sample metadata, matrices, reads, spectra, experimental structures, and downloadable research artifacts—not curated database assertions or reference marker collections.
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。
Research customer pain points, trends, and competitors through the OMA market engine. Use for market discovery or voice-of-customer analysis.
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。
When the user wants to create competitor comparison or alternative pages for SEO and sales enablement. Also use when the user mentions 'alternative page,' 'vs page,' 'competitor comparison,' 'comparison page,' '[Product] vs [Product],' '[Product] alternative,' or 'competitive landing pages.' Covers four formats: singular alternative, plural alternatives, you vs competitor, and competitor vs competitor. Emphasizes deep research, modular content architecture, and varied section types beyond feature tables.
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。
When the user wants help with pricing decisions, packaging, or monetization strategy. Also use when the user mentions 'pricing,' 'pricing tiers,' 'freemium,' 'free trial,' 'packaging,' 'price increase,' 'value metric,' 'Van Westendorp,' 'willingness to pay,' or 'monetization.' This skill covers pricing research, tier structure, and packaging strategy.
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。
Enhanced architecture guidance for voice-first Brazilian fintech applications. Use when designing voice interfaces, implementing PIX/Boletos, optimizing financial systems, or making technology stack decisions for Brazilian market applications. Integrates with docs/ content, MCP tools for Brazilian market research, enhanced validation scripts, and comprehensive Brazilian compliance 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。
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。
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。
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。
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。
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。
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。
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。
The default code-driven browser path for interactive browsing work: drives any browser from code with token-efficient a11y snapshots (57% smaller via compactSnapshot), ref-based clicks, coordinate control (CUA), viewport pinning, CAPTCHA solving (reCAPTCHA, Turnstile, hCaptcha, slider, OCR), Chrome MV3 APIs (tabs/bookmarks/history/downloads/topSites), network snoop (read API JSON instead of DOM), flight-recorder trace (jsonl + HAR + screenshots), OOPIF-aware snapshots, dialog policy, human handoff for login/OTP, device emulation, request routes, stealth via CloakBrowser as the default owned engine with zero exposed CDP ports, and an attached engine that drives the user's own signed-in browser through BrowserSkill (connectBrowserSkill, bskSnapshot, one-click onboarding via bskOnboard). MUST USE for interactive browser work: scraping, blocked/WAF/JS-rendered pages, logins, the user's logged-in sites and open tabs, extension popups, form filling, screenshots, web QA, CAPTCHAs. The ulw-research browsing lane runs on ultimate-browsing, not this skill.
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。
Runs JACKS (Joint Analysis of CRISPR/Cas9 Knockout Screens; Allen et al 2019 Genome Research) which models per-sgRNA log-fold-change as the product of a treatment-dependent gene-essentiality term and a treatment-independent guide-efficacy term. Covers the Bayesian decomposition math, the hierarchical efficacy prior shared across screens performed with the same library, when JACKS outperforms MAGeCK (multi-screen joint analysis, libraries with broad efficacy variance) and when it does not (single screen, novel libraries with no prior efficacy), library-reuse efficacy transfer, downstream essentiality interpretation, and the 2.5x sample-size reduction enabled by efficacy-aware testing. Use when running multiple screens with the same library, when guide-level noise is suspected to dominate per-gene signal, when reusing published essentiality reference screens for efficacy priors, or when comparing screens performed across cell lines that share library but differ biologically.
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。
Calculates tumor mutational burden from WES/WGS/panel data with Friends of Cancer Research harmonization equations, per-assay calibration (FDA 10/Mb = 7.8 TSO500 = 8.4 OncomineTML), synonymous/indel/germline filtering, hypermutator tiering, blood TMB, and integration with HLA-LOH and neoantigen quality (Luksza 2017 fitness). Use when assessing ICI eligibility under tumor-specific cutoffs (McGrail 2021), comparing tissue vs bTMB, or auditing TMB-H reporting against ESMO 2024 and FDA pembrolizumab pan-tumor 2020.
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 market conditions, geographic opportunities, pricing, consumer behavior, and product validation across Google Maps, Facebook, Instagram, Booking.com, and TripAdvisor.
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。
Astropy is the core Python package for astronomy, providing essential functionality for astronomical research and data analysis.
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。