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Detect, date, and contextualize whole-genome duplication (WGD / paleopolyploidy) events using wgd v2 (Chen & Zwaenepoel 2024), KsRates (Sensalari 2022 substitution-rate-corrected Ks dating), DupGen_finder (Qiao 2019), MAPS (Li 2018 phylogenomic), POInT (Conant 2008 ordered-block), SLEDGe (2024 ML-based), Whale.jl (Bayesian DTL+WGD), and synteny-anchored paranome construction. Use when identifying ancient polyploidy from Ks distributions and synteny block analysis, positioning WGD events relative to speciation, distinguishing tandem from segmental from WGD duplications, dating the 2R/3R vertebrate / fish / salmonid WGDs, building paranome and Ks-age mixture models, applying KsRates substitution-rate correction across lineages, or testing alternative biased-fractionation / dosage-balance models post-WGD.

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。

Estimate SNP heritability and partition it across functional annotations, cell types, and loci from GWAS summary statistics or individual-level genotypes. Implements LDSC, stratified LDSC with the baseline-LD model, Finucane 2018 cell-type prioritization, LDAK SumHer, HDL, HESS local heritability, BOLT-REML, GCTA-GREML, graphREML, and Popcorn cross-population genetic correlation. Use when computing total h2_SNP from summary stats, partitioning heritability across functional categories, prioritizing trait-relevant tissues or cell types from ENCODE/Roadmap chromatin marks, reconciling LDSC vs LDAK enrichment estimates, computing local heritability with HESS, estimating genetic correlation between traits, or producing publication-grade enrichment with calibrated sensitivity to model assumptions.

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。

Estimate bivariate genetic correlation (rg) between traits from GWAS summary statistics or individual-level genotypes using cross-trait LDSC, HDL, LAVA, rho-HESS, GREML-bivariate, Popcorn, and HDL-L. Use when quantifying shared genetic architecture between two traits, screening MR validity before causal inference, distinguishing global from locus-level rg, estimating trans-ancestry rg, separating partial from full causation via LCV gcp, or producing a STROBE-MR-compliant cross-trait sensitivity battery. Cross-trait LDSC intercept absorbs sample overlap and is NOT a bias; HDL is biased under sample overlap above ~5%. High rg between exposure and outcome motivates CHP-aware MR sensitivity (CAUSE, LHC-MR).

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 gene-level association from GWAS summary statistics via genetically predicted tissue expression using FUSION, PrediXcan, S-PrediXcan, S-MultiXcan, UTMOST, MOSTWAS, kTWAS, EpiXcan, TIGAR-V2, and probabilistic fine-mapping with FOCUS and MA-FOCUS. Use when running TWAS from GWAS sumstats, prioritising candidate causal genes from a GWAS lead locus, picking single-tissue vs cross-tissue models, identifying LD-induced TWAS false positives, choosing ancestry-matched prediction weights, fine-mapping co-regulated TWAS hits, or triangulating TWAS with cis-eQTL Mendelian randomization and colocalization to nominate a causal gene.

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。

Detect allele-specific chromatin accessibility from ATAC-seq using WASP, GATK ASEReadCounter, or RASQUAL. Use when mapping cis-regulatory genetic variants from heterozygous SNPs, separating cis from trans regulation, building chromatin QTL (caQTL) maps, validating GWAS variant function with allelic imbalance, or detecting reference allele mapping bias before downstream 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。

Migrate configuration from Bluejay voice AI testing platform to Coval. Use when customer says "migrate from bluejay", "bluejay migration", "import bluejay config", or needs to transfer agents, simulations, metrics, and schedules from Bluejay to Coval.

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 needs to set up multiple academic courses in a learning management system (Canvas/LMS) from structured data sources. This skill automates the entire workflow extracting course schedules from emails/attachments, matching instructors from CSV files, creating courses, enrolling teachers, publishing announcements with class details, uploading syllabi, enabling resource sharing for instructors teaching multiple courses, and publishing all courses. Triggers include course schedule setup, Canvas/LMS administration, academic term preparation, instructor assignment, syllabus distribution, and multi-course management.

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 this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.

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。

Perform a full InDesign data merge from a CSV/TSV and an .indd template (or a PDF that gets converted to .indd automatically). Use this skill whenever the user wants to merge a data file with a layout template — including visiting cards, certificates, badges, catalogs, mailers, labels, invoices, or any per-row personalisation. Triggers on: "data merge", "InDesign merge", "merge my CSV with InDesign", "batch export PDF from template", "variable data InDesign", "personalise each row", or any request combining an .indd / PDF template with a data file. Use this skill even when only two of the three inputs (template, data, images) are mentioned — and even when the user never uses the phrase "data merge".

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。

Discover a single-page-app's hidden backend API from its public JS bundle, then test that API for broken access control / missing authentication. One of the highest-yield web plays in modern recon — SPAs ship their entire backend route map to the browser, and the API behind them is frequently missing the auth middleware the login page implies. Built from an authorized engagement where this play found an unauthenticated financial API that an ASM scan reporting hundreds of 'Criticals' completely missed. Use whenever a target serves a JS-heavy SPA (React/Vue/Angular/Next), an 'app'/'console'/'dashboard'/'portal' subdomain, or any `*api*` host shows up in recon. Leaked build artifacts (source maps / .env / .git / asset-manifest) are owned by hunt-source-leak; API version-inventory and behavioral diffing by hunt-shadow-api; this skill owns mapping a live SPA's backend routes from its JS bundle and testing them for broken access control / missing auth.

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。

Fetch and display BBC News stories from various sections and regions via RSS feeds. Use when the user asks for BBC news, UK news headlines, world news from BBC, or news from specific BBC sections (technology, business, politics, science, health, entertainment, regional UK news, or world regions).

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 this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.

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。

Web scraping, search, crawling, and page interaction via the Firecrawl CLI. Use this skill whenever the user wants to search the web, find articles, research a topic, look something up online, scrape a webpage, grab content from a URL, extract data from a website, crawl documentation, download a site, or interact with pages that need clicks or logins. Also use when they say "fetch this page", "pull the content from", "get the page at https://", or reference scraping external websites. This provides real-time web search with full page content extraction and interact capabilities — beyond what Claude can do natively with built-in tools. Do NOT trigger for local file operations, git commands, deployments, or code editing tasks.

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。

Bulk extract content from an entire website or site section. Use this skill when the user wants to crawl a site, extract all pages from a docs section, bulk-scrape multiple pages following links, or says "crawl", "get all the pages", "extract everything under /docs", "bulk extract", or needs content from many pages on the same site. Handles depth limits, path filtering, and concurrent extraction.

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。

Requires OFOX_API_KEY — create one at https://app.ofox.ai, plus a video you already have. Makes an existing clip longer, or replaces its ending. Use when a user wants more of footage they already have, e.g. "extend this 5-second clip to 15", "keep going from where this one ends", "re-shoot the ending from 4 seconds on", or "add another shot onto this". A frame is pulled out of the clip at zero cost and becomes the first frame of a newly generated segment, which is then joined onto the original. Do not use to change what is inside the picture — measured, the API accepts an edit mode field and silently ignores it, so no content-editing route exists here and none returns an error either; for two stills you already have see keyframe-animation, and for a clip from nothing see the seedance-* scenarios.

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。

Requires OFOX_API_KEY — create one at https://app.ofox.ai. Produce a set of product images to choose between — several styles, backgrounds or treatments of one product, for a listing, a store page or an ad — and quote the whole set's cost before any of it is spent. From a real product photo it edits that photo once per style, so the item stays identical across the set; for a fictional or prototype product with no photo it generates from text instead, and says what that costs in accuracy. Delegates to ofox-image-core. Use when a user asks for several product images at once, e.g. "give me 4 main images in different styles for this product", "a few background options for this photo", "some listing images for my shop", or "show this product on white, on wood, and in a lifestyle scene". Do not use for a single change to a single image (see image-edit), for video from a product photo (see seedance-product-video), or for one plain text-to-image render (that is ofox-image-core's `generate`).

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。

Audit or design server-side indexes, directories, reputations, or aggregates that are DERIVED from traffic a platform witnesses (chain heads, run verdicts, heartbeats, publish activity) so that consent gates the derivation, not merely the read: no shadow index over non-consenting principals, post-consent-only evidence windows, retention bounds and delete-propagation into derived rows, activity-oracle threat modeling, and audit-logged ranking discretion. Use when building a capability directory / whois from witnessed activity, a reputation ledger from settled outcomes, vitals aggregation across tenants, or any "the yellow pages can't lie because they index signatures" feature. Keywords: shadow index, derived index, consent gates derivation, activity oracle, phone book not a log, delist propagation, ranking weight audit, k-anonymous aggregate, subpoena surface. NOT for the consent-screen UX and scope-ladder mechanics themselves (use local-first-tenancy-boundary), reputation scoring math and Sybil economics (use agent-identity-continuity-reputation), or discovery-layer architecture L1/L2/L3 (use agent-discovery-directories-guilds).

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。

Generates OpenAPI 3.0/3.1 specs, TypeScript client types, and curl examples from existing route handler code. Supports Express, Next.js API routes, Fastify, and Hono. Produces complete documentation including auth requirements, rate limits, error codes, and request/response examples. Activate on: 'generate API docs', 'OpenAPI from code', 'document endpoints', 'API reference', 'swagger generation', 'endpoint documentation'. NOT for: API design from scratch (use api-architect), deployment (use devops-automator), testing (use test-automation-expert).

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。

OpenClawのリリース情報を確認し、Discord向けの告知文を作成します。版ごとの更新手順や利用者に試してほしい点を整理し、投稿にも対応します。

• beta版の告知文を作りたいとき
• stable版の更新方法を案内したいとき
• 利用者向けのテスト項目の整理

OpenClawのブラウザ操作で、ログイン状態やタブを確認しながら複数手順の作業を進めます。古くなった操作対象の参照やタイムアウトへの対処も扱います。

• 複数手順のウェブ作業を進めたいとき
• 操作前にログイン状態を確認したいとき
• タブの再利用と重複整理

OpenClawの機能分類とQAの証拠を照合し、品質・機能の充足度・長期サポートの評価を見直して、成熟度のスコアカード文書を更新するスキルです。

• 機能領域ごとの成熟度を見直したいとき
• 想定した利用手順の充足度を確認したいとき
• QAの証拠から評価文書を更新したいとき

AIエージェントの作業ログから、GitHubのPRやissueに添える記録を作ります。関連部分を絞り、機密情報を除いてプレビューと掲載を扱います。

• PRに添えるエージェント作業記録の作成
• ローカルログから関連する依頼と結果を抽出
• 機密情報を除いた記録のプレビュー

現在のブランチの変更差分だけを点検し、冗長なコメントや不要な防御処理、型の強引な変換、周囲と合わない書き方を、動作を変えずレビュー前に整理するスキル。

• AI生成コードの冗長コメント整理
• 不要な防御処理や型変換の点検
• 変更箇所を周囲の書き方にそろえる

Feishuのナレッジベースをたどり、ページの所在確認や作成・移動・名前変更を支援します。リンクから対象を特定し、文書の閲覧・編集につなげます。

• wikiリンクの対象を確認したいとき
• ナレッジベースの階層を確認したいとき
• 指定した親の下にページを作りたいとき