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概要と使いどころ
Parse and extract data from Chinese identity documents for KYC (Know Your Customer) compliance. Adapts the original kyc-doc-parse skill for Chinese ID cards, business licenses, and domestic compliance requirements. Triggers on "A股KYC", "身份文件解析", "parse Chinese ID", "KYC document", "客户身份识别", or "extract [document type]".
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。
Quality check investment banking pitch decks for A-share M&A and capital raising transactions. Adapts the original ib-check-deck skill for Chinese market standards, CAS financials, and domestic IB conventions. Triggers on "投行deck检查", "IB deck QC", "check pitch deck China", "deck quality check", "材料质量检查", or "review deck [company/transaction]".
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。
Tax-loss harvesting strategies for A-share portfolios. Adapts the original tax-loss-harvesting skill for Chinese tax rules, A-share market conventions, and domestic tax treatment. Triggers on "A股税务亏损抵扣", "亏损抵扣", "tax loss harvesting China", "税务优化", "亏损抵扣策略", or "harvest tax losses [portfolio]".
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。
Net asset value (NAV) reconciliation and tie-out for Chinese funds and investment portfolios. Adapts the original nav-tieout skill for A-share holdings, Chinese fund accounting standards, and domestic custody arrangements. Triggers on "A股净值核对", "NAV计算", "NAV tie-out China", "基金净值", "net asset value [fund]", or "portfolio valuation [date]".
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。
Screen and evaluate A-share investment opportunities. Adapts the original deal-screening skill for Chinese market data sources, A-share screening criteria, and domestic deal origination. Triggers on "A股标的筛选", "标的筛选", "screening China", "deal screening A-share", "筛选投资标的", or "screen for [criteria]".
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。
Chinese KYC (Know Your Customer) and AML (Anti-Money Laundering) rules for financial services. Adapts the original kyc-rules skill for Chinese regulatory requirements and domestic compliance. Triggers on "A股KYC规则", "中国反洗钱", "AML rules China", "KYC compliance China", "反洗钱规定", or "KYC requirements".
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 when implementing game math — vectors, transforms, interpolation, curves, random number generation, and common geometric recipes
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 when implementing procedural generation — noise-based terrain, BSP dungeons, cellular automata caves, wave function collapse, and seeded randomness in Godot 4.3+
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。
Your brand designer. Names the business (with the trademark, domain and handle checks to run before falling in love with a name), writes the voice, a one-line promise and a tagline, and briefs the look: colours, type, logo and the first things a customer sees, so they match the positioning the marketing director chose. Use when the user says "name my business", "brand this", "logo", "brand identity", "what should it look like", "tagline", or "brand voice".
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。
Drive the LTX Director (Timeline) node. Its Add Image/Text/Audio buttons are DOM-only and cannot be clicked by an agent; edit the hidden timeline_data JSON widget instead. Load when a workflow contains LTXDirector / LTXDirectorGuide / PromptRelayEncodeTimeline, or when asked to add, move, retime, or remove timeline segments (image / text / audio / motion).
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。
15-agent hierarchical mesh coordination for v3 implementation. Orchestrates parallel execution across security, core, and integration domains following 10 ADRs with 14-week timeline.
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。
Core module implementation for claude-flow v3. Implements DDD domains, clean architecture patterns, dependency injection, and modular TypeScript codebase with comprehensive testing.
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。
Domain-Driven Design architecture for claude-flow v3. Implements modular, bounded context architecture with clean separation of concerns and microkernel pattern.
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。
Live SEO data via DataForSEO MCP server. SERP analysis (Google, Bing, Yahoo, YouTube, Google Images), keyword research (volume, difficulty, intent, trends), backlink profiles, on-page analysis (Lighthouse, content parsing), competitor analysis, content analysis, business listings, AI visibility (ChatGPT scraper, LLM mention tracking), and domain analytics. Requires DataForSEO extension installed. Use when user says "dataforseo", "live SERP", "keyword volume", "backlink data", "competitor data", "AI visibility check", "LLM mentions", "image SERP", "google images", "image rankings", or "real search data".
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 slow React Router loaders by eliminating redundant DB queries, creating slim query variants, and parallelizing independent fetches. Use proactively when writing or reviewing loader code that calls domain operations services (e.g. CourseOperationsService, VideoOperationsService), or when triaging a slow page load.
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。
Grilling session that challenges your plan against the existing domain model, sharpens terminology, and updates documentation (CONTEXT.md, ADRs) inline as decisions crystallise. Use when user wants to stress-test a plan against their project's language and documented decisions.
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 deepening opportunities in a codebase, informed by the domain language in CONTEXT.md and the decisions in docs/adr/. Use when the user wants to improve architecture, find refactoring opportunities, consolidate tightly-coupled modules, or make a codebase more testable and AI-navigable.
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。
Interactive QA session where user reports bugs or issues conversationally, and the agent files GitHub issues. Explores the codebase in the background for context and domain language. Use when user wants to report bugs, do QA, file issues conversationally, or mentions "QA session".
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。
Privacy manifest (PrivacyInfo.xcprivacy) implementation including required reason APIs, tracking domains, third-party SDK declarations, and App Tracking Transparency. Use when preparing apps for App Store privacy requirements.
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。
Generate deep linking infrastructure with URL schemes, Universal Links, and App Intents for Siri/Shortcuts. Use when handling custom URL schemes, Universal Links/Associated Domains, or routing to specific content from external sources.
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。
This packages provides C++ header files for developers wishing to create R packages that processes BAM files. ompBAM automates file access, memory management, and handling of multiple threads 'behind the scenes', so developers can focus on creating domain-specific functionality. The included vignette contains detailed documentation of this API, including quick-start instructions to create a new ompBAM-based package, and step-by-step explanation of the functionality behind the example packaged in
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。
Method for scalable identification of spatially variable genes (SVGs) in spatially-resolved transcriptomics data. The method is based on nearest-neighbor Gaussian processes and uses the BRISC algorithm for model fitting and parameter estimation. Allows identification and ranking of SVGs with flexible length scales across a tissue slide or within spatial domains defined by covariates. Scales linearly with the number of spatial locations and can be applied to datasets containing thousands or more
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 ORFhunteR package is a R and C++ library for an automatic determination and annotation of open reading frames (ORF) in a large set of RNA molecules. It efficiently implements the machine learning model based on vectorization of nucleotide sequences and the random forest classification algorithm. The ORFhunteR package consists of a set of functions written in the R language in conjunction with C++. The efficiency of the package was confirmed by the examples of the analysis of RNA molecules fr
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。
Variance-stabilizing transformations help with the analysis of heteroskedastic data (i.e., data where the variance is not constant, like count data). This package provide two types of variance stabilizing transformations: (1) methods based on the delta method (e.g., 'acosh', 'log(x+1)'), (2) model residual based (Pearson and randomized quantile residuals).
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。