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概要と使いどころ
Analyzes buyback program design with timing optimization, price sensitivity, and EPS accretion impact modeling. Use when optimizing buybacks, modeling repurchase economics, or comparing return-of-capital alternatives.
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。
Coordinates large block execution with market impact minimization, counterparty selection, and crossing network utilization. Use when executing block trades, managing large orders, or minimizing market footprint.
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。
Structures warehouse lending for securitization programs with advance rates, eligibility criteria, and ramp-up analysis. Use when managing warehouse lines, structuring ramp facilities, or analyzing warehouse economics.
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。
Assesses operational improvement opportunities with cost rationalization, revenue stabilization, and management changes for distressed businesses. Use when evaluating turnaround plans, identifying operational improvements, or assessing management capability.
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。
Assesses leadership team capacity to scale with organizational design review, key person risk, and executive bench analysis. Use when evaluating management scalability, identifying talent gaps, or assessing organizational readiness.
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。
Evaluates synthetic CDO and CRT structures with credit default swap mechanics and funded/unfunded tranche analysis. Use when analyzing synthetic structures, evaluating credit risk transfer, or modeling CDS-based securitizations.
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。
Evaluates optimal leverage with WACC minimization, rating impact, and financial flexibility assessment across market conditions. Use when optimizing capital structure, analyzing target leverage, or evaluating rating implications.
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。
Reference patterns for search, optimization, and greedy algorithms. Covers binary search (classic and on-answer), two pointers, sliding window, greedy strategies, prefix sums, merge intervals, and monotonic stack. Use when solving problems that involve searching sorted data, optimizing over constraints, processing subarrays/substrings, or reducing range queries.
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。
Provides Mermaid diagram syntax, best practices, and styling rules for technical visualizations. Use when creating diagrams, flowcharts, sequence diagrams, class diagrams, state diagrams, ER diagrams, architecture diagrams, C4 diagrams, visualizations, or any visual documentation in markdown. Always use this skill when generating or updating Mermaid code blocks.
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。
Plan, implement, or improve an internationalization and localization strategy for UI content, formatting, and regional adaptation. Use when the user asks to add i18n, localize, translate, support multiple languages, handle regional formats, manage locale switching, or build a multilingual product.
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。
Redact rendered visual artifacts — Mermaid diagrams, SVGs, and HTML dashboards — before public disclosure, using an ordered longest-first mapping table (specific label, then namespace label, then catch-all placeholder) that swaps sensitive identifiers for descriptive stand-ins while preserving structure, node ids, and teaching value. Covers whole-word identifier replacement, node-label-vs-node-id discrimination, re-rendering the derived image from the redacted source, and verifying the result through the redaction gate. Use when a diagram or dashboard built from internal findings must ship to a public mirror without leaking the names behind it.
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。
Produce publication-ready 2D graphics with proper DPI, color profiles, typography, and export formats for print and digital media. Use when preparing figures for academic journal submission, creating graphics for print publications, ensuring graphics meet publisher technical specifications, exporting visualizations for web with proper optimization, or creating multi-format exports from a single source.
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。
Create and maintain a product or project backlog with prioritized items, acceptance criteria, and estimates. Covers user story writing, MoSCoW prioritization, backlog grooming, item splitting, and status tracking. Use when starting a new project and converting scope into actionable items, during ongoing grooming before sprint planning, re-prioritizing after stakeholder feedback or scope changes, or splitting oversized items into implementable pieces.
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。
Apply ant colony optimization and foraging theory to resource search, exploration-exploitation tradeoffs, and distributed discovery. Covers scout deployment, trail reinforcement, diminishing returns detection, and adaptive foraging strategy selection. Use when searching a large solution space where brute-force enumeration is impractical, balancing investment between exploring new approaches and deepening known good ones, optimizing resource allocation across uncertain opportunities, or diagnosing premature convergence on local optima.
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。
Deploy machine learning models to edge devices using Google AI Edge Gallery, TensorFlow Lite, ONNX Runtime, and MediaPipe. Covers model quantization (INT8/INT4), on-device inference with Gemma 4 models, Android/iOS deployment via AI Edge Gallery, hardware delegate selection (GPU/NPU/DSP), and performance benchmarking on constrained devices. Use when deploying models to mobile phones, IoT devices, or embedded systems where cloud inference is impractical due to latency, cost, or connectivity constraints.
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。
Improve an existing R-based pictogram glyph for the visualization layer. Covers visual audit of the current glyph, diagnosis of specific issues (proportions, readability, glow balance), targeted modifications to the glyph function, re-rendering, and before/after comparison. Works for skill, agent, and team glyphs. Use when a glyph renders poorly at small sizes, its visual metaphor is unclear, it has proportion issues, the neon glow effect is unbalanced, or after adding new palettes or changing the rendering pipeline.
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。
Design serialization schemas using JSON Schema, Protocol Buffer definitions, or Apache Avro. Covers schema versioning, backwards compatibility, validation rules, and evolution strategies for long-lived data formats. Use when defining a new API contract or data interchange format, adding fields to an existing schema without breaking consumers, migrating between schema versions, choosing between schema systems, or documenting data validation rules for automated enforcement.
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。
Implement layered collective defense using alarm signaling, role mobilization, and proportional response. Covers threat detection, alert propagation, immune response patterns, escalation tiers, and post-incident recovery for distributed systems and organizations. Use when designing defense-in-depth where no single guardian covers all threats, building incident response that scales with severity, or when current defense is over-reactive to every alert or under-reactive to genuine threats.
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。
Set up a Blender scene programmatically via Python (bpy) with objects, materials, lighting, camera, and environment configuration. Use when creating reproducible 3D visualization scenes, automating product or architectural rendering setup, generating multiple scene variations programmatically, building template scenes for batch rendering workflows, or integrating 3D visualization into data pipelines.
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。
Extract maximum value from existing code — performance optimization, API surface refinement, and dead weight elimination. The art of turning base code into gold through systematic identification and amplification of value-bearing patterns. Use when optimizing a working but sluggish codebase, refining an API surface that has accumulated cruft, reducing bundle size or memory footprint, or preparing code for open-source release — when code works correctly but doesn't shine and needs polish rather than a full rewrite.
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。
Build and deploy a pkgdown documentation site for an R package to GitHub Pages. Covers _pkgdown.yml configuration, theming, article organization, reference index customization, and deployment methods. Use when creating a documentation site for a new or existing package, customizing layout or navigation, fixing 404 errors on a deployed site, or migrating between branch-based and GitHub Actions deployment methods.
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。
Image optimization analysis for SEO and performance. Checks alt text, file sizes, formats, responsive images, lazy loading, and CLS prevention. Use when user says "image optimization", "alt text", "image SEO", "image size", or "image audit".
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。
React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.
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。
Details Paraglide.js i18n implementations.
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。