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概要と使いどころ
Evaluates tender offer structures with premium analysis, proration risk, and conditional tender strategies. Use when analyzing tender offers, calculating tender economics, or evaluating proration 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。
Evaluates dual-class share structures with discount analysis, unification catalysts, and governance reform probability assessment. Use when analyzing share class spreads, evaluating unification likelihood, or assessing dual-class dynamics.
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 proxy fight mechanics with shareholder base analysis, ISS/Glass Lewis recommendations, and vote probability modeling. Use when analyzing proxy contests, assessing vote outcomes, or evaluating director nomination campaigns.
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 management buyouts and take-private proposals with fairness assessment, minority squeeze-out mechanics, and appraisal rights analysis. Use when analyzing going-private deals, evaluating MBO fairness, or assessing minority shareholder protections.
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 PLG dynamics with viral coefficients, freemium conversion, product-qualified leads, and expansion revenue mechanics. Use when analyzing PLG companies, assessing virality, or evaluating product-driven acquisition.
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 insider purchase patterns with cluster buying identification, historical signal analysis, and conviction scoring. Use when analyzing insider buying, assessing management confidence signals, or tracking insider activity 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。
Evaluates Dutch auction mechanics with optimal pricing strategy, odd-lot proration, and shareholder base analysis. Use when analyzing Dutch auctions, modeling tender strategies, or evaluating self-tender 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。
Evaluates activist investor campaigns with thesis assessment, proposed changes, and likely outcome analysis. Use when analyzing activist situations, evaluating campaign theses, or assessing activist track records.
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 governance-related catalysts with board refreshment, compensation reform, and shareholder proposal analysis. Use when analyzing governance catalysts, evaluating shareholder proposals, or assessing governance improvement potential.
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。
Standards for Helm charts in SDD projects, including settings-driven 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。
Scaffolds Helm charts for SDD components, driven by component settings.
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。
Scaffolds the config component for centralized configuration management, driven by component settings.
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。
Scaffolds Node.js/TypeScript backend components with CMDO architecture, driven by component settings.
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 skill should be used when the user asks to "add a Lottie animation", "play a .lottie or .json animation", "integrate a Bodymovin/After Effects animation on web or mobile", "control Lottie playback / segments", "make a scroll-driven Lottie", "recolor a Lottie at runtime", or "export an AE animation to Lottie". Covers dotLottie/lottie-web integration, playback control, interactivity, theming, and the AE export checklist.
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 skill should be used when the user asks to "build a scroll animation", "add ScrollTrigger", "pin a section while scrolling", "scrub an animation to scroll", "create a hero timeline", "do a horizontal scroll section", "split text and animate words", "animate a layout change with GSAP Flip", "sync ScrollTrigger with Lenis", "Lenis smooth scroll jittery", "ScrollTrigger markers drift", "ScrollTrigger pins break with Lenis/Locomotive", or "set up smooth scroll with GSAP pinning". Covers GSAP timelines, ScrollTrigger, SplitText, Flip, and Lenis/Locomotive smooth-scroll sync for code-driven web 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。
Use when developing or extending EZ_math_model itself from a written implementation plan, especially adding algorithms, tool skills, templates, or multi-step repo changes.
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。
Flatten nested conditionals with early returns or table-driven methods - keep nesting depth under 3 levels
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。
Write the test first, watch it fail, write minimal code to pass
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。
Execute implementation plan by dispatching fresh subagent for each task, with code review between 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。
Build UI interactions whose motion comes from a physical model instead of durations and easing curves. Turn a feel described in words ("a toggle that feels heavy", "a drawer with a magnetic snap", "a card you throw into a corner", "a knob with heavy detents", "a sticky slider") into a production component driven by mass, stiffness, damping and force fields (magnets, friction, walls), on a line, in a plane or around a dial, plus a test rig that plots position, velocity and the force field, overlays the model's prediction on the measured motion, and exports the same spring to Framer Motion, react-spring, SwiftUI, Compose or CSS linear(). Use this whenever someone asks for an animation, transition, spring, gesture, drag, flick, snap points, bottom sheet, toggle, slider, carousel or any micro-interaction that should "feel" a certain way, or wants to tune or explain a spring, even if they never say "physics".
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。
Programmatic SEO planning and analysis for pages generated at scale from data sources. Covers template engines, URL patterns, internal linking automation, thin content safeguards, and index bloat prevention. Use when user says "programmatic SEO", "pages at scale", "dynamic pages", "template pages", "generated pages", or "data-driven 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。
Execute plans via autonomous agents with two-stage review per task. Use for complex implementations requiring quality gates.
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。
Comprehensive 7-phase SEO optimization workflow covering technical SEO auditing, keyword research strategy, on-page optimization, content SEO and E-E-A-T, structured data implementation, link building, and AI search optimization. Use when auditing search engine visibility, diagnosing ranking issues, implementing schema markup, conducting keyword research, optimizing content for E-E-A-T signals, or adapting for AI-driven search (AI Overviews, SGE).
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 structured Product Requirements Documents using hypothesis-driven methodology with Implementation Phases tracking.
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。