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概要と使いどころ
Extracts a Stitch design and generates production code artifacts — CSS custom properties with dark mode tokens, a Tailwind v4 @theme block, and a semantic design system document. Run this before framework conversion 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。
Converts a Stitch screen, a local HTML file, or a URL into clean, platform-agnostic HTML5 + CSS — semantic markup, CSS custom properties for theming, dark mode via prefers-color-scheme, mobile-first responsive, zero framework dependencies. Works in browsers, WebViews, Capacitor, and Ionic. Only the Stitch route needs an API key.
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。
Audits Stitch-generated components for WCAG 2.1 AA accessibility issues and applies fixes — semantic HTML, ARIA attributes, keyboard navigation, focus management, and screen reader support.
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。
Captures project-specific patterns and anti-patterns into the project documentation. Loaded by other skills when they discover project-specific knowledge worth encoding for future sessions.
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。
Captures project-specific patterns and anti-patterns into the project's configuration file. Loaded by other skills (bug-killer, feature-dev, etc.) when they discover project-specific knowledge worth encoding for future sessions.
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。
Captures project-specific patterns and anti-patterns into the project's CLAUDE.md. Loaded by other skills (bug-killer, feature-dev, etc.) when they discover project-specific knowledge worth encoding for future sessions.
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 custom AI agents in Claude Code from a user's problem statement. This skill analyzes the user's use case, asks smart clarifying questions, researches the internet for similar agents (GitHub repos, blogs, Claude Code community patterns), and then architects and builds production-ready Claude Code agents — including subagents, skills, hooks, slash commands, MCP integrations, and CLAUDE.md configuration. Use this skill whenever the user wants to create an agent, build an automation workflow, set up a Claude Code subagent, design a multi-agent system, or says things like 'build me an agent for...', 'automate this with Claude Code', 'I want a subagent that...', 'help me create a workflow', 'set up an agent pipeline', or any variation of wanting Claude Code to do something specialized. Also trigger when the user mentions agent architecture, agent SDK, agentic workflows, or task delegation in Claude Code — even if they don't use the word 'agent' explicitly.
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 writing, reviewing, or optimizing CUDA kernels targeting sm_120a (RTX 5090 / Consumer Blackwell, GB202) in the imp inference engine. Triggers on CUDA/PTX kernel code, shared-memory layout, bank conflicts, tensor-core MMA (mxf4nvf4, tf32, 3xTF32/3xFP16), GEMV/GEMM/attention/quantization/GDN-scan kernels, decode tok/s under expected, kernel emitting HMMA instead of mxf4nvf4, occupancy, register pressure, __launch_bounds__, spills, PDL. Pair with `benchmark-cuda` for measurement and `check-degeneration` after hot-path 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。
Use when verifying that a model in the imp inference engine produces coherent output without repetition loops, token-stuck states, or state corruption across turns or streams. Triggers on "degenerates", "check degeneration", "repetition loop", "own own own", "stuck token", "empty content", "multi-turn regression", "does it still work", "NIAH", and after enabling CUDA graphs / changing forward pass / MoE routing / KV cache or KV dtype / GDN state or scan / sparse attention / PDL / speculation (MTP, n-gram) / ragged prefill / batched-decode kernels (smallm, producer quantize) / FA2 softmax.
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。
Decomposes portfolio risk with factor attribution, idiosyncratic risk, and marginal contribution to risk analysis. Use when decomposing portfolio risk, attributing risk sources, or analyzing factor risk contribution.
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。
Builds regime detection models with hidden Markov, threshold, and Bayesian change-point methodologies for strategy adaptation. Use when modeling regime changes, detecting market shifts, or adapting strategies to market conditions.
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。
Builds mean-variance, Black-Litterman, and risk parity optimization models with constraint management and rebalancing rules. Use when optimizing portfolios, implementing risk parity, or applying Black-Litterman views.
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。
Designs rebalancing triggers with calendar-based, threshold-based, and hybrid approaches with tax and cost optimization. Use when designing rebalancing rules, optimizing rebalancing frequency, or modeling turnover impact.
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 preliminary transaction terms including price, consideration, conditions, reps/warranties, and indemnification. Use when drafting LOIs, preparing term sheets, or summarizing negotiated deal points.
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 Monte Carlo simulations for portfolio analysis with return distribution, tail risk, and path-dependent scenario evaluation. Use when running portfolio simulations, estimating tail risk, or analyzing return distributions.
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。
Constructs multi-factor models with value, momentum, quality, size, and volatility factor definitions and portfolio construction rules. Use when building factor models, designing systematic strategies, or constructing factor portfolios.
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。
Deconstructs VC term sheets with liquidation preference waterfalls, anti-dilution mechanics, and option pool impact analysis. Use when analyzing term sheets, comparing investor terms, or modeling founder dilution.
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。
Constructs risk parity allocation with equal risk contribution, leverage optimization, and asset class volatility targeting. Use when building risk parity, equalizing risk contribution, or designing leveraged balanced portfolios.
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。
Constructs proprietary factor definitions with signal specification, universe application, and orthogonalization methodology. Use when defining custom factors, creating proprietary signals, or building factor libraries.
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 backtesting methodology with out-of-sample testing, cross-validation, and overfitting detection techniques. Use when validating backtests, detecting overfitting, or ensuring backtest robustness.
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。
Constructs stat arb strategies with pair selection, signal generation, and portfolio optimization under market neutrality constraints. Use when building stat arb models, designing market-neutral strategies, or optimizing pair portfolios.
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。
Quantifies revenue and cost synergies with build-up methodology, realization timelines, and integration cost offsets. Use when estimating deal synergies, modeling cost savings, or building synergy cases for IC.
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 ML applications in investment with feature engineering, model selection, and implementation considerations for alpha generation. Use when evaluating ML for investing, designing ML pipelines, or assessing ML strategy feasibility.
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 factor timing strategies with macro regime indicators, valuation spreads, and momentum signals for factor rotation. Use when analyzing factor timing, evaluating rotation signals, or designing tactical factor allocation.
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。