672 件 ・ 関連度順
概要と使いどころ
Encrypt and decrypt files with age (FiloSottile/age), a simple, modern encryption tool with small explicit keys, post-quantum support, no config options, and UNIX-style composability. A practical replacement for GPG in most workflows.
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。
Nature-style chemistry manuscript reviewer skill for testing synthesis, catalysis, chemical biology, molecular/materials chemistry and analytical claims against identity, purity, controls, selectivity, mechanism, scope, reproducibility and quantitative comparison.
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。
Work with the Toss Securities Open API for Korean and US stock market data, stock info, exchange rates, market calendars, account and holdings lookups, order history, buying power, sellable quantity, commissions, conditional orders, rankings, trading trends, market indicators, realtime WebSocket integration, and delegated trading workflows. Use when an agent needs to run user-delegated buy/sell/order-management loops, call or build against developers.tossinvest.com, inspect the Toss OpenAPI schema, generate client code, or operate with TOSS_API_KEY/TOSS_SECRET_KEY credentials.
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。
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。
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。
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。
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。
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。
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。
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。
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。
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。
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。
Evaluates conglomerate discount with SOTP analysis, segment-level valuation, and separation benefit quantification. Use when analyzing conglomerate value, building SOTP models, or evaluating break-up 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 bolt-on acquisitions for existing platforms with strategic fit, synergy quantification, and return contribution analysis. Use when screening add-on targets, building platform acquisition strategies, or modeling tuck-in 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 alternative data sources including satellite, NLP sentiment, web scraping, and geolocation for alpha signal generation. Use when analyzing alt data, evaluating new data sources, or integrating non-traditional signals.
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。
Measures implementation shortfall with paper portfolio comparison, delay cost attribution, and execution quality assessment. Use when measuring implementation shortfall, analyzing execution quality, or attributing trading costs.
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 signal half-life, turnover implications, and capacity constraints for systematic alpha factors. Use when analyzing signal persistence, evaluating factor decay, or estimating strategy capacity.
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 crowding with positioning analysis, valuation spread monitoring, and unwind risk assessment. Use when analyzing factor crowding, assessing unwind risk, or monitoring factor valuation extremes.
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。