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概要と使いどころ
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 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。
Evaluates uptier exchanges, drop-down transactions, and covenant-stripping maneuvers as aggressive liability management tools. Use when analyzing LMTs, evaluating creditor-on-creditor violence, or assessing cooperation agreement strategies.
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 local currency government and corporate bond markets with yield analysis, inflation dynamics, and FX carry assessment. Use when analyzing local currency debt, evaluating EM bond opportunities, or assessing carry strategies.
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 geographic expansion strategies with market entry sequencing, localization requirements, and international unit economics. Use when analyzing expansion plans, assessing international readiness, or modeling geo-expansion 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 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 commodity price exposure with forward curve analysis, hedging strategies, and break-even price sensitivity. Use when analyzing commodity risk, designing hedging programs, or stress testing price assumptions.
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。
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 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。
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 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。
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。
Specific optimization strategies for Python scripts working with larger-than-memory datasets via Dask.
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。
Next.js 16 caching model expertise covering the 'use cache' directive, cacheLife() API, cacheTag() for invalidation, cacheComponents configuration, and Partial Prerendering (PPR). Use when implementing caching strategies in Next.js 16+ applications, migrating from unstable_cache, or optimizing server component rendering.
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。
Feature flag lifecycle management -- safe feature toggling, gradual rollouts, A/B testing patterns, flag cleanup strategies, and technical debt prevention. Covers LaunchDarkly, Unleash, OpenFeature, and custom 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。
Analyzes project dependencies, detects outdated packages, identifies breaking changes, and suggests safe update strategies. Helps maintain dependency health and security.
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。
Database design and optimization specialist. Schema design, query optimization, indexing strategies, data modeling, and migration planning for relational and NoSQL databases.
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。
Guide Git branching strategies, branch naming, and merge operations. Use when: creating branches, merging, pull requests, Git workflow questions. Keywords: branch, merge, PR, pull request, GitFlow, GitHub Flow, 分支, 合併, 工作流程.
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 production-ready React/Next.js code from Figma designs. Instructs proper Figma MCP tool usage, component reuse strategies, and variant mapping. **CRITICAL: ALWAYS use this skill when user provides a Figma link or mentions implementing/redesigning from Figma design.** Never attempt manual implementation without invoking this skill first.
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 the user needs to deploy, operate, secure, or scale running infrastructure — cloud architecture (multi-region, VPC, FinOps), CI/CD and deployment strategies, secrets/IAM/supply-chain security, observability and SLO alerting, SRE and incident response, Kubernetes vs ECS and autoscaling, Terraform/Pulumi/CDK, or platform engineering and DORA metrics. Also for generating runbooks, pipeline configs, Terraform modules, or Kubernetes manifests. For choosing the system's architecture or stack before it runs (monolith vs microservices, which database), use software-architect.
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。
Audit, classify, and selectively forget stored memories. Covers memory enumeration and classification by type/age/access frequency, staleness detection for outdated references, fidelity checks using external anchors, a decision tree for selective withdrawal, tombstoned deaccession so removed content stays recoverable, counter-memory inoculation for failed strategies that would otherwise be re-derived, preemptive filtering rules for what should never become memories, and an audit trail so forgetting itself is reviewable. Use when memory has grown large and uncurated, when project state has shifted significantly since memories were written, when retrieval quality has degraded, or as periodic maintenance alongside manage-memory.
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 and execute A/B tests for ML models in production using traffic splitting, statistical significance testing, and canary/shadow deployment strategies. Measure performance differences and make data-driven decisions about model rollout. Use when validating a new model version before full rollout, comparing candidate models trained with different algorithms, measuring business metric impact of model changes, or when regulatory requirements mandate gradual rollout.
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 secure secrets management in Kubernetes using SealedSecrets for GitOps, External Secrets Operator for cloud secret managers, and rotation strategies. Handle TLS certificates, API keys, and credentials with encryption at rest and RBAC controls. Use when storing sensitive configuration for Kubernetes applications, implementing GitOps where secrets must be version-controlled, integrating with AWS Secrets Manager or Azure Key Vault, rotating credentials without downtime, or migrating from plaintext Secrets to encrypted solutions.
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。