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概要と使いどころ
Use when evaluating diagnostic biomarker performance from case-control expression data with logistic regression and ROC curves, exporting coefficient and AUC tables together with a ROC PDF. NOT for: survival analysis, time-to-event outcomes, multiclass classification, calibration curves, decision-curve analysis, or nomogram construction.
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 estimating immune infiltration from bulk RNA-seq expression matrices with ssGSEA/GSVA, comparing case versus control groups, and generating downstream immune-score visualizations. NOT for single-cell RNA-seq, absolute cell proportion estimation, or clinical decision making.
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。
Guides Metronome usage-based billing integration decisions — event ingestion (single and batch, idempotency, billable metrics), contract design (rate cards, overrides, dimensional pricing, products), invoicing lifecycle (grace periods, finalization, Stripe sync), credit and commit management (prepaid, postpaid, thresholds, auto-recharge), and Stripe integration (arrears invoicing, tax providers, line item limits). Use when building, modifying, or reviewing any Metronome integration — including ingesting usage events, creating customers, contracts, or rate cards, designing billable metric group keys, managing credits and commits, configuring invoicing, syncing invoices with Stripe Billing, billing AI applications for token usage, setting up self-serve (PLG) pricing, reviewing customer health, commit burn, portfolios, or renewals, or migrating from Stripe Billing Meters to Metronome.
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。
Guides Stripe integration decisions across development and test environment planning (separate sandboxes vs the shared test mode sandbox), API selection (Checkout Sessions vs PaymentIntents), Connect platform setup (Accounts v2, controller properties), billing/subscriptions, tax and registrations (Stripe Tax, automatic_tax, product tax codes), Treasury financial accounts, integration options (Checkout, Payment Element), migrating from deprecated Stripe APIs, and security best practices (API key management, API key permissions, webhooks, OAuth). Use when planning, building, modifying, testing, or reviewing any Stripe integration, including choosing a development environment, accepting payments, building marketplaces, integrating Stripe, processing payments, setting up subscriptions, collecting sales tax, VAT, or GST, creating connected accounts, or implementing secure key handling.
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 synthesizing perf findings into evidence-backed recommendations and decisions.
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。
Format, create, and update concise XQUIC pull request bodies and review-state transitions. Use when asked to prepare or edit an alibaba/xquic PR body, create a draft PR from an already-pushed branch, update a PR after code changes pushed a new head, or move a validated draft toward review.
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 evaluating a decision through Charlie Munger-style mental models: inversion, incentives, quality filters, multidisciplinary reasoning, and concentrated judgment.
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 evaluating crypto decisions through a Cobie-style cycle filter: common-sense risk, narrative traps, leverage humility, and avoiding obvious stupid trades.
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。
World-class data science skill for statistical modeling, experimentation, causal inference, and advanced analytics. Expertise in Python (NumPy, Pandas, Scikit-learn), R, SQL, statistical methods, A/B testing, time series, and business intelligence. Includes experiment design, feature engineering, model evaluation, and stakeholder communication. Use when designing experiments, building predictive models, performing causal analysis, or driving data-driven decisions.
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。
SCIP optimization with PySCIPOpt. Use when facing an optimization problem with an objective, hard constraints, soft penalties, integer decisions, routing, assignment, scheduling, allocation, packing, capacity, inventory, or service-level rules. Prefer modeling and solving the problem with PySCIPOpt when it is available.
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。
Train GPT-2 scale models (~124M parameters) efficiently on a single GPU. Covers GPT-124M architecture, tokenized dataset loading (e.g., HuggingFace Hub shards), modern optimizers (Muon, AdamW), mixed precision training, and training loop implementation.
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 medical AI agents for clinical decision support, medical record summarization, diagnostic assistance, and healthcare workflow automation.
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。
Context priming before work (bd prime) and self-reflection after completion to extract patterns, gotchas, and decisions into the knowledge base.
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。
STATE.md reading, writing, and field-level updates. Provides cross-session state persistence via .planning/STATE.md with structured fields for current task, completed phases, blockers, decisions, and quick 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。
Convert requirements into structured technical specifications with architecture decisions
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。
Observation capture and retrieval across sessions. Stores decisions, discoveries, and bugfix patterns. Searchable via tags and relevance scoring.
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。
Capture, validate, query, and sync architectural patterns and design decisions in the knowledge graph
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 specification creation from codebase research. Produces requirements, acceptance criteria, architecture decisions, implementation plans, and risk analysis.
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。
Process meeting recordings and transcripts into decisions, action items, and team 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。
Transforms conversations and discussions into structured documentation pages in Notion. Captures insights, decisions, and knowledge from chat context, formats appropriately, and saves to wikis or databases with proper organization and linking for easy discovery.
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 multi-cloud architectures using a decision framework to select and integrate services across AWS, Azure, and GCP. Use when building multi-cloud systems, avoiding vendor lock-in, or leveraging best-of-breed services from multiple providers.
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。
Expert .NET backend architect specializing in C#, ASP.NET Core, Entity Framework, Dapper, and enterprise application patterns. Masters async/await, dependency injection, caching strategies, and performance optimization. Use PROACTIVELY for .NET API development, code review, or architecture decisions.
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。
Capture and serialize project state, architectural decisions, and working context. Use when preserving knowledge for later sessions, handoffs, or searchable context retrieval.
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。
Expert database architect specializing in data layer design from scratch, technology selection, schema modeling, and scalable database architectures. Masters SQL/NoSQL/TimeSeries database selection, normalization strategies, migration planning, and performance-first design. Handles both greenfield architectures and re-architecture of existing systems. Use PROACTIVELY for database architecture, technology selection, or data modeling decisions.
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。