991 件 ・ 関連度順
概要と使いどころ
Implementing Zero Trust Network Access (ZTNA) in cloud environments by configuring identity-aware proxies, micro-segmentation, continuous verification with conditional access policies, and replacing traditional VPN-based access with BeyondCorp-style architectures across AWS, Azure, and GCP.
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Implements AWS Nitro Enclave-based confidential computing environments with cryptographic attestation, KMS policy integration using PCR-based condition keys, and secure vsock communication channels. The practitioner builds enclave images, configures attestation-aware KMS policies, validates attestation documents against the AWS Nitro PKI root of trust, and establishes isolated computation pipelines for processing sensitive data such as PII, cryptographic keys, and healthcare records. Activate...
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Guides experiment state transitions: launching, pausing, resuming, ending, shipping variants, archiving, resetting, duplicating, and copying to another project. Covers preconditions, implications for variant assignment and analysis, and the decision framework for when to use each action. TRIGGER when: user asks to launch, pause, resume, end, ship, archive, reset, duplicate, or copy an experiment to another project. DO NOT TRIGGER when: user is creating an experiment (use creating-experiments), configuring rollout (use configuring-experiment-rollout), or setting up metrics (use configuring-experiment-analytics).
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Designs genomics experiments so technical nuisance variation (batch, lane, plate, flow cell, operator, reagent lot, processing day) is balanced against the biological variable of interest and therefore estimable rather than confounded, using constrained sample-to-batch assignment (designit, OSAT), the confounder/mediator/collider distinction, and the principle that no post-hoc correction recovers a fully confounded design. Covers detecting hidden batches with surrogate variable analysis, a decision table for downstream correction (ComBat-seq, RUVSeq, SVA) whose execution is deferred to differential-expression/batch-correction, and reproducibility metadata. Use when assigning samples to sequencing batches/lanes/plates, avoiding batch-condition confounding, deciding whether a design is salvageable by correction, choosing a correction method, or estimating the number of hidden batches. For the experimental unit, randomization, and blocking concepts see experimental-design/randomization-blocking.
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Computes and interprets treatment effect measures (OR, RR, RD, HR, NNT) with calibrated confidence intervals (Wilson, Newcombe, Miettinen-Nurminen, MOVER, profile likelihood, Bender NNT) and reports marginal vs conditional estimands per FDA 2023 covariate adjustment guidance. Use when reporting treatment effects in confirmatory trials, comparing effect sizes across studies, or constructing forest plots.
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Simulate wave propagation — acoustic, electromagnetic, elastic, and quantum waves. FDTD, spectral methods, and absorbing boundary conditions for 1D/2D/3D wave equations with sources, scattering, and dispersion.
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Architectures and techniques for neural operators on discontinuous PDE solutions (shocks, contact discontinuities, steep gradients). Covers local-global spectral design (ShockFNO), reflection padding for non-periodic BCs, resolution scaling for shock width, and frequency-band error diagnostics. Use for low-viscosity Burgers, compressible Euler, Riemann problems, or any PDE where standard FNO produces Gibbs oscillations.
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Performs logistic regression for clinical trial outcomes (binary, ordinal, multinomial) with marginal-vs-conditional estimand reporting per FDA 2023 covariate adjustment guidance, g-computation/standardisation for marginal effects, modified Poisson for RR, Brant test for proportional odds, Firth penalty for separation, and Hauck-Donner detection. Use when modeling binary or ordinal endpoints in confirmatory or exploratory clinical trials.
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Plot per-group distributions of continuous data using boxplots, violins, beeswarms, quasirandom jitter, and raincloud plots with sample-size honesty (Weissgerber 2015), KDE-bandwidth awareness, and N-aware encoding choices. Use when comparing distributions across a small number of groups — expression per cluster, biomarker per arm, scores per condition — and the bar-of-mean default is misleading.
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Solve partial differential equations — finite differences, spectral methods, and physics-informed neural networks (PINNs via DeepXDE). Supports 1D/2D/3D, steady/transient, linear/nonlinear PDEs with Dirichlet, Neumann, and periodic boundary conditions.
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Explains ML predictions on omics data with SHAP, LIME, and permutation importance, handling the correlated-feature trap, the conditional-vs-interventional Shapley choice, and the attribution-is-not-causation boundary. Use when interpreting an omics classifier, debugging shortcut/batch learning, or deciding whether an attribution ranking can be trusted as biology. For validated feature selection see machine-learning/biomarker-discovery; explanations are not a selection method.
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End-to-end clinical trial analysis workflow from CDISC SDTM/ADaM loading through ICH E9(R1) estimand-driven primary analysis to CONSORT 2025 regulatory-compliant reporting. Covers data preparation, FDA 2023 marginal vs conditional logistic regression, categorical tests with Boschloo, modern HTE/subgroup methods, missing-data sensitivity (MMRM, reference-based MI, Permutt tipping point), graphical multiplicity (Bretz-Maurer), survival analysis (Cox/RMST/competing risks) when applicable, and Table 1. Use when performing a complete analysis of clinical trial data.
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PyTorch library for audio generation including text-to-music (MusicGen) and text-to-sound (AudioGen). Use when you need to generate music from text descriptions, create sound effects, or perform melody-conditioned music generation.
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Walk every branching path and boundary condition in content, report only unhandled edge cases. Orthogonal to adversarial review - method-driven not attitude-driven. Use when you need exhaustive edge-case analysis of code, specs, or diffs.
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Implement round lifecycle, game rules, objectives, announcements, win conditions, and restart-safe cleanup.
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Extract key clauses from terms and conditions documents, then generate a plain-language PDF summary for client review.
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Reads and sets the weather on a running CARLA server via get_weather/set_weather, and turns natural-language weather requests ("heavy rain at sunset", "light fog at night", "clear noon", "dust storm") into exact WeatherParameters — cloudiness, precipitation, puddles, wind, fog, sun position, wetness, dust. Use when the user asks to "set/change the weather", "make it rainy/foggy/sunny/night", "apply a weather preset", or describes any sky/lighting condition.
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Cloudflare-first platform selection. Decision trees for Workers, D1, R2, KV, DO, Queues, Vectorize, Containers, Sandboxes, Flagship, Agent Memory, Workflows v2. Default stack, override conditions, auth, data patterns, reliability.
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Comprehensive PR code review for OneKey monorepo. Use when reviewing PRs, code changes, or diffs — covers security (secrets/PII leakage, supply-chain, AuthN/AuthZ), code quality (hooks, race conditions, null safety, concurrent requests), and OneKey-specific patterns (Fabric crashes, MIUI, BigNumber). Triggers on "review PR", "review this PR", "code review", "check this diff", "审查 PR", "代码审查", "review
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Use when you need to review, improve, or build Spring Boot 4.0.x applications — including proper usage of @SpringBootApplication, component annotations (@Controller, @Service, @Repository), bean definition and scoping, configuration classes and @ConfigurationProperties (with @Validated), component scanning, conditional configuration and profiles, constructor injection, @Primary and @Qualifier for multiple beans of the same type, bean minimization, graceful shutdown, virtual threads, Jakarta EE namespace consistency, and scheduled tasks. This should trigger for requests such as Review Java code for Spring Boot application; Apply best practices for Spring Boot application in Java code. Part of cursor-rules-java project
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Reply to an applicant for an employer's Otameshi Tenshoku listing: match the application against the listed requirements item by item on what the applicant actually stated, list the questions still open, and draft an invitation, a request for clarification, a hold notice or a decline that sticks to the listed conditions, keeps other applicants and internal notes out, and presents any new work or dates as a proposal rather than an agreed term. Use when a hiring manager, recruiter or small-business owner has applications to answer, wants to know what to ask before inviting, or has to decline; do not use to judge an applicant's aptitude or predict hiring outcomes, to screen on personal attributes unrelated to the work, to design the work (trial-work-design) or the listing (trial-listing-draft), or to send the message or update applicant records on the user's behalf.
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Check, from the employer's side, what has been put in writing before a paid Otameshi Tenshoku assignment starts under gyomu-itaku: item by item whether the deliverable, delivery date, fee and how it is calculated, payment due date, who pays, out-of-scope work, revision limits, company support, rights, confidentiality and termination are stated in a contract, purchase order or email rather than in chat or by word of mouth, count the days from delivery to payment, and find pay tied to satisfaction or hiring, unpaid or unlimited revisions and one-sided termination. Use when a hiring manager, recruiter or small-business owner is about to offer terms to a candidate, has been asked by a candidate what is still unwritten, or needs the conditions written for a platform contract screen; do not use to judge whether the terms are lawful or the fee fair, to compute withholding or consumption tax, for employment contracts, or to sign, send or conclude the contract on the user's behalf.
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Plan the first months at a new Japanese employer after an offer is accepted, listing every expectation and promise made in interviews, the offer, or negotiation with its source, separating what is in writing from what was only said and what has no agreed measure, working out when the probation period ends and whether its criteria and conditions are known, and scheduling who to confirm what with before and after the start date. Use when a user has accepted an offer and wants to prepare for joining, make sure verbal promises such as remote days or a future role are confirmed, or understand what the probation period means for them; do not use to predict whether they will fit in or pass probation, to judge their manager or colleagues, to rule on the legality of probation terms, or to contact the new employer.
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Lay out how a job search will actually run alongside a current job: the hours available each week and what they go to, how weekday daytime slots for interviews will be found, which routes to use (direct applications, job boards, scout sites, agents, referrals, public employment services) and what each one discloses to whom, how to keep the search from reaching the current employer through public profiles or workplace equipment, a single tracker of every application's stage, next action, and deadline, and a date and conditions for reviewing, reducing, or pausing the search. Use when someone has decided to start looking, or to gather information while employed, and asks how to fit it in, which routes to use, whether the applications they are running fit the time they have, or how to keep track of them; do not use to decide whether to change jobs, to pick or rate employers or job postings, to predict outcomes, or to send applications, publish profiles, or contact agents on the user's behalf.
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。