'versus' 검색 결과

Turn an article URL into a clean, vault-ready markdown note with the defuddle CLI, stripping ads, navigation and comments and cutting 40-60% of tokens versus a raw fetch. Prefer it over a raw web fetch whenever the task is reading or ingesting a normal web article. Triggers: "/defuddle <url>", "clean import this page".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Watch incoming feedback from outside engineers on your open-source repos: comments, reviews, issues, mentions. Use when you need to find who replied to your pull requests or issues, triage human versus bot responses, and answer within a day with evidence.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Show what this repository has already decided about its vulnerabilities, read from the durable record rather than by scanning again — open versus resolved, the decision taken on each, and what is still waiting. Use when picking up triage after a break, auditing past decisions, onboarding to a repository with security history, or checking nothing high-severity is open before a release.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Knowledge base from the NASA Fault Management Handbook (NASA-HDBK-1002, Draft 2, April 2012). Use for the systems-engineering discipline of designing what a system does when it fails — the fault/failure/anomaly vocabulary, the five FM strategies (two prevention strategies — design-time fault avoidance and operational failure avoidance — plus three tolerance strategies — failure masking, failure recovery, and goal change), the six FM process activities across NASA mission phases Pre-A to E, response-latency-versus-time-to-criticality (TTC) design trades, fault/failure containment regions and FEPPs, the four redundancy approaches, top-down FM requirements development, FM verification vs. validation, the seven dedicated FM milestone reviews (FMCR/FMARR/FMPDR/FMCDR/FMTRR/FMLRR/FMCERR), FM organizational structure, and mined NASA Lessons Learned. Space-mission oriented (flight/ground/operations); spacecraft examples throughout. IMPORTANT: built from an UNAPPROVED DRAFT — several sections (detailed architecture building blocks, the Assessment & Analysis guidance, and Operations & Maintenance) are placeholders in the source and are thin here. Thin on detailed FDIR algorithms, terrestrial/IT fault management, and any approved-standard terminology that may have changed since Draft 2.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Trace BIRD routing-daemon behavior through the actual C source tree. Use for implementation questions, suspected upstream bugs, parser/keyword origins, filter instructions, protocol state machines, route attributes, birdc commands, stack traces, or BIRD 2 versus BIRD 3 source comparisons. Prefer a local checkout or exact upstream tag/commit and cite concrete files, functions, and revisions. Route config linting to bird-agent and active incidents to bird-troubleshooting.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Designs and reviews mobile architecture across Flutter, native Android, native iOS, React Native and Kotlin Multiplatform. Covers state ownership, data flow, modules, dependency boundaries, navigation, offline data and KMP shared source sets versus host apps. Use when structuring a feature, choosing state management, refactoring layers or deciding where a real class or screen belongs.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Overhaul one app's UI against an audit: UX audit and UI review of the running app, current versus proposed design tokens, a before/after visual plan, a startcycle build, point fixes, accessibility and browser tests, delivered as a PR after GO. Use for "redesign this app", "design overhaul", "audit and fix the UI", "give the app a new look".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Deploy a project to Vercel safely. Preview versus production, project linking, vercel CLI and Git-based flows, promote, rollback. Never deploys without the user's explicit approval. Use for any request to deploy, ship, publish, promote or roll back on Vercel.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Configure a Next.js app for Vercel. next.config versus vercel.json, proxy (renamed from middleware in Next.js 16), images, redirects and headers, function duration and region, caching and static export pitfalls, and local verification. Use when editing next.config, vercel.json or routing for a Next.js project on Vercel.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Create or improve codewhale skills. Use when the user wants a new skill, wants to update an existing skill, or needs guidance on when a skill should be a skill versus MCP, hooks, tools, or a plugin scaffold.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Evaluates and implements age estimation and verification technologies for online services. Covers facial age estimation, digital ID verification, self-declaration with risk assessment, AI-based age estimation, and the accuracy versus privacy tradeoff. Includes ICO guidance and euCONSENT framework. Keywords: age verification, age estimation, facial analysis, digital ID, children, online safety.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Read a Japanese job posting closely and report what it actually states: base pay separated from fixed overtime allowance, the range converted to an hourly basis, required versus preferred requirements, and the terms left ambiguous. Use when a user wants a 求人票 or job description examined before applying, wants two postings compared on the same basis, or wants questions prepared about pay and working conditions; do not use to judge whether the user will be hired, to infer duties the posting does not state, or to evaluate the employer's reputation.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Bead-based signal normalization and cross-batch harmonization for CyTOF and high-parameter cytometry - EQ four-element bead normalization of instrument sensitivity drift (CATALYST normCytof, premessa), and reference-anchor cross-batch normalization (CytoNorm, per-cluster quantile splines). Covers the distinction between within-run drift correction and between-batch correction, the mandatory anchor/reference sample, why normalization is per-cluster with many quantiles, and the over-correction risk. Use when correcting CyTOF signal drift, harmonizing multi-batch or multi-site studies, or deciding whether to normalize data versus model batch in the design.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Analyzes pooled CRISPR screens with MAGeCK (Li et al 2014), covering count generation (mageck count), the RRA two-condition workflow (mageck test using alpha-RRA over per-sgRNA negative-binomial p-values), the MLE multi-condition workflow (mageck mle with explicit design matrix and beta-score output), normalization choice (median vs total vs control-sgRNA vs spike-in), sgRNA efficiency injection, paired-sample testing, time-course design, drug-screen versus dropout-screen design matrices, MAGeCKFlute and MAGeCK-VISPR downstream visualization, and decision logic for when to use MAGeCK vs JACKS / BAGEL2 / drugZ / Chronos. Use when running a fresh CRISPR screen analysis, picking RRA vs MLE for the experimental design, choosing a normalization method from QC signatures, debugging MLE convergence failure or NaN beta scores, comparing MAGeCK output across tools, or building a batch-aware multi-cell-line / multi-condition MLE design matrix.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Resolve subclonal copy number, whole-genome doubling, and copy-number tumor evolution from bulk sequencing with Battenberg, TITAN, and MEDICC2. Covers clonal versus subclonal copy-number states, haplotype phasing for subclonal resolution, cancer cell fraction, whole-genome-doubling detection and timing relative to mutations, mirrored subclonal allelic imbalance, and copy-number phylogenies. Use when a tumor is heterogeneous and bulk data shows non-integer copy number, when calling subclonal CNAs, detecting or timing whole-genome doubling, reconstructing copy-number evolution, or deciding between Battenberg and TITAN.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Quantify homologous recombination deficiency (HRD) from tumor copy number using the three genomic-scar metrics — loss of heterozygosity (LOH), large-scale state transitions (LST), and telomeric allelic imbalance (TAI) — with scarHRD, and via the whole-genome HRDetect and CHORD models. Covers the genomic instability score, the PARP-inhibitor clinical context, whole-genome-doubling correction, and the scar-versus-state distinction. Use when computing an HRD score for PARP-inhibitor eligibility, deriving LOH/LST/TAI scars from allele-specific copy number, deciding between scar-based and mutational-signature HRD methods, or interpreting an HRD result in a BRCA-reverted or low-purity tumor.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Detect somatic and germline copy number variants from targeted, exome, and whole-genome sequencing with CNVkit, a read-depth caller that combines on-target and off-target (antitarget) coverage. Covers panel-of-normals construction, flat-reference tumor-only calling, hybrid/amplicon/WGS modes, CBS vs HMM segmentation selection, purity-aware integer calling, and reconciliation against GATK and allele-specific callers. Use when calling CNVs from hybrid-capture panels or exomes, deciding whether CNVkit (depth-only) is the right tool versus an allele-specific caller, building a panel of normals, diagnosing flat-reference false positives, or interpreting log2 ratios into copy-number states.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Bead-based signal normalization and cross-batch harmonization for CyTOF and high-parameter cytometry - EQ four-element bead normalization of instrument sensitivity drift (CATALYST normCytof, premessa), and reference-anchor cross-batch normalization (CytoNorm, per-cluster quantile splines). Covers the distinction between within-run drift correction and between-batch correction, the mandatory anchor/reference sample, why normalization is per-cluster with many quantiles, and the over-correction risk. Use when correcting CyTOF signal drift, harmonizing multi-batch or multi-site studies, or deciding whether to normalize data versus model batch in the design.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Resolve subclonal copy number, whole-genome doubling, and copy-number tumor evolution from bulk sequencing with Battenberg, TITAN, and MEDICC2. Covers clonal versus subclonal copy-number states, haplotype phasing for subclonal resolution, cancer cell fraction, whole-genome-doubling detection and timing relative to mutations, mirrored subclonal allelic imbalance, and copy-number phylogenies. Use when a tumor is heterogeneous and bulk data shows non-integer copy number, when calling subclonal CNAs, detecting or timing whole-genome doubling, reconstructing copy-number evolution, or deciding between Battenberg and TITAN.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Analyzes pooled CRISPR screens with MAGeCK (Li et al 2014), covering count generation (mageck count), the RRA two-condition workflow (mageck test using alpha-RRA over per-sgRNA negative-binomial p-values), the MLE multi-condition workflow (mageck mle with explicit design matrix and beta-score output), normalization choice (median vs total vs control-sgRNA vs spike-in), sgRNA efficiency injection, paired-sample testing, time-course design, drug-screen versus dropout-screen design matrices, MAGeCKFlute and MAGeCK-VISPR downstream visualization, and decision logic for when to use MAGeCK vs JACKS / BAGEL2 / drugZ / Chronos. Use when running a fresh CRISPR screen analysis, picking RRA vs MLE for the experimental design, choosing a normalization method from QC signatures, debugging MLE convergence failure or NaN beta scores, comparing MAGeCK output across tools, or building a batch-aware multi-cell-line / multi-condition MLE design matrix.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Quantify homologous recombination deficiency (HRD) from tumor copy number using the three genomic-scar metrics — loss of heterozygosity (LOH), large-scale state transitions (LST), and telomeric allelic imbalance (TAI) — with scarHRD, and via the whole-genome HRDetect and CHORD models. Covers the genomic instability score, the PARP-inhibitor clinical context, whole-genome-doubling correction, and the scar-versus-state distinction. Use when computing an HRD score for PARP-inhibitor eligibility, deriving LOH/LST/TAI scars from allele-specific copy number, deciding between scar-based and mutational-signature HRD methods, or interpreting an HRD result in a BRCA-reverted or low-purity tumor.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Detect somatic and germline copy number variants from targeted, exome, and whole-genome sequencing with CNVkit, a read-depth caller that combines on-target and off-target (antitarget) coverage. Covers panel-of-normals construction, flat-reference tumor-only calling, hybrid/amplicon/WGS modes, CBS vs HMM segmentation selection, purity-aware integer calling, and reconciliation against GATK and allele-specific callers. Use when calling CNVs from hybrid-capture panels or exomes, deciding whether CNVkit (depth-only) is the right tool versus an allele-specific caller, building a panel of normals, diagnosing flat-reference false positives, or interpreting log2 ratios into copy-number states.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Chooses and designs developer surfaces for an agentic product: SDK, CLI, MCP, GUI, API, webhooks, and pd tube-style listener/sender workflows. Use when deciding why a user belongs in an SDK versus CLI/MCP/GUI, planning Python SDK parity, or making agent invocation easy across languages. NOT for implementing the full SDK, generic API docs, or UI visual design.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Verify every visual-evidence artifact (screenshot/GIF/recording) attached to a PR carries a provenance manifest binding it to real daemon-backed truth — daemon port, run id, transcript head hash, agent node id, commit, and an honest real/fixture/mock source label — and that operator-control-panel PRs cover the required set of proof states. Use when gating a PR's proof artifacts before merge, auditing whether a "LIVE" visual claim is backed by a real daemon run versus a reused or fixture-backed mock, or defining the required state-coverage set for a control-panel change. NOT for the capture technique itself — headless Playwright, `screencapture -x -l`, non-interruptive capture (use port-daddy-agent-skill's visual-evidence doctrine), designing the receipt body schema an artifact attaches to (use agent-work-receipt-designer), or the broader dogfood/Potemkin product-quality bar (use multi-agent-authoring-product-bar).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.