706건 · 관련도순
Estratégia de quality gates determinísticos e adoção incremental para CI/CD. Use quando o usuário pedir "quality gate", "gate de qualidade", "política de bloqueio", "baseline", "ratchet", "qualidade no CI" ou "SonarQube".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Quality gates determinísticos para PHP e Laravel. Use quando o usuário pedir "quality gate PHP", "quality gate Laravel", "PHPStan", "Larastan", "Pest", "PHPUnit", "PHPMD", "Composer Audit", cobertura PHP ou baseline PHP.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Quality gates determinísticos para aplicações e serviços Node.js, JavaScript e TypeScript. Use quando o usuário pedir "quality gate Node.js", ESLint ou tsc em Node.js, testes, cobertura, complexidade, SAST, SCA, build ou baseline para Node.js.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Quality gates determinísticos para Vue.js, JavaScript e TypeScript. Use quando o usuário pedir "quality gate Vue.js", ESLint, vue-tsc ou Vitest em projeto Vue.js, cobertura, complexidade, bundle, acessibilidade ou baseline para Vue.js.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Quality gates determinísticos para PostgreSQL. Use quando o usuário pedir "quality gate PostgreSQL", "quality gate Postgres", migrations ou schema drift PostgreSQL, constraints ou índices PostgreSQL, EXPLAIN, plano de consulta ou baseline para PostgreSQL.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Post-deploy canary monitoring. Watches the live app for console errors, performance regressions, and page failures using the browse daemon. Takes periodic screenshots, compares against pre-deploy baselines, and alerts on anomalies. Use when: "monitor deploy", "canary", "post-deploy check", "watch production", "verify deploy". (gstack)
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Performance regression detection using the browse daemon. Establishes baselines for page load times, Core Web Vitals, and resource sizes. Compares before/after on every PR. Tracks performance trends over time. Use when: "performance", "benchmark", "page speed", "lighthouse", "web vitals", "bundle size", "load time". (gstack) Voice triggers (speech-to-text aliases): "speed test", "check performance".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Optimize performance of the Adobe Dispatcher Apache HTTP Server module and related HTTPD configuration for AEMaaCS cloud workflows only, with cloud-specific baseline and runtime verification.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Validates animation durations, enforces typography scale, checks component accessibility, and prevents layout anti-patterns in Tailwind CSS projects. Use when building UI components, reviewing CSS utilities, styling React views, or enforcing design consistency.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Build publication-quality figures in R with ggplot2 using the grammar of graphics (data + aesthetics + geometries + scales + facets + themes) with CVD-safe palettes, cairo_pdf TrueType embedding, programmatic aes via tidy evaluation, and the theme_classic publication baseline. Use when producing static figures in R for papers, presentations, or reports.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Visualize copy number profiles, segments, allele-specific tracks, and cohort patterns from CNVkit, GATK, ASCAT, FACETS, Sequenza, and other callers. Covers genome-wide and per-chromosome log2 scatter plots, B-allele-frequency/minor-allele-fraction tracks, ideograms, cohort heatmaps, circos views, and caller-native plots. Use when creating publication CNV figures, choosing which plot answers a given question, diagnosing a wrong diploid baseline visually, displaying loss of heterozygosity, or deciding what depth-only plots cannot reveal.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Infer integer allele-specific copy number, tumor purity, and ploidy from tumor sequencing by jointly modeling read depth (logR) and B-allele frequency (BAF) with ASCAT, Sequenza, FACETS, PURPLE, and PureCN (tumor-only). Covers the purity-ploidy identifiability problem, the diploid-baseline (dipLogR) anchor, major/minor copy number, loss of heterozygosity, sunrise/contour fit diagnostics, and reconciliation of conflicting fits. Use when tumor analysis needs absolute copy number rather than relative log2, when estimating purity and ploidy, calling LOH or copy-neutral LOH, resolving whole-genome doubling, running tumor-only allele-specific calling, or choosing among ASCAT, Sequenza, FACETS, and PureCN.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Reads, validates, and prepares CDISC SDTM and ADaM clinical trial data for analysis. Covers SDTM domain joins (DM, AE, EX, VS, LB, DS), ADaM architecture (ADSL, BDS, OCCDS, ADTTE) with traceability, treatment-emergent AE conventions, baseline derivation, SUPPQUAL/NSV handling, Define-XML 2.1, and Pinnacle 21 / CORE validation. Use when working with clinical trial datasets in CDISC SDTM/ADaM format, preparing analysis-ready data, or validating for regulatory submission.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Builds QSAR / QSPR models using chemprop D-MPNN, MolFormer, Uni-Mol, ChemBERTa, random forest baselines, and Gaussian processes with explicit handling of OECD 5 principles, applicability domain (kNN, leverage, conformal prediction, Mahalanobis), scaffold-balanced splits, ensemble uncertainty, calibration (Platt, isotonic), feature importance (SHAP, atomic attribution), and prospective validation. Use when building target-specific predictive models from in-house bioassay data, ADMET endpoints, or selectivity profiles.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Estimate SNP heritability and partition it across functional annotations, cell types, and loci from GWAS summary statistics or individual-level genotypes. Implements LDSC, stratified LDSC with the baseline-LD model, Finucane 2018 cell-type prioritization, LDAK SumHer, HDL, HESS local heritability, BOLT-REML, GCTA-GREML, graphREML, and Popcorn cross-population genetic correlation. Use when computing total h2_SNP from summary stats, partitioning heritability across functional categories, prioritizing trait-relevant tissues or cell types from ENCODE/Roadmap chromatin marks, reconciling LDSC vs LDAK enrichment estimates, computing local heritability with HESS, estimating genetic correlation between traits, or producing publication-grade enrichment with calibrated sensitivity to model assumptions.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
The watch-fix-rerun method for making an existing platform agent or automated process efficient and correct: baseline its ledger, run one unit yourself, fix the class behind every wasted call, rerun, record. Use when asked to improve, optimize, watch, or 'make efficient' an agent, a sandbox session, a sync job, or any recurring automated process.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Validates animation durations, enforces typography scale, checks component accessibility, and prevents layout anti-patterns in Tailwind CSS projects. Use when building UI components, reviewing CSS utilities, styling React views, or enforcing design consistency.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Запускает lightweight baseline verification для docs, OpenSpec, skills и live context этого репозитория.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Systematic performance profiling and optimization methodology. Covers CPU profiling, memory profiling, heap analysis, goroutine/thread analysis, FlameGraph generation, latency profiling, database query profiling, network I/O profiling, and continuous performance regression detection. USE WHEN: investigating performance issues, optimizing slow code, reducing memory usage, diagnosing GC/CPU bottlenecks, finding N+1 queries, analyzing pprof/flamegraph output, or establishing a performance baseline. Triggers on "profiling", "performance analysis", "slow code", "flame graph", "pprof", "heap dump", "memory leak", "CPU spike", "bottleneck".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Runs BenchmarkDotNet microbenchmarks. Setup, memory diagnosers, baselines, result analysis.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Gates CI on perf regressions. Automated threshold alerts, baseline tracking, trend reports.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Defines baseline C# conventions loaded first -- naming, file layout, style rules.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Detect anomalies in DNP3 (Distributed Network Protocol 3) communications used in SCADA systems by monitoring for unauthorized control commands, firmware update attempts, protocol violations, and deviations from baseline traffic patterns using deep packet inspection and machine learning approaches.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Implement User and Entity Behavior Analytics using Elasticsearch/OpenSearch to build behavioral baselines, calculate anomaly scores, perform peer group analysis, and detect insider threat indicators such as data exfiltration, privilege abuse, and unauthorized access patterns.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.