'jar' 검색 결과

Crie conteúdo de marketing otimizado para SEO com voz de marca consistente. Inclui analisador de voz de marca, otimizador de SEO, frameworks de conteúdo e templates de redes sociais. Use ao escrever posts em blog, criar conteúdo de redes sociais, analisar voz de marca, otimizar SEO, planejar calendários de conteúdo, ou quando o usuário menciona criação de conteúdo, voz de marca, otimização de SEO, marketing em redes sociais ou estratégia de conteúdo.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Quando o usuário quer planejar, projetar ou implementar um teste A/B ou experimento. Também use quando o usuário menciona "teste A/B", "split test", "experimento", "testar essa mudança", "copy variante", "teste multivariado" ou "hipótese". Para implementação de rastreamento, veja analytics-tracking.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Orientação de liderança executiva para tomada de decisões estratégicas, desenvolvimento organizacional e gerenciamento de stakeholders. Inclui analisador de estratégia, modelagem de cenários financeiros, frameworks de governança corporativa e playbooks de relações com investidores. Use ao planejar estratégia, preparar apresentações para conselho, gerenciar investidores, desenvolver cultura organizacional, tomar decisões executivas, ou quando o usuário menciona CEO, planejamento estratégico, reuniões do conselho, atualizações de investidores, liderança organizacional ou estratégia executiva.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Cria planos de implementação detalhados e segmentados através de pesquisa, entrevistas com stakeholders e revisão multi-LLM. Use quando planejar features que precisam de análise pré-implementação aprofundada.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Agentes autônomos são sistemas de IA que conseguem decompor objetivos, planejar ações, executar ferramentas e se auto-corrigir sem orientação humana constante. O desafio não é torná-los capazes - é torná-los confiáveis. Cada decisão extra multiplica a probabilidade de falha. Esta habilidade cobre loops de agentes (ReAct, Plan-Execute), decomposição de objetivos, padrões de reflexão e confiabilidade em produção. Insight-chave: taxas de erro compostas matam agentes autônomos. Uma taxa de sucesso de 95% por passo cai para 60% b

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Load this skill whenever the project involves writing, editing, or reviewing content for a general audience, including UI copy, help text, error messages, documentation, and instructions. Under no circumstances use jargon, complex sentence structures, or unexplained acronyms without providing plain alternatives. Absolutely always target a reading level accessible to the broadest possible audience. Load alongside content-design/SKILL.md for all content work.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Exasol BucketFS file system management via the `exapump bucketfs` CLI. Covers listing, uploading, downloading, and deleting files and directories in BucketFS, the `bfsdefault` service and other bucket services, bucket structure, `bfs_*` profile settings, the `/buckets/<service>/<bucket>/<path>` UDF path, and staging JARs, models, and Script Language Containers for UDFs.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Build, install, validate, and debug custom Exasol virtual schema adapters. Covers source-specific JDBC dialect implementation with `virtual-schema-common-jdbc` including `SqlDialect`, metadata reader behavior, type mapping, and pushdown capabilities, custom document-file adapter code boundaries, custom adapter properties, JAR packaging, BucketFS deployment, adapter script setup, `EXPLAIN VIRTUAL` validation, and adapter-side remote debugging workflows.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Restate the last message in blunt, no-nonsense plain language, stripped of all jargon and hedging.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Restate the last message in plain human language, with no jargon.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Plain language directive. Cuts verbosity, corporate jargon, and overused AI vocabulary. Use when you want clear, direct, short writing. No fluff, no hedging, no unnecessary complexity.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Detects m6A modifications from Oxford Nanopore direct-RNA-sequencing (ONT DRS) signal data using m6Anet (Hendra 2022 *Nat Methods* 19:1590; multiple-instance-learning neural network over DRACH 5-mer signal). Covers the required upstream pipeline (Dorado / Guppy basecalling -> minimap2 transcriptome alignment with `-ax map-ont -uf -k14 --secondary=no` -> nanopolish eventalign with `--scale-events --signal-index` (m6Anet-required) plus `--summary` / `--threads` housekeeping -> m6anet dataprep -> m6anet inference), per-site vs per-read probability interpretation including the `mod_ratio` per-site stoichiometry column, the DRACH-only modeling constraint, minimum-coverage thresholds (20-50 reads per site for stable probability estimates), multi-condition comparison via xPore (Pratanwanich 2021 *Nat Biotechnol* 39:1394), Nanocompore (Leger 2021 *Nat Commun* 12:7198), ELIGOS (Jenjaroenpun 2021 *NAR* 49:e7), and Dorado native modification calling (RNA004 chemistry, 2024+), reference-transcriptome version pinning, the cDNA-vs-DRS chemistry distinction (cDNA-Nanopore CANNOT be used for modification detection), and orthogonal validation against MeRIP / GLORI. Use when calling m6A from ONT DRS without immunoprecipitation, choosing m6Anet vs xPore vs Nanocompore vs ELIGOS vs Dorado native, interpreting per-site `probability_modified` vs `mod_ratio` vs per-read modification probabilities, comparing methylation between conditions from ONT data, deciding between m6Anet for known DRACH sites and Dorado/Remora for genome-wide screening, pinning RNA002 vs RNA004 chemistry and basecaller model versions, or troubleshooting eventalign / dataprep failures.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Raw, anti-design aesthetic inspired by concrete architecture with unadorned elements, jarring layouts, and functional minimalism.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when the user wants a full feature action plan (backend + frontend + tests) with the PWDEVIA 7-question method before any code is written — 'planejar a feature X', 'criar plano de feature', 'plan the user CRUD'. Interviews the human (max 2 rounds) and writes .planning/feat/features/{slug}/plan.md. Do NOT use to write code (feat-exec), for small 1–3 file changes (feat-quick), backend-only or UI-only scope (feat-backend / feat-frontend), tests for existing code (feat-test) or code review (feat-review).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Detects m6A modifications from Oxford Nanopore direct-RNA-sequencing (ONT DRS) signal data using m6Anet (Hendra 2022 *Nat Methods* 19:1590; multiple-instance-learning neural network over DRACH 5-mer signal). Covers the required upstream pipeline (Dorado / Guppy basecalling -> minimap2 transcriptome alignment with `-ax map-ont -uf -k14 --secondary=no` -> nanopolish eventalign with `--scale-events --signal-index` (m6Anet-required) plus `--summary` / `--threads` housekeeping -> m6anet dataprep -> m6anet inference), per-site vs per-read probability interpretation including the `mod_ratio` per-site stoichiometry column, the DRACH-only modeling constraint, minimum-coverage thresholds (20-50 reads per site for stable probability estimates), multi-condition comparison via xPore (Pratanwanich 2021 *Nat Biotechnol* 39:1394), Nanocompore (Leger 2021 *Nat Commun* 12:7198), ELIGOS (Jenjaroenpun 2021 *NAR* 49:e7), and Dorado native modification calling (RNA004 chemistry, 2024+), reference-transcriptome version pinning, the cDNA-vs-DRS chemistry distinction (cDNA-Nanopore CANNOT be used for modification detection), and orthogonal validation against MeRIP / GLORI. Use when calling m6A from ONT DRS without immunoprecipitation, choosing m6Anet vs xPore vs Nanocompore vs ELIGOS vs Dorado native, interpreting per-site `probability_modified` vs `mod_ratio` vs per-read modification probabilities, comparing methylation between conditions from ONT data, deciding between m6Anet for known DRACH sites and Dorado/Remora for genome-wide screening, pinning RNA002 vs RNA004 chemistry and basecaller model versions, or troubleshooting eventalign / dataprep failures.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Plain-English translation layer for non-technical Copilot CLI users. Translates every approval prompt, error message, and technical output into clear, jargon-free English with color-coded risk indicators.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.