'ecc' 검색 결과

예약한 Itô GPU의 모델 서비스 지원 여부와 향후 확인 사항을 정리합니다. 현재는 서비스를 시작하지 않으며, 미지원 범위와 필요한 조건을 안내합니다.

• Itô GPU 예약 후 서비스 지원을 확인합니다
• OpenAI 호환 접속 주소의 제약을 확인합니다
• 향후 모델 서비스 설정을 검토합니다

Docker와 Docker Compose로 개발 환경을 구성하고 통신, 데이터 보존, 권한을 점검하며 원본 파일을 변경하지 않고 CLI 설치 절차를 검증합니다.

• 앱과 데이터베이스 개발 환경 구성
• 컨테이너 통신과 데이터 보존 확인
• Docker 보안 설정 검토

실제 메일함에서 수신 메일을 분류하고 대화 기록을 바탕으로 답장을 작성하며 발송 기록을 확인합니다. 발신 계정과 수신자를 정리하고 약속, 기한, 처리 상태를 보고합니다.

• 수신함 분류와 보관 후보 정리
• 이전 대화를 반영한 답장 초안 작성
• 메일 발송과 발송 기록 확인

기존 웹 페이지나 HTML을 점검하고 콘텐츠와 브랜드를 유지하면서 시각 요소, 문구, 움직임, 사용성 문제를 수정해 공유 전에 마무리합니다.

• 생성한 페이지를 공유 전에 다듬습니다
• 획일적인 디자인과 불일치를 검토합니다
• 모바일 표시와 사용성 문제를 수정합니다

웹사이트와 앱 화면을 설계하고 글자, 여백, 색상부터 조작 흐름까지 개선합니다. 사용성 검토, 화면 크기 대응, 표시 성능 확인도 다룹니다.

• 새 화면의 구성과 외관 설계
• 기존 화면의 글자·여백·색상 개선
• 접근성과 화면 크기 대응 점검

Examine container isolation in a CTF sandbox, a controlled security challenge environment. The skill checks kernel and runtime conditions, verifies whether an exploit crosses into host context, and preserves a minimal reproducible chain with evidence.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Review container isolation settings in a CTF environment
• Check kernel exploit prerequisites
• Verify whether a trigger crosses the container boundary

Organize multi-step agent tasks with persistent Markdown files for plans, findings, and progress. Records decisions, errors, and test results, then reloads the selected task's planning files to resume work after an interruption.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning research that involves many tool calls
• Resuming work after an interruption
• Recording errors and test results

Establish reusable design guidelines by reviewing project files and asking about unresolved preferences. The skill records the audience, brand personality, visual direction, and design principles in a project file for future sessions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Set design guidelines at the start of a project
• Record brand and visual preferences for future sessions
• Organize design context from existing code and assets

Design and refine web interfaces around their audience, tasks, and brand tone. Covers typography, color, layout, motion, accessibility, and performance, with guided techniques for reviewing existing screens and preparing them for release.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Establishing a design system
• Reviewing interface usability and accessibility
• Adapting layouts for mobile devices

Three modes for CS-conference papers (CVPR/ICCV/ECCV vision, ACL/EMNLP/NAACL NLP, ICLR/NeurIPS/ICML/AAAI ML). DIRECT-EDIT mode (common): the user describes a change in Chinese or English and the manuscript (LaTeX or Markdown) is edited directly through a CS-venue writing toolkit with author sign-off (use for 改这段 / 把中文想法写成 latex / polish / de-AI / translate / compress a passage). REVIEW mode (occasional, pre-submission): harden the paper through an adversarial courtroom review engine (N holistic domain reviewers / contestability routing / two-sided trial / three-way verdict / clerk-converged multi-round loop) with consensus-gated, author-signed revisions (use for review / critique / 审稿 / 评审 / mock-review). AUTO mode (unattended, opt-in via /goal): run the review-revise loop toward a verifiable goal, applying safe fixes under a drift-bounded policy and queueing risky ones. Resolves all inputs at runtime, no hardcoded paths. Not a from-scratch drafter (use ml-paper-writing) and not an official-venue rebuttal.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Analyzes single-cell pooled CRISPR screens (Perturb-seq, CROP-seq, Perturb-CITE-seq, ECCITE-seq, multiome) where each cell carries an sgRNA and a scRNA-seq / surface-protein / chromatin readout. Covers experimental design (direct-capture Perturb-seq Dixit 2016 vs CROP-seq 3'UTR-barcoded Datlinger 2017 vs ECCITE-seq vs Multiome), MOI for sgRNA assignment, escaper-cell filtering (Mixscape, Papalexi 2021), SCEPTRE NB GLM + permutation for low-MOI (Barry 2024 Genome Biol 25:124), the Pertpy framework, factor decomposition, genome-scale Perturb-seq (Replogle 2022 Cell, 2.5M cells), and per-perturbation single-cell DE. Use when running a single-cell CRISPR screen, choosing direct-capture vs CROP-seq architecture, filtering escaper cells, performing single-cell DE, integrating Perturb-seq with pathway analysis, scaling to GW CRISPRi via Replogle protocol, or analyzing multi-omics screens.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when designing, reviewing, refining, fixing, or codifying frontend UI with Maestro's self-contained Impeccable core

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Operate ECCV2022-RIFE video frame interpolation, benchmark evaluation, and training workflows from a source checkout.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when the user wants to design, redesign, shape, critique, audit, polish, clarify, distill, harden, optimize, adapt, animate, colorize, extract, or otherwise improve a frontend interface. Covers design systems, anti-pattern detection, brand vs product registers, typography, color (OKLCH), spacing, motion, copy, and accessibility. By Paul Bakaus (ex-Google, ex-Disney, ex-Unity). Full version includes 23 sub-commands, CLI detection, Chrome extension, and E2E test suite: npx skills add pbakaus/impeccable

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Guide users through ECC's current agents, skills, commands, hooks, rules, install profiles, and project onboarding by reading the live repository surface before answering.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Build an evidence-backed ECC install plan for a specific repo by sorting skills, commands, rules, hooks, and extras into DAILY vs LIBRARY buckets using parallel repo-aware review passes. Use when ECC should be trimmed to what a project actually needs instead of loading the full bundle.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Analyzes single-cell pooled CRISPR screens (Perturb-seq, CROP-seq, Perturb-CITE-seq, ECCITE-seq, multiome) where each cell carries an sgRNA and a scRNA-seq / surface-protein / chromatin readout. Covers experimental design (direct-capture Perturb-seq Dixit 2016 vs CROP-seq 3'UTR-barcoded Datlinger 2017 vs ECCITE-seq vs Multiome), MOI for sgRNA assignment, escaper-cell filtering (Mixscape, Papalexi 2021), SCEPTRE NB GLM + permutation for low-MOI (Barry 2024 Genome Biol 25:124), the Pertpy framework, factor decomposition, genome-scale Perturb-seq (Replogle 2022 Cell, 2.5M cells), and per-perturbation single-cell DE. Use when running a single-cell CRISPR screen, choosing direct-capture vs CROP-seq architecture, filtering escaper cells, performing single-cell DE, integrating Perturb-seq with pathway analysis, scaling to GW CRISPRi via Replogle protocol, or analyzing multi-omics screens.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Configura el repositorio de GitHub de punta a punta con gh — descripción, topics, labels, rama develop, protección de ramas, merge settings y metadatos — a partir de la definición del producto. Úsalo al crear el repo de un proyecto nuevo o cuando haya que poner en orden uno existente (p. ej. "configura el repo", "prepara el repositorio en GitHub", "pon los labels y la protección de ramas").

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Frontend UI design, critique, audit, polish. Use when the user wants to design, redesign, shape, critique, audit, polish, clarify, distill, harden, optimize, adapt, animate, colorize, extract, or otherwise improve a frontend interface. Covers websites, landing pages, dashboards, product UI, app shells, components, forms, settings, onboarding, and empty states. Handles UX review, visual hierarchy, information architecture, cognitive load, accessibility, performance, responsive behavior, theming, anti-patterns, typography, fonts, spacing, layout, alignment, color, motion, micro-interactions, UX copy, error states, edge cases, i18n, and reusable design systems or tokens. Also use for bland designs that need to become bolder or more delightful, loud designs that should become quieter, live browser iteration on UI elements, or ambitious visual effects that should feel technically extraordinary. Not for backend-only or non-UI tasks.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Build an evidence-backed ECC install plan for a specific repo by sorting skills, commands, rules, hooks, and extras into DAILY vs LIBRARY buckets using parallel repo-aware review passes. Use when ECC should be trimmed to what a project actually needs instead of loading the full bundle.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Apply the confirmed design direction to all visual surfaces. This skill now bridges the pipeline's placeholder system with the `impeccable` skill's PRODUCT.md and DESIGN.md files. If `{{DESIGN_DIRECTION}}` still appears as a literal placeholder, run `/impeccable teach` to establish design context.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Analyzes single-cell pooled CRISPR screens (Perturb-seq, CROP-seq, Perturb-CITE-seq, ECCITE-seq, multiome) where each cell carries an sgRNA and a scRNA-seq / surface-protein / chromatin readout. Covers experimental design (direct-capture Perturb-seq Dixit 2016 vs CROP-seq 3'UTR-barcoded Datlinger 2017 vs ECCITE-seq vs Multiome), MOI for sgRNA assignment, escaper-cell filtering (Mixscape, Papalexi 2021), SCEPTRE NB GLM + permutation for low-MOI (Barry 2024 Genome Biol 25:124), the Pertpy framework, factor decomposition, genome-scale Perturb-seq (Replogle 2022 Cell, 2.5M cells), and per-perturbation single-cell DE. Use when running a single-cell CRISPR screen, choosing direct-capture vs CROP-seq architecture, filtering escaper cells, performing single-cell DE, integrating Perturb-seq with pathway analysis, scaling to GW CRISPRi via Replogle protocol, or analyzing multi-omics screens.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Analyzes single-cell pooled CRISPR screens (Perturb-seq, CROP-seq, Perturb-CITE-seq, ECCITE-seq, multiome) where each cell carries an sgRNA and a scRNA-seq / surface-protein / chromatin readout. Covers experimental design (direct-capture Perturb-seq Dixit 2016 vs CROP-seq 3'UTR-barcoded Datlinger 2017 vs ECCITE-seq vs Multiome), MOI for sgRNA assignment, escaper-cell filtering (Mixscape, Papalexi 2021), SCEPTRE NB GLM + permutation for low-MOI (Barry 2024 Genome Biol 25:124), the Pertpy framework, factor decomposition, genome-scale Perturb-seq (Replogle 2022 Cell, 2.5M cells), and per-perturbation single-cell DE. Use when running a single-cell CRISPR screen, choosing direct-capture vs CROP-seq architecture, filtering escaper cells, performing single-cell DE, integrating Perturb-seq with pathway analysis, scaling to GW CRISPRi via Replogle protocol, or analyzing multi-omics screens.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

NanoClaw v2の操作と拡張 — ECCのゼロ依存関係セッション対応REPLで、claude -p上に構築。

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.