'selection' 검색 결과

Expert database architect specializing in data layer design from scratch, technology selection, schema modeling, and scalable database architectures. Masters SQL/NoSQL/TimeSeries database selection, normalization strategies, migration planning, and performance-first design. Handles both greenfield architectures and re-architecture of existing systems. Use PROACTIVELY for database architecture, technology selection, or data modeling decisions.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Detect positive (diversifying / episodic / pervasive) selection using codon dN/dS frameworks. Implements PAML codeml site models (M0/M1a/M2a/M7/M8/M8a), branch models, branch-site model A (Zhang 2005), and HyPhy methods (BUSTED, BUSTED-S, BUSTED-MH, BUSTED-PH, MEME, FEL, FUBAR, aBSREL, SLAC, RELAX, GARD, FUBAR-MH). Includes McDonald-Kreitman framework (asymptotic alpha, impMKT, polyDFE, DFE-alpha, GRAPES) for within-species + divergence inference, RERconverge for trait-correlated rate shifts, CSUBST for convergent substitution, and PhyloAcc for accelerated noncoding evolution. Use when testing adaptive evolution at codons, branches, or full gene; running GARD recombination pre-screen; controlling alignment-error and gBGC false positives; reconciling PAML vs HyPhy results; or performing genome-scale selection scans.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

RFP creation, distribution, evaluation, and shortlisting for in-house legal teams selecting outside counsel. Draft a new legal services RFP from scratch, evaluate firm responses against weighted criteria, produce a shortlist recommendation with selection rationale for GC sign-off, or design the end-to-end RFP process from scratch. Trigger on: 'draft an RFP', 'run an RFP process', 'evaluate firm responses', 'score the RFP submissions', 'which firms should we shortlist', 'selection recommendation', 'how do we run a panel RFP', 'RFP for legal services', 'pitch process', 'firm selection process', 'compare the proposals', 'we're reviewing our panel', 'panel refresh RFP', 'we need to go to market', 'request for proposal', 'evaluate the pitches', 'which firm won the RFP', 'write a legal RFP', 'RFP evaluation criteria', 'design the RFP process', 'how do we weight the criteria'.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Binder design tool selection and workflow routing guidance. Use this skill when: (1) Deciding between BoltzGen, BindCraft, or RFdiffusion, (2) Planning a binder design campaign, (3) Understanding trade-offs between different approaches, (4) Selecting tools for specific target types. For specific tool parameters, use the individual tool skills (boltzgen, bindcraft, rfdiffusion, etc.).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Discover trending new products across 16 TikTok Shop regional markets via Nexscope new product ranking data. Trigger when users mention TikTok new product rankings, TikTok hot-selling products, TikTok Shop bestsellers, short-video e-commerce product selection, TikTok new product discovery, cross-border TikTok product selection, TikTok new product rankings, TikTok bestsellers, short-video product selection, TikTok viral products, new product ranking, TikTok product trends. Even if the user does not explicitly mention "Nexscope" or "new product ranking", trigger this skill whenever their need involves discovering hot-selling new products or emerging product trends on TikTok Shop.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Mercado Libre product selection data query and analysis via the Nexscope gateway, covering 24 tools for products, catalog, keywords, categories, trends, sellers, reviews, exchange rates, and plan usage across Mexico, Brazil, Argentina, Chile, and Colombia sites. Triggered by: Mercado Libre, MercadoLibre, product selection, product search, category trends, hot keywords, reverse traffic keywords, seller search, review search, exchange rate, plan usage, Lanjing, Mercado product research.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Search and analyze products on the Chinese 1688 wholesale platform (Alibaba domestic B2B market) for sourcing, supplier discovery, and product selection. Trigger when the user mentions 1688 product search, finding suppliers on 1688, wholesale product queries, factory sourcing, dropshipping supplier search, 1688 keyword product selection, wholesale price comparison, filtering by sales volume, any product research on the 1688 platform, 1688 search, 1688 product selection, find suppliers, factory lookup, wholesale pricing, supplier search, domestic sourcing, 1688 products. Even if the user does not explicitly say "1688", if their need involves searching for wholesale products, finding domestic suppliers, or sourcing from the domestic Chinese market, this skill should also be triggered.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Advanced Amazon product search and filtering powered by Nexscope data, supporting multi-dimensional criteria including category, price, monthly sales, keywords, BSR rank, review count, rating, package dimensions, weight, fulfillment type, and more. Trigger when users mention Nexscope product selection, Amazon product search, advanced product selection, BSR filtering, sales rank filtering, keyword-based product discovery, category search, competitor screening, niche product discovery, historical rank filtering, or similar terms. Even if the user does not explicitly mention "Nexscope," trigger this skill whenever the request involves multi-criteria Amazon product search, sales-metric-based filtering, or advanced product selection beyond simple keyword search.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use this skill when configuring or troubleshooting the Salesforce CPQ Guided Selling wizard: Quote Process records, ProcessInput questions that filter products by field value, mapping answers to Product2 classification fields, and search type choice (Standard, Enhanced, Custom). Trigger keywords: CPQ guided selling, quote process wizard, product selection wizard, SBQQ__QuoteProcess__c, SBQQ__ProcessInput__c, guided product selection, auto select product, product search plugin. NOT for bundles, product options, or product rules — use admin/cpq-product-catalog-setup. NOT for writing a custom ProductSearchPlugin in Apex — use apex/cpq-apex-plugins.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Detect positive (diversifying / episodic / pervasive) selection using codon dN/dS frameworks. Implements PAML codeml site models (M0/M1a/M2a/M7/M8/M8a), branch models, branch-site model A (Zhang 2005), and HyPhy methods (BUSTED, BUSTED-S, BUSTED-MH, BUSTED-PH, MEME, FEL, FUBAR, aBSREL, SLAC, RELAX, GARD, FUBAR-MH). Includes McDonald-Kreitman framework (asymptotic alpha, impMKT, polyDFE, DFE-alpha, GRAPES) for within-species + divergence inference, RERconverge for trait-correlated rate shifts, CSUBST for convergent substitution, and PhyloAcc for accelerated noncoding evolution. Use when testing adaptive evolution at codons, branches, or full gene; running GARD recombination pre-screen; controlling alignment-error and gBGC false positives; reconciling PAML vs HyPhy results; or performing genome-scale selection scans.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Detect positive (diversifying / episodic / pervasive) selection using codon dN/dS frameworks. Implements PAML codeml site models (M0/M1a/M2a/M7/M8/M8a), branch models, branch-site model A (Zhang 2005), and HyPhy methods (BUSTED, BUSTED-S, BUSTED-MH, BUSTED-PH, MEME, FEL, FUBAR, aBSREL, SLAC, RELAX, GARD, FUBAR-MH). Includes McDonald-Kreitman framework (asymptotic alpha, impMKT, polyDFE, DFE-alpha, GRAPES) for within-species + divergence inference, RERconverge for trait-correlated rate shifts, CSUBST for convergent substitution, and PhyloAcc for accelerated noncoding evolution. Use when testing adaptive evolution at codons, branches, or full gene; running GARD recombination pre-screen; controlling alignment-error and gBGC false positives; reconciling PAML vs HyPhy results; or performing genome-scale selection scans.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Detect positive (diversifying / episodic / pervasive) selection using codon dN/dS frameworks. Implements PAML codeml site models (M0/M1a/M2a/M7/M8/M8a), branch models, branch-site model A (Zhang 2005), and HyPhy methods (BUSTED, BUSTED-S, BUSTED-MH, BUSTED-PH, MEME, FEL, FUBAR, aBSREL, SLAC, RELAX, GARD, FUBAR-MH). Includes McDonald-Kreitman framework (asymptotic alpha, impMKT, polyDFE, DFE-alpha, GRAPES) for within-species + divergence inference, RERconverge for trait-correlated rate shifts, CSUBST for convergent substitution, and PhyloAcc for accelerated noncoding evolution. Use when testing adaptive evolution at codons, branches, or full gene; running GARD recombination pre-screen; controlling alignment-error and gBGC false positives; reconciling PAML vs HyPhy results; or performing genome-scale selection scans.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Principled action selection for autonomous agents using Expected Free Energy (EFE) under the Active Inference framework. Each candidate action is scored as G(n) = w_prag * (-E[p_n] * damping) + w_epist * (-Var[p_n] * novelty(n)), where agent beliefs are maintained as per-node Beta distributions and updated via Bayesian observation. Selection is a softmax over -gamma * G, making exploration an automatic consequence of uncertainty rather than a hardcoded epsilon. Precision weights w_prag and w_epist self-adapt via surprise history, shifting the agent between exploitation and exploration regimes without any manually tuned schedule.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Expert database architect specializing in data layer design from scratch, technology selection, schema modeling, and scalable database architectures. Masters SQL/NoSQL/TimeSeries database selection, normalization strategies, migration planning, and performance-first design. Handles both greenfield architectures and re-architecture of existing systems. Use PROACTIVELY for database architecture, technology selection, or data modeling decisions.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Slack 답변을 제목, 표, 버튼, 선택 메뉴로 정리해 결과 확인과 후속 작업을 돕습니다. Slack 앱용 Block Kit JSON 작성과 검증도 지원합니다.

• Slack 진행 보고를 정리합니다
• Slack 답변에 확인 버튼을 추가합니다
• 메뉴에서 다음 작업을 선택하도록 합니다

Paperclip Runner의 동작 평가에 판정 조건, 실행 대상, 보고서 검증 데이터를 추가하고 증거와 실행 이력을 남겨 검증합니다.

• Runner 동작 평가를 추가합니다
• 정상 및 잘못된 증거로 판정을 검증합니다
• 실행 대상 목록과 지속 평가를 갱신합니다

Paperclip의 Mine 받은 편지함에서 완료되거나 오래 변화가 없는 항목을 정리 후보로 제시합니다. 사용자가 체크박스로 확인한 항목만 보관 처리합니다.

• 완료된 업무를 받은 편지함에서 정리할 때
• 오래 변화가 없는 수신 항목 재검토
• 아직 합쳐지지 않은 커밋이 있는 작업 확인

Measure performance in the agentic-flow worker system, including trigger detection, agent selection, caching, and concurrent operations. Run a full suite or individual benchmarks to compare response times and resource usage against configured targets.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking overall worker system performance
• Measuring trigger detection and agent selection speed
• Examining cache behavior and memory usage

Select random winners from lists, CSV files, Excel, or Google Sheets. Choose multiple winners without duplicates, exclude previous winners, apply entry-based weighting, and record the selection method and timestamp for review.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Drawing winners for social media giveaways
• Selecting event raffle winners and alternates
• Randomly choosing survey or test participants

Design and test rules for activating skills through human requests, AI selection, applications, or other skills. Check eligibility before matching descriptions, then return a JSON decision showing the selected channel, score, and policy reason.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Separate explicit requests from automatic skill selection
• Test allowed callers and skill composition limits
• Check cases where no skill should activate

Prepare and publish releases of the Cline VS Code extension. Follow separate paths for stable, nightly, and emergency legacy releases, covering version selection, release notes, approval steps, packaged builds, and checks after publication.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing a stable extension release
• Creating a manual nightly build
• Publishing an emergency legacy hotfix

Build interactive terminal screens with OpenTUI using its core API, React, or Solid. Guides framework selection, components, layout, keyboard input, animations, and testing, with references for project setup and troubleshooting.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Starting an OpenTUI project with React or Solid
• Building terminal forms and selection menus
• Adapting layouts to terminal dimensions

Prepare voiceovers, background music, sound effects, transcripts, captions, and background cutouts for HyperFrames compositions. The skill guides audio provider selection, word timing, and caption styling, with local alternatives for several audio tasks.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating voiceovers for HyperFrames videos
• Preparing background music and sound effects
• Adding word-timed subtitles and karaoke captions