'app filtering' 검색 결과

Generates optimized read queries using Dapper. Includes multi-mapping for joins, pagination, dynamic filtering, CTEs, and best practices for high-performance data access.

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Configure related lists on Salesforce records — column choice on Page Layouts (max 10 fields per related list), sort field selection, the Spring '24 Enhanced Related Lists component (which adds filtering, mass actions, and 10-row inline display), the choice between Related Lists / Related Lists - Single / Related List - Quick Links in Lightning App Builder, per-record-type related-list assignments, and the View All / pagination UX. NOT for FSL WorkOrderMilestone-on-Work-Order layout (use admin/fsl-sla-configuration-requirements), NOT for the Lightning page performance Related List - Single vs Full tradeoff in isolation (use admin/lightning-page-performance-tuning), NOT for Industries Timeline-as-related-list (use admin/health-cloud-timeline), NOT for offline-priming related-list caps (use admin/fsl-mobile-app-setup).

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Controls error rates across thousands of simultaneous tests in genomics discovery using false-discovery-rate methods (Benjamini-Hochberg 1995; Benjamini-Yekutieli 2001 for arbitrary dependence; Storey q-value with pi0 estimation; local FDR; independent filtering Bourgon 2010; covariate-weighted FDR via IHW Ignatiadis 2016), plus family-wise error control (Bonferroni, Holm) and the GWAS genome-wide threshold. Covers the FDR-versus-FWER choice as the discovery-versus-confirmatory distinction, the dependence assumptions behind BH (PRDS) versus BY, pi0 estimation, the independent-filtering and false-coverage-rate traps, and reproducibility ranking via IDR (Li 2011). Use when correcting p-values from genome-wide tests, choosing between BH/BY/q-value/Bonferroni, setting an FDR threshold, applying IHW or independent filtering, or interpreting q-values. For confirmatory trials with few pre-specified endpoints (closed testing, graphical/gatekeeping), see clinical-biostatistics/multiplicity-graphical.

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Guides MongoDB users through implementing and optimizing Atlas Search (full-text), Vector Search (semantic), and Hybrid Search solutions. Use this skill when users need to build search functionality for text-based queries (autocomplete, fuzzy matching, faceted search), semantic similarity (embeddings, RAG applications), or combined approaches. Also use when users need text containment, substring matching ('contains', 'includes', 'appears in'), case-insensitive or multi-field text search, or filtering across many fields with variable combinations. Provides workflows for selecting the right search type, creating indexes, constructing queries, and optimizing performance using the MongoDB MCP server.

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Implement REST APIs in ABP Framework with AppServices, DTOs, pagination, filtering, and authorization. Use when building API endpoints for ABP applications.

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Controls error rates across thousands of simultaneous tests in genomics discovery using false-discovery-rate methods (Benjamini-Hochberg 1995; Benjamini-Yekutieli 2001 for arbitrary dependence; Storey q-value with pi0 estimation; local FDR; independent filtering Bourgon 2010; covariate-weighted FDR via IHW Ignatiadis 2016), plus family-wise error control (Bonferroni, Holm) and the GWAS genome-wide threshold. Covers the FDR-versus-FWER choice as the discovery-versus-confirmatory distinction, the dependence assumptions behind BH (PRDS) versus BY, pi0 estimation, the independent-filtering and false-coverage-rate traps, and reproducibility ranking via IDR (Li 2011). Use when correcting p-values from genome-wide tests, choosing between BH/BY/q-value/Bonferroni, setting an FDR threshold, applying IHW or independent filtering, or interpreting q-values. For confirmatory trials with few pre-specified endpoints (closed testing, graphical/gatekeeping), see clinical-biostatistics/multiplicity-graphical.

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Controls error rates across thousands of simultaneous tests in genomics discovery using false-discovery-rate methods (Benjamini-Hochberg 1995; Benjamini-Yekutieli 2001 for arbitrary dependence; Storey q-value with pi0 estimation; local FDR; independent filtering Bourgon 2010; covariate-weighted FDR via IHW Ignatiadis 2016), plus family-wise error control (Bonferroni, Holm) and the GWAS genome-wide threshold. Covers the FDR-versus-FWER choice as the discovery-versus-confirmatory distinction, the dependence assumptions behind BH (PRDS) versus BY, pi0 estimation, the independent-filtering and false-coverage-rate traps, and reproducibility ranking via IDR (Li 2011). Use when correcting p-values from genome-wide tests, choosing between BH/BY/q-value/Bonferroni, setting an FDR threshold, applying IHW or independent filtering, or interpreting q-values. For confirmatory trials with few pre-specified endpoints (closed testing, graphical/gatekeeping), see clinical-biostatistics/multiplicity-graphical.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Controls error rates across thousands of simultaneous tests in genomics discovery using false-discovery-rate methods (Benjamini-Hochberg 1995; Benjamini-Yekutieli 2001 for arbitrary dependence; Storey q-value with pi0 estimation; local FDR; independent filtering Bourgon 2010; covariate-weighted FDR via IHW Ignatiadis 2016), plus family-wise error control (Bonferroni, Holm) and the GWAS genome-wide threshold. Covers the FDR-versus-FWER choice as the discovery-versus-confirmatory distinction, the dependence assumptions behind BH (PRDS) versus BY, pi0 estimation, the independent-filtering and false-coverage-rate traps, and reproducibility ranking via IDR (Li 2011). Use when correcting p-values from genome-wide tests, choosing between BH/BY/q-value/Bonferroni, setting an FDR threshold, applying IHW or independent filtering, or interpreting q-values. For confirmatory trials with few pre-specified endpoints (closed testing, graphical/gatekeeping), see clinical-biostatistics/multiplicity-graphical.

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Implement REST APIs in ABP Framework with AppServices, DTOs, pagination, filtering, and authorization. Use when building API endpoints for ABP applications.

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Build advanced AgentDB workflows with database synchronization, multiple databases, and search that combines meaning with attribute filters. Covers similarity measures, diverse search results, and operational patterns for distributed AI applications.

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• Sharing stored patterns across multiple AgentDB instances
• Filtering semantic search by date or category
• Separating knowledge and conversation databases

Build advanced AgentDB workflows with database synchronization, multiple databases, and search that combines meaning with attribute filters. Covers similarity measures, diverse search results, and operational patterns for distributed AI applications.

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• Sharing stored patterns across multiple AgentDB instances
• Filtering semantic search by date or category
• Separating knowledge and conversation databases

Configure images and videos as surface textures in Three.js, and use environment images for backgrounds and reflections. Adjust placement, color settings, repetition, and filtering, then manage texture memory through compression, reuse, and disposal.

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• Adjust image placement and tiling on 3D surfaces
• Use HDR images for backgrounds and reflections
• Apply video or Canvas content to a 3D surface

Manage AdGuard Home network filtering from the command line. Update blocklists, register devices, configure DNS rules that map names to addresses, and inspect query logs and statistics through the AdGuard Home REST API.

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• Adding and refreshing ad-blocking filter lists
• Mapping local domain names to IP addresses
• Registering devices and managing fixed address assignments

Read, analyze, modify, and visualize molecular structures with RDKit in Python. Calculate molecular properties, search for similar structures or specific structural patterns, apply reaction rules, and generate 2D or 3D coordinates with detailed control over processing.

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• Calculating properties for a collection of molecules
• Searching for molecules with similar structures
• Filtering molecules by specific structural patterns

Screen compound libraries for drug discovery using drug-likeness guidelines, structural alerts that flag patterns for review, and molecular complexity measures. Combine criteria to shortlist candidates and retain filtering results with reasons for further assessment.

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• Screen compound libraries with drug-likeness rules
• Flag PAINS and other structures for review
• Combine property, structure, and complexity filters

Build Java applications that store and manage records in Azure Table Storage or Cosmos DB Table API. It guides creating tables, adding and updating records, filtering queries, and grouping changes into transactions that succeed or fail together.

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• Implement product record creation, updates, and deletion
• Filter records and retrieve only the required fields
• Update records together within the same partition

Plan security controls for LLM applications and AI agents, covering malicious instructions, data leakage, model tampering, and API access. Includes guidance and code examples for filtering outputs, enforcing permissions, isolating networks, and recording audit events.

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• Reviewing defenses against prompt injection in AI agents
• Filtering personal information from LLM responses
• Designing authentication and rate limits for LLM APIs

Prepare raw credit data for pre-loan modeling by checking data quality and screening variables. Review missing values, predictive usefulness, stability, noise, and correlation, then export an Excel report with organization-level statistics and cleaning results.

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• Prepare credit data before building a model
• Compare missing values and bad-sample rates across organizations
• Screen variables for usefulness, stability, and redundancy

Turn a screen recording of repetitive work into automation scripts. The skill reconstructs the workflow, proposes options suited to installed tools, and helps build and test the chosen approach while preserving human review steps.

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• Automating repeated report downloads and spreadsheet filtering
• Turning file sorting and renaming into scripts
• Reducing repetitive transfers between applications

Build search and recommendation features with Pinecone, a managed database for numerical representations of content. Covers data loading, combined meaning and keyword search, filtering by attributes, and separating data by user.

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• Build document retrieval for AI answers
• Combine meaning and keyword search
• Separate search data by user

Investigate OpenSEO production errors in Cloudflare Workers Observability. Count worker invocations accurately, compare periods and deployment versions, and filter previously investigated noise to focus analysis on errors that need attention.

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• Investigating a spike in production errors
• Counting out-of-memory events and worker failures
• Comparing error rates across periods or deployments

Build search and recommendation features with Pinecone, a managed database for numerical representations of content. Covers data loading, combined meaning and keyword search, filtering by attributes, and separating data by user.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build document retrieval for AI answers
• Combine meaning and keyword search
• Separate search data by user

Choose, create, and review software diagrams that explain structure, interactions, workflows, states, or data relationships. Select suitable notation and rendering tools, then plan static exports or interactive views with readable layouts, accessible controls, and validation.

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• Explain login interactions with a sequence diagram
• Turn a database schema into an entity relationship diagram
• Document application architecture with C4

Query GitHub pull requests by author, status, or review needs, and extract selected fields as JSON. Inspect the data structure and size first, then use jq, a JSON filtering tool, to refine results.

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• Find pull requests awaiting review
• List pull request numbers and titles by author
• Identify pull requests with many changed files