'filtering' 검색 결과

Build intermediary servers for authorized security exercises, separating public traffic from the command server used by the test team. It covers simple forwarding, request filtering with nginx or Apache, TLS certificates, and validation against the exercise’s traffic profile.

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• Separating public infrastructure from an exercise command server
• Filtering exercise traffic against a defined profile
• Adding TLS certificates to a redirector

Multi-dimensional Amazon product search and filtering based on Nexscope data, covering 14 marketplaces, with support for historical monthly snapshot lookback. Trigger when the user mentions Nexscope product search, Amazon product filtering, competitor research, category analysis, brand bestsellers, seller analysis, seasonal products, historical snapshot review, product search, monthly sales/revenue, ABA keyword product discovery, price range filtering, new product discovery, multi-condition combined filtering, product search, competitor research, category analysis, brand bestsellers, seller analysis, seasonal products, historical snapshot. Even if the user does not explicitly mention "Nexscope", if their need involves Amazon product search, filtering, comparison, or product exploration by category/brand/seller dimensions, this skill should also be triggered.

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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.

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Build safety checks for LLM prompts and responses using Meta's LlamaGuard moderation model. Classify potentially harmful content, block flagged messages, serve checks through an API, and integrate them with NeMo Guardrails.

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• Checking prompts before sending them to an LLM
• Filtering responses before displaying them
• Building a moderation API

Check single-cell RNA-seq data quality, filter low-quality cells using median absolute deviation, a measure of spread, and compare plots before and after filtering. Outputs include filtered data and the original dataset with quality annotations.

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• Assess single-cell RNA-seq data quality
• Filter low-quality cells and rarely detected genes
• Compare quality plots before and after filtering

Protein design quality control, filtering thresholds, and ranking guidance. Use this skill when: (1) Evaluating design quality for binding, expression, or structure, (2) Setting filtering thresholds for pLDDT, ipTM, PAE, (3) Checking sequence liabilities (cysteines, deamidation, polybasic clusters), (4) Creating multi-stage filtering pipelines, (5) Computing PyRosetta interface metrics (dG, SC, dSASA), (6) Checking biophysical properties (instability, GRAVY, pI), (7) Ranking designs with composite scoring. This skill provides research-backed thresholds from binder design competitions and published benchmarks.

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Quality control metrics and filtering thresholds for protein design. Use this skill when: (1) Evaluating design quality for binding, expression, or structure, (2) Setting filtering thresholds for pLDDT, ipTM, PAE, (3) Checking sequence liabilities (cysteines, deamidation, polybasic clusters), (4) Creating multi-stage filtering pipelines, (5) Computing PyRosetta interface metrics (dG, SC, dSASA), (6) Checking biophysical properties (instability, GRAVY, pI), (7) Ranking designs with composite scoring. This skill provides research-backed thresholds from binder design competitions and published benchmarks.

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Building AI-powered personalization systems: recommendation engines, collaborative filtering, content-based filtering, user preference learning, cold-start solutions, and LLM-enhanced personalized experiences. Use when "recommendation system, personalization, collaborative filtering, content-based filtering, user preferences, recommend, suggestions, for you, similar items, you might like, " mentioned.

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YouYing Shopee product selection tool supporting product query and filtering across all Shopee marketplaces, covering Malaysia, Taiwan (China), Indonesia, Thailand, Philippines, Singapore, Vietnam, Brazil, Mexico, Chile, and Colombia. Triggered when users mention Shopee product selection, Shopee product search, Shopee bestsellers, Shopee market analysis, Shopee category selection, Shopee keyword selection, Shopee sales filtering, Shopee price filtering, Southeast Asia e-commerce product selection, Shopee product search, Shopee product selection, Shopee bestsellers, or Shopee market analysis. Even if the user does not explicitly mention "YouYing" or "Shopee," this skill should be triggered whenever their need involves searching for products or filtering Shopee product data on the Shopee platform.

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Use Nexscope data to search and filter Amazon products, supporting multi-dimensional criteria including price, monthly sales, BSR ranking, gross margin, ratings, fulfillment method, badges, seller origin, and more across multiple Amazon marketplaces. Trigger when the user mentions Amazon product selection research, product filtering, sales filtering, product discovery, BSR analysis, niche product discovery, competitor analysis, market opportunity assessment, product-level market size estimation, gross margin screening, Nexscope product selection, Amazon product selection, sales filtering, BSR analysis, profit screening, market analysis, product selection tool. Even if the user does not explicitly mention "Nexscope", if their need involves filtering and analyzing Amazon product-level data for product selection, this skill should also be triggered.

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Nexscope Product Database multi-condition filtering. Filter Amazon products by category, price, sales volume, revenue, reviews, rating, weight, BSR rank, LQS, seller type, and more across 10 marketplaces. Trigger when users mention Amazon product selection, product database filtering, BSR rank filtering, category-based product discovery, high-rating low-competition products, FBA product search, Amazon product discovery, or similar terms. Even if the user does not explicitly mention "Nexscope" or "product database," trigger this skill whenever the request involves filtering Amazon products by multiple criteria or discovering potential products.

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가정이나 소규모 실험실의 네트워크를 변경하기 전에 장비 구성과 복구 절차를 정리합니다. VLAN 분리, DNS 필터링, VPN 도입을 단계적으로 계획하고 점검합니다.

• 일상 기기와 사물 인터넷·방문자 기기를 분리합니다
• DNS 필터링으로의 전환을 계획합니다
• VPN 원격 접속을 검토합니다

REST API의 URL 명명, HTTP 상태 코드, 페이지네이션과 오류 응답을 설계하고 검토합니다. 인증, 접근 권한, 호출 횟수 제한과 버전 관리 방침도 정리합니다.

• 새 API의 URL과 응답을 설계할 때
• 기존 API 명세를 검토할 때
• 페이지네이션과 필터링 방식을 선택할 때

Prepare training datasets for large language models with GPU-assisted cleaning. Covers duplicate removal, quality filtering, and personal information redaction, with workflows for text, images, video, and audio using NeMo Curator.

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• Clean web data for model training
• Remove exact and near duplicates from large text collections
• Redact personal information from training text

Add or troubleshoot Table v9 features such as sorting, filtering, pagination, selection, merged cells, and column layout. Guidance helps check feature registration, dependencies, and row-processing settings, with references selected for the task.

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• Adding sorting, filtering, and pagination
• Investigating missing table APIs or state
• Configuring row selection, cell selection, or merged cells

Prepare training datasets for large language models with GPU-assisted cleaning. Covers duplicate removal, quality filtering, and personal information redaction, with workflows for text, images, video, and audio using NeMo Curator.

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• Clean web data for model training
• Remove exact and near duplicates from large text collections
• Redact personal information from training text

Build safety checks for LLM prompts and responses using Meta's LlamaGuard. This skill covers content classification, batch moderation, serving a moderation API, and connecting checks to NeMo Guardrails.

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• Screening user prompts before sending them to an LLM
• Checking generated responses before displaying them
• Building an API for content safety checks

Matched filtering techniques for gravitational wave detection. Use when searching for signals in detector data using template waveforms, including both time-domain and frequency-domain approaches. Works with PyCBC for generating templates and performing matched filtering.

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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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Guide to quality filtering raw VCF files before computing summary stats (Ts/Tv ratio, variant counts, AF distributions). Covers detecting raw VCFs via FILTER column and QUAL inspection, QUAL-based filtering with bcftools, Ts/Tv interpretation, and when NOT to filter. Read before any variant-level QC task. See bcftools-variant-manipulation for advanced filters, gatk-variant-calling for caller config, samtools-bam-processing for upstream alignment QC.

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Comprehensive variant filtering including GATK VQSR, hard filters, bcftools expressions, and quality metric interpretation for SNPs and indels. Use when filtering variants using GATK best practices.

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Control AdGuard Home DNS filtering via HTTP API. Use when managing blocklists/allowlists, checking domain filtering status, toggling protection, or clearing DNS cache. Supports blocking/allowing domains, viewing statistics, and protecting/disabling DNS filtering.

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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.

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