“data quality”的搜索结果

Comprehensive SEO analysis for any website or business type. Orchestrates 52 sub-skills (52 active): full site audits, technical SEO (CWV/INP), schema, E-E-A-T content quality, images, sitemaps, GEO for AI Overviews, llms.txt generation, AI crawler audit, plus live GSC and Ahrefs MCP data. GSC: overview, drops, opportunities, brand-vs-nonbrand, cannibalization, compare, content-decay, index-issues, new-keywords. Ahrefs: overview, backlinks, keywords, competitors, content-gap, broken-links, new-links, anchor-analysis, dr-history, top-pages. Cross-MCP: serp, content-brief, brand-radar, site-audit-pro, report. Local: log-analysis, ai-content-check, internal-links, local, migration-check, obsidian-kb. AI Readability: llms-txt, robots-ai. Performance: lighthouse-audit, core-web-vitals, page-experience. Quality & AI Search: rich-results, ai-optimization, eeat-audit, helpful-content. Triggers on: "SEO", "audit", "schema", "Core Web Vitals", "sitemap", "E-E-A-T", "AI Overviews", "GEO", "technical SEO", "content quality", "structured data", "GSC", "Ahrefs", "backlinks", "keywords", "search console", "domain rating", "local SEO", "migration", "lighthouse", "pagespeed", "LCP", "INP", "CLS", "page experience", "interstitials", "mobile friendly", "rich results", "rich snippets", "schema eligibility", "AI optimization", "AI search readiness", "EEAT", "trust signals", "helpful content", "people-first", "content quality gate".

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Conduct rigorous cross-study meta-analysis of scRNA-seq data from ENCODE, integrating multiple single-cell transcriptomic datasets for a tissue/cell type. Use when the user wants to answer "what cell types exist in my tissue and what genes define them?" by combining scRNA-seq data across donors, labs, and platforms. Follows the Mawla et al. 2019 framework for assessing cross-study reproducibility, TIN-based quality filtering, and detection-limit-aware interpretation. Handles batch correction (Harmony/Seurat), dropout awareness, cross-contamination artifacts, and platform-specific biases. Use this skill for ANY scRNA-seq integration task, cross-dataset comparison, cell atlas construction, or reproducibility assessment involving ENCODE single-cell data.

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Use when writing, auditing, or troubleshooting Salesforce Validation Rules. Triggers: 'validation rule', 'required field formula', 'rule fires unexpectedly', 'integration failing validation', 'data quality', 'errorConditionFormula', 'errorDisplayField', 'errorMessage 255 characters', 'ValidationRule metadata', 'validationRule-meta.xml', 'deploy validation rule inactive', 'bypass custom permission', 'FIELD_CUSTOM_VALIDATION_EXCEPTION', 'validation rule on custom metadata type', 'compound field validation rule', 'require at least one child record before a stage', 'HasOpportunityLineItem'. NOT for Flow-based validation — use admin/flow-for-admins for that.

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Audit research data management infrastructure -- score FAIR principles compliance (Findable, Accessible, Interoperable, Reusable) across all sub-principles, evaluate data governance frameworks and lifecycle management, assess metadata schema quality (Dublin Core, DataCite, DDI.

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Codified expertise for quality control, non-conformance investigation, root cause analysis, corrective action, and supplier quality management in regulated manufacturing. Informed by quality engineers with 15+ years experience across FDA, IATF 16949, and AS9100 environments. Includes NCR lifecycle management, CAPA systems, SPC interpretation, and audit methodology. Use when investigating non-conformances, performing root cause analysis, managing CAPAs, interpreting SPC data, or handling supplier quality issues.

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Credit risk data cleaning and variable screening pipeline for pre-loan modeling. Use when working with raw credit data that needs quality assessment, missing value analysis, or variable selection before modeling. it covers data loading and formatting, abnormal period filtering, missing rate calculation, high-missing variable removal,low-IV variable filtering, high-PSI variable removal, Null Importance denoising, high-correlation variable removal, and cleaning report generation. Applicable scenarios arecredit risk data cleaning, variable screening, pre-loan modeling preprocessing.

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帮助将机器学习实验整理为生产系统，明确数据规格、可复现训练、质量评估、部署、监控与回退，并形成实施计划和审查事项。

• 将实验代码整理为训练流水线
• 制定模型发布前的质量标准
• 调查数据泄漏与预处理差异

Evaluate 3D point clouds and movement trajectories against reference data with CloudAnalyzer. Prepare inputs, measure differences, apply quality thresholds, inspect results visually, and track baseline history for mapping, localization, and perception workflows.

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• Compare a generated point cloud with reference data
• Measure errors in estimated movement trajectories
• Check ground segmentation against quality thresholds

Analyze single-cell RNA-seq data with Scanpy, from quality checks and normalization to cell grouping, exploratory marker genes, and plots. Bundled scripts also prepare sample-level count summaries and support conversion of R objects to h5ad.

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• Check data quality and normalize single-cell measurements
• Visualize cell groups with UMAP or t-SNE
• Explore marker genes and review cell-type labels

Analyze Neuropixels neural recordings with SpikeInterface, from loading and preprocessing to motion correction and spike sorting, which separates signals by neuron. Calculate quality metrics, review candidate units, and export plots and data for further inspection.

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• Inspect and preprocess SpikeGLX, Open Ephys, or NWB recordings
• Check and correct movement-related drift before spike sorting
• Evaluate and select units using quality metrics

Build searches that find similar meanings using vector databases, which store numerical representations of data. Choose search indexes, combine semantic and keyword search, and tune retrieval quality and response time for search, recommendations, or AI answer systems.

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• Build search that finds documents with similar meanings
• Improve document retrieval for AI answers
• Design a recommendation engine

Build an end-to-end evaluation pipeline for Python AI applications using pixie-qa and real language-model calls. Define criteria, prepare realistic test data, score application runs, and analyze the evidence to produce a prioritized improvement plan.

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• Setting up quality checks for Python AI applications
• Creating realistic scenarios for known failure modes
• Scoring full application runs with real language-model calls

Improve LLM prompts using Arize production traces, evaluation results, and human feedback. Extract current instructions, identify recurring failures, revise the prompt, and compare experiments to measure changes in output quality and catch regressions.

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• Improve a prompt using failed production traces
• Fix responses that use the wrong output format
• Review unsupported answers in retrieval-based responses

Plan large sets of search-focused pages using templates and data, such as location guides, comparisons, and directories. Define unique content for each page, URL patterns, internal links, and quality checks before launch.

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• Planning service pages for multiple locations
• Designing templates for product comparison pages
• Structuring a directory or industry glossary

Design a RAG pipeline that retrieves documents to support AI answers. Analyze document splitting, select embedding models and vector databases, and evaluate retrieval quality using test queries and known relevant documents.

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• Designing a RAG system for internal documents
• Choosing how to split documents for search
• Comparing embedding models and vector databases

Automate website tasks such as navigating pages, filling forms, clicking buttons, taking screenshots, and extracting data. The skill also supports web app testing, with specialized guides for Electron desktop apps, Slack, and cloud browsers.

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• Automate form entry and page navigation
• Capture screenshots and extract page data
• Explore web apps for bugs

Plan and audit large sets of pages generated from structured data. Assess source quality, template uniqueness, URLs, and internal links, then identify thin content and unnecessary search indexing with prioritized recommendations and staged publishing guidance.

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• Plan directory or glossary pages from structured data
• Audit repetitive content across generated pages
• Design URL patterns and automated internal links

Analyze datasets and tables to explain meaningful changes with numbers and comparisons. Check data quality, distinguish supported findings from hypotheses, and recommend charts that help readers understand the results and make decisions.

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• Comparing monthly sales trends
• Examining differences across regions or segments
• Measuring gaps against targets

Plan or run quality checks and peak calling, which identifies enriched genomic regions, for ChIP-seq, CUT&RUN, and CUT&Tag. The workflow accounts for controls and replicates and prepares signal tracks, quality reports, and differential binding designs.

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• Check data quality and enrichment signals
• Choose narrow or broad peaks for the biological target
• Create bigWig tracks and quality dashboards

Plan AI-assisted SEO, from keyword research and competitor comparison pages to content production using data and templates. It also guides content changes for AI search citations, tool selection, quality checks, and ongoing performance tracking.

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• Planning competitor alternative and comparison pages
• Designing page production using data and templates
• Reviewing content for citations in AI Overviews

Create, audit, and improve pages for AI search engines by checking crawl access, metadata, clear answers, sources, and structured data that describes page content. It produces prioritized fixes and citation-friendly pages without guaranteeing inclusion in AI answers.

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• Audit a blog for AI search visibility
• Create an article page with clear answers and supporting sources
• Fix metadata and structured data on existing pages

Choose metrics for AI product revenue, sales efficiency, retention, and computing costs. Plan dashboards and weekly reviews, assess customer data quality, and decide how to credit marketing and sales interactions for completed deals.

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• Choosing revenue and sales metrics for an AI product
• Planning dashboards for executives and sales teams
• Assessing CRM data quality

Search YouTube by keyword and upload date, then collect video metrics and channel details through BrowserAct. Results include views, likes, comments, duration, and subscriber counts for comparing videos and researching activity around a topic.

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• Collecting metrics for videos published this week
• Researching recent videos from competing channels
• Comparing reactions to campaign videos

Turn a detailed outline into a philosophy or interdisciplinary manuscript. Draft chapters with repeated quality reviews, check terminology and arguments across sections, and prepare the manuscript, evaluation reports, abstract, and metadata for submission.

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• Draft a full paper from a detailed outline
• Review argument and citation quality chapter by chapter
• Check terminology and argument flow across chapters