Results for “data-quality”

Analyze single-cell RNA-seq data with Scanpy, covering quality control, normalization, clustering, marker gene identification, visualization, and trajectory analysis.

Creates interactive, publication-quality charts with 40+ types, hover tooltips, zoom, and web-embeddable exports for dashboards and exploratory analysis.

Analyze single-cell RNA-seq data with Scanpy, covering quality control, normalization, clustering, marker gene identification, visualization, and trajectory analysis.

Enforces SQL quality gates after every implementation: lint with SQLFluff, validate migrations with Flyway or Liquibase, and run database tests with pgTAP or equivalents.

Analyze single-cell RNA-seq data with Scanpy, covering quality control, normalization, clustering, marker gene identification, visualization, and trajectory analysis.

Scrapes Amazon product data from ASINs using browseract.com automation API and performs competitive analysis, comparing specifications, pricing, review quality, and visual strategies.

Designs and implements production-grade RAG systems by chunking documents, generating embeddings, configuring vector stores, building hybrid search pipelines, applying reranking, and evaluating retrieval quality.

Create publication-quality static, animated, and interactive plots with fine-grained control over every element, from basic charts to multi-panel figures, with export to PNG, PDF, and SVG.

Create interactive, publication-quality visualizations with 40+ chart types, hover, zoom, pan, and web embedding. Choose between Plotly Express and Graph Objects for dashboards, exploratory analysis, and presentations.

Create interactive, publication-quality visualizations with 40+ chart types, including scatter, bar, line, 3D, maps, and financial charts, with support for hover, zoom, pan, subplots, and export to HTML or static images.

Review PostgreSQL code for best practices, anti-patterns, and quality standards including JSONB, arrays, custom types, schema design, functions, and security features like Row Level Security.

Creates interactive, publication-quality visualizations in Python with 40+ chart types, including scatter, bar, heatmap, 3D, and financial charts, plus subplots, styling, and export to HTML or static images.

Inspect, profile, clean, reconcile, analyze, visualize, and verify spreadsheet data while preserving formulas, formatting, types, and source files. Use when working with .xlsx, .xlsm, .xls, .ods, .csv, or .tsv files; answering questions from a workbook; auditing formulas or data quality; comparing sheets or versions; producing pivots, charts, forecasts, or summary workbooks; repairing malformed tables; or validating that spreadsheet edits and calculations are accurate.

Read-only audit of a codemod or bulk mechanical transform for behavior-preservation — compiles/lints is not same-behavior. Use when "did this codemod break anything", "audit this bulk refactor", or before merging a mass find-replace. Diff quality → audit-code-review. SQL → plan-data-integrity.

After a Content Factory run completes successfully, harvest prompts, character references, cost data, and quality scores; diff them against the canonical prompt_registry and character_identity store; open improvement PRs (prompt updates + new character references). Runs ONCE per successful run, then closes. Also files findings into a CF_IMPROVER_ISSUES.csv (Intelliverse audit framework pattern) for the librarian's weekly roll-up.