Skills DirectoryData & Analytics
Data analysis, BI, visualization, datasets, statistics, and ML workflows
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Showing 10,417–10,440 of 13,069 skills
Continue working on an OpenSpec change by creating the next artifact. Use when the user wants to progress their change, create the next artifact, or continue their workflow.
Archive multiple completed changes at once. Use when archiving several parallel changes.
Archive a completed change in the experimental workflow. Use when the user wants to finalize and archive a change after implementation is complete.
Grill the user against a repo-local CONTEXT.md, sharpen ambiguous terminology, ground terms in code, and update shared language before implementation. Use when shaping a feature, bugfix, refactor, or repo workflow in an existing codebase.
Control and document the FieldWorks desktop application with WinForms MCP or WinApp MCP. Use this skill whenever a task requires launching FieldWorks, restoring or opening a FieldWorks project, walking WinForms UI flows, collecting manual screenshots, reproducing UI bugs, or verifying a fix inside the live FLEx application.
Implement the approved plan or design with minimal scope and adherence to repo conventions.
Organize the ~/Downloads folder by categorizing files and subdirectories into subfolders using AI-driven analysis.
Create self-contained HTML apps from user data (CSV, JSON, bank statements, APIs) with interactive tables and charts.
Classifies sensitive data in AI/ML training datasets including bias detection for Art. 9 categories, data card documentation, provenance tracking, and consent verification for model training. Keywords: AI training data, ML dataset, bias detection, data card, model training, Art 9, consent, GDPR AI.
Managing privacy risks from AI-driven inferences about individuals including derived data classification, profiling under GDPR Art. 22, inference accuracy obligations, and controlling automated personality/behaviour predictions. Keywords: AI inference, derived data, profiling, automated predictions, GDPR.
Computational pathology toolkit for analyzing whole-slide images (WSI) and multiparametric imaging data. Use this skill when working with histopathology slides, H&E stained images, multiplex immunofluorescence (CODEX, Vectra), spatial proteomics, nucleus detection/segmentation, tissue graph construction, or training ML models on pathology data. Supports 160+ slide formats including Aperio SVS, NDPI, DICOM, OME-TIFF for digital pathology workflows.
Recalculate XLSX formula outputs in Node.js after cell edits without opening Excel, LibreOffice, or browser automation.
Add fresh formula readback to SheetJS and xlsx workflows after writing XLSX inputs in Node.js.
Recalculate formula outputs for ExcelJS workbook flows in Node.js after agents or services edit cells.
Use bilig-workpaper WorkPaper state for workbook formulas, agent spreadsheet tools, MCP file-backed or remote demo editing, and XLSX formula bug reports without driving spreadsheet UI.
Recalculate XLSX formula outputs in Node.js after cell edits without opening Excel, LibreOffice, or browser automation.
Use @bilig/workpaper WorkPaper state for workbook formulas, agent spreadsheet tools, MCP file-backed or remote demo editing, and XLSX formula bug reports without driving spreadsheet UI.
ETF量化趋势分析技能——基于\"ETF资金流向判断股市趋势\"分析框架，输入日期自动获取ETF资金流向数据，输出结构化量化分析报告。核心分析维度：宽基ETF资金流向（大盘趋势）、行业ETF资金分化（结构性行情）、跨境ETF溢价率（市场情绪）、国家队动向（政策底信号）。触发场景：用户要求分析某日ETF趋势、ETF资金流向分析、ETF量化报告、股市趋势判断（基于ETF）、ETF底部信号/顶部信号识别、风格切换判断，或提到\"etf-quant-dao\"时使用。
Surveys the current repo's open GitHub issues, ranks them by triage label and dependency graph, and recommends an optimal execution order; when you pick one to start, it gates on whether the issue is clear enough to execute and routes unclear ones to the grill-with-docs skill before any code is written. It can also render the board as a self-contained HTML map that groups issues into business lines, draws their dependency arrows, and spells out which feature each chain ships once completed. U...
Molecular machine learning toolkit. Property prediction (ADMET, toxicity), GNNs (GCN, MPNN), MoleculeNet benchmarks, pretrained models, featurization, for drug discovery ML.
Molecular machine learning toolkit. Property prediction (ADMET, toxicity), GNNs (GCN, MPNN), MoleculeNet benchmarks, pretrained models, featurization, for drug discovery ML.
Molecular machine learning toolkit. Property prediction (ADMET, toxicity), GNNs (GCN, MPNN), MoleculeNet benchmarks, pretrained models, featurization, for drug discovery ML.
Molecular machine learning toolkit. Property prediction (ADMET, toxicity), GNNs (GCN, MPNN), MoleculeNet benchmarks, pretrained models, featurization, for drug discovery ML.
Molecular machine learning toolkit. Property prediction (ADMET, toxicity), GNNs (GCN, MPNN), MoleculeNet benchmarks, pretrained models, featurization, for drug discovery ML.