Data & Analytics

Apply geographic location factors to CWICR estimates. Adjust costs for regional labor rates, material prices, and market conditions.

Schedule labor crews based on CWICR norms and project timeline. Calculate crew sizes, shifts, and labor loading curves.

Track and analyze historical cost data using CWICR. Compare actual vs estimated costs, build project cost database, and improve future estimates.

Apply price escalation to CWICR estimates over time. Calculate inflation adjustments, material price indices, and labor rate increases.

Plan equipment requirements using CWICR norms. Calculate equipment hours, scheduling, utilization rates, and rental vs purchase analysis.

Validate CWICR data quality and estimate inputs. Check for errors, inconsistencies, outliers, and missing data.

Load and parse DDC CWICR construction cost database from multiple formats: Parquet, Excel, CSV, Qdrant snapshots. Foundation for all CWICR operations.

Optimize crew composition using CWICR labor norms. Balance productivity, cost, and skill requirements for construction crews.

Calculate construction costs using DDC CWICR resource-based methodology. Break down costs into labor, materials, equipment with transparent pricing.

Compare cost estimates across projects, versions, and scenarios. Identify variances, benchmark against standards, and generate comparison reports.

Build cost assemblies from CWICR work items. Combine multiple items into reusable templates for common construction elements.

Generate images and visualizations from Revit/IFC files without BIM software. Python-based noBIM tool for batch processing.

Create interactive KPI dashboards for construction projects. Track schedule, cost, quality, and safety metrics in real-time.

Analyze labor productivity from site data. Compare planned vs actual, identify trends, benchmark against industry standards.

Train ML models with scikit-learn, PyTorch, TensorFlow. Use for classification/regression, neural networks, hyperparameter tuning, or encountering overfitting, underfitting, convergence issues.

Designs effective KPI dashboards with proper metric selection, visual hierarchy, and data visualization best practices. Use when building executive dashboards, creating analytics views, or presenting business metrics.

This skill should be used when analyzing CSV datasets, handling missing values through intelligent imputation, and creating interactive dashboards to visualize data trends. Use this skill for tasks involving data quality assessment, automated missing value detection and filling, statistical analysis, and generating Plotly Dash dashboards for exploratory data analysis.

This skill should be used when working with CSV files to create interactive data visualizations, generate statistical plots, analyze data distributions, create dashboards, or perform automatic data profiling. It provides comprehensive tools for exploratory data analysis using Plotly for interactive visualizations.

Extract reusable patterns from CFN Loop retrospective data to improve future sprint execution. - Analyze feedback themes - Identify recurring patterns - Calculate pattern frequency - Generate pattern recommendations 1. Analyze feedback themes across multiple sprints 2. Use statistical methods to identify significant patterns 3. Create pattern confidence score based on: - Frequency of occurrence - Impact on sprint success - Reliability of evidence - Agent Synergy P

Analyze and optimize tweets for maximum reach using Twitter's open-source algorithm insights. Rewrite and edit user tweets to improve engagement and visibility based on how the recommendation system ranks content.

Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude needs to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv, etc) for: (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing data, (3) Modify existing spreadsheets while preserving formulas, (4) Data analysis and visualization in spreadsheets, or (5) Recalculating formulas

Adaptive epoch selection for Walk-Forward Optimization. TRIGGERS - WFO epoch, epoch selection, WFE optimization, overfitting epochs.

Analyze team AI usage patterns and productivity metrics. Activate when users mention "usage analytics", "ai metrics", "productivity tracking", "usage reports", or "roi analysis". Handles analytics and reporting configuration. Use when working with windsurf usage analytics functionality. Trigger with phrases like "windsurf usage analytics", "windsurf analytics", "windsurf".

Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude needs to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv, etc) for: (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing data, (3) Modify existing spreadsheets while preserving formulas, (4) Data analysis and visualization in spreadsheets, or (5) Recalculating formulas