2 399 data analysis skills from 443 authors. They crunch numbers, pivot tables and turn both into something readable. Half of them fit into 1 951 tokens or less — that is what one costs your context window when the agent loads it. 545 ship runnable scripts rather than instructions alone. 31 of them cannot work without an MCP server, most often rube. We also found 355 copies of these same skills sitting in other people's repositories — counted once here, not 355 times.
2 399 unique 443 authors 1 328 updated this month 230 from vendors
Calculate embodied carbon in construction materials. Track CO2 emissions, compare alternatives, and generate sustainability reports.
Handle CSV files from construction software exports. Auto-detect delimiters, encodings, and clean messy data.
Profile construction data to understand characteristics, distributions, quality metrics, and patterns. Essential for data quality assessment and ETL planning.
Read and parse XML from construction systems - P6 schedules, BSDD exports, IFC-XML, COBie-XML. Convert to pandas DataFrames.
Merge pandas DataFrames from multiple construction sources. Handle different schemas, keys, and data quality issues.
Automate construction data processing using LLM (ChatGPT, Claude, LLaMA). Generate Python/Pandas scripts, extract data from documents, and create automated pipelines without deep programming knowledge.
Comprehensive Pandas toolkit for construction data analysis. Filter, group, aggregate BIM elements, calculate quantities, merge datasets, and generate reports from structured construction data.
Assess construction data quality using completeness, accuracy, consistency, timeliness, and validity metrics. Automated validation with regex patterns, thresholds, and reporting.
Extract quantities from BIM/CAD data for cost estimation. Group by type, level, zone. Generate QTO reports.
Create 4D construction simulations by linking BIM elements to project schedules. Generate time-based visualizations, sequence analysis, and construction phasing with Gantt integration.
Generate Quantity Take-Off (QTO) reports from BIM/CAD data. Extract volumes, areas, counts by category. Group elements, apply calculation rules, and create cost estimates automatically.
Generate Gantt charts for construction scheduling. Create visual project timelines with dependencies and progress tracking.
Extract and analyze data from construction ERP systems. Pull project data for analytics, reporting, and integration.
Create visualizations for construction data. Generate charts, graphs, heatmaps, and interactive dashboards using Matplotlib, Seaborn, and Plotly for project analysis and reporting.
Build KPI dashboards for construction projects. Track CPI, SPI, quality, safety metrics in real-time.
Forecast project outcomes using historical data: cost overruns, schedule delays, risk probabilities. Machine learning models for construction prediction.
Build automated ETL (Extract-Transform-Load) pipelines for construction data. Process PDFs, Excel, BIM exports. Generate reports, dashboards, and integrate with other systems. Orchestrate with Airflow or n8n.
Automatically generate PDF reports from construction data. Create formatted project reports with charts and tables.
Analyze large-scale construction datasets. Process thousands of projects for patterns, benchmarks, and predictive insights.
Automate daily construction report generation using n8n workflow automation.
Construction safety incident reporting and analysis. Capture incidents, conduct investigations, track corrective actions, and analyze trends for prevention.
PowerPoint generation for construction: project updates, stakeholder presentations, progress reports, bid presentations. Automated slide creation with charts and data.
Excel/spreadsheet processing for construction: estimates, schedules, tracking logs, quantity takeoffs. Formulas, formatting, analysis.
Security review checklist for construction software systems. Use when building integrations, APIs, data pipelines, or dashboards for construction projects.
Synchronize construction digital twins with real-time data. Connect BIM models with IoT sensors, progress updates, and field data for live project visualization and monitoring.
Analyze construction labor productivity using data analytics. Track worker performance, identify inefficiencies, predict resource needs, and optimize crew allocation for maximum efficiency.
IoT-based material tracking for construction sites. Monitor material delivery, storage conditions, usage, and inventory with sensors, RFID, GPS, and real-time dashboards.
Multi-project portfolio analytics dashboard. Aggregate KPIs across projects, track portfolio health, compare performance, and support executive decision-making.
Automated safety compliance verification for construction sites. Check PPE usage, zone access, working at heights regulations, and generate compliance reports using rule-based and ML approaches.
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Free DeFi analytics across all chains — TVL, token prices, DEX volumes, fees/revenue, stablecoins, and bridges
Technical analysis with 130+ indicators using pandas-ta for crypto market data
Portfolio-level performance measurement including return metrics, risk metrics, risk-adjusted ratios, rolling analysis, and HTML reports
Solana token data, PnL, risk scores, 1-second OHLCV, wallet analytics, and self-hosted swap execution via Raptor
Token holder distribution, concentration metrics, insider detection, and supply analysis for Solana tokens
Professional trading charts including candlesticks, equity curves, drawdowns, correlation heatmaps, and return distributions
High-performance vectorized backtesting with parameter optimization, portfolio simulation, and rich performance metrics
Create a personal athlete 81-cell MandalArt grid from an Ohtani Shohei-style 64+8+1 model. Use when the user asks for 大谷翔平 81 宮格, 個人運動員81宮格, sports skill maps, athlete training Mandala charts, badminton 81 grids, or editable JSON/SVG/PNG-ready athlete development templates with Ohtani-style colors.
Generate statistical analysis code with 4-round review. Select appropriate statistical tests, interpret results, and produce analysis reports with p-values, effect sizes, and confidence intervals. Use when analyzing experimental data for a paper.
Generate publication-quality scientific figures using matplotlib/seaborn with a three-phase pipeline (query expansion, code generation with execution, VLM visual feedback). Handles bar charts, line plots, heatmaps, training curves, ablation plots, and more. Use when the user needs figures, plots, or visualizations for a paper.
Generate publication-quality LaTeX tables from experimental results. Convert JSON/CSV data to booktabs-styled tables with bold best results, multi-row layouts, and proper captions. Use when creating result tables, comparison tables, or ablation tables for papers.
IR playbook execution, evidence collection, forensic timeline analysis, memory forensics, and post-incident reporting following NIST SP 800-61 and SANS PICERL methodology
SOC alert triage, incident playbook automation, escalation workflows, shift reporting, and SOC KPI tracking
| Datadog integration. Manage Monitors, Dashboards, Incidents, Notebooks, Logs, Metrics and more. Use when the user wants to interact with Datadog data.
| Directus integration. Manage Collections, Users, Presets, Dashboards, Flows. Use when the user wants to interact with Directus data.
| Geckoboard integration. Manage Dashboards, Datasets. Use when the user wants to interact with Geckoboard data.
| Google Analytics integration. Manage Accounts. Use when the user wants to interact with Google Analytics data.
| Google Sheets integration. Manage analytics data, records, and workflows. Use when the user wants to interact with Google Sheets data.