A data-driven analytics tool for benchmarking construction costs and tracking historical escalation patterns.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install historical-cost-analyzer
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install historical-cost-analyzer using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Historical Cost Analyzer?
The Historical Cost Analyzer is a sophisticated Openclaw Skills component designed to transform raw project data into actionable financial intelligence. By leveraging historical cost indices and location-specific factors, this skill allows estimators and project managers to normalize past performance data to current market conditions. It provides a statistical framework for understanding cost drivers and improving the accuracy of future project budgets.
This skill is essential for organizations looking to move beyond gut-feeling estimates toward a data-centric approach. It automates the complex task of adjusting for inflation and regional price variations, ensuring that every benchmark used in your planning process is calibrated to a national average or specific local market. Integrating this into your Openclaw Skills workflow ensures consistent and defensible financial projections.
Historical Cost Analyzer Use Cases
- Benchmarking current estimates against a repository of completed projects to ensure market alignment.
- Calculating cost escalation rates between specific years to adjust long-term budgets.
- Identifying hidden cost drivers by analyzing correlations between project variables and final spend.
- Performing similarity searches to find historical precedents for unique or complex project types.
- Analyzing historical overrun patterns to better quantify risk contingencies in new proposals.
How Historical Cost Analyzer Works
- Import historical project data through a structured pandas DataFrame containing costs, dates, and locations.
- Apply normalization algorithms to adjust historical figures based on the RSMeans City Cost Indexes and annual escalation factors.
- Execute benchmark calculations to determine median costs per square foot and percentile distributions across the dataset.
- Run statistical correlation tests (Pearson r) to identify which factors, such as project size or location, have the highest impact on cost.
- Compare active estimates against the normalized historical dataset to determine the project's cost percentile.
- Generate a detailed Markdown report summarizing benchmarks, escalation trends, and risk analysis for stakeholders.
Historical Cost Analyzer Setup
To utilize this skill within your environment, ensure you have the required analytical libraries installed via pip:
pip install pandas numpy scipy
Once installed, you can initialize the Historical Cost Analyzer by loading your project history from an Excel or CSV file into the provided class structure.
Historical Cost Analyzer Data Schema & Taxonomy
The analyzer organizes data into a structured format for statistical processing. Below are the core data points managed by the skill:
| Data Point | Description | Key Metric |
|---|---|---|
CostBenchmark |
Statistics for specific metrics | Median, 25th/75th Percentiles |
EscalationAnalysis |
Year-over-year price changes | Annual Rate, Total Change |
CostDriver |
Impact factors | Correlation Coefficient, Impact % |
ProjectData |
Raw project attributes | Gross Area, Final Cost, Location |
Historical Cost Analyzer Advanced Features
- Multi-year escalation modeling supporting projections and historical adjustments from 2015 through 2026.
- Regional normalization using a built-in library of City Cost Indexes for major North American hubs.
- Automated similarity engine that uses area tolerance and project classification to find the most relevant historical matches.
- Statistical overrun analysis that segments budget performance by project size and type.
- Comprehensive automated report generation for quick stakeholder communication.
SKILL.md
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