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codebase-cost-estimator

Convert measured LOC and architectural complexity into development hours and total cost.

15|3|Updated Apr 9, 2026
Search Tags:#codebase#complexity#ROI#cost#LOC#estimate#overhead
One-click install
npx skills add https://github.com/LazyIsEfficient/agentic-os --skill codebase-cost-estimator
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: codebase-cost-estimator
Source: https://github.com/LazyIsEfficient/agentic-os/tree/main/.claude/skills/codebase-cost-estimator
Command: npx skills add https://github.com/LazyIsEfficient/agentic-os --skill codebase-cost-estimator

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) and assets (resource) components.

What problem does it solve?

Estimate the full cost of delivering an existing codebase by translating measured lines of code and architectural complexity into development hours and total cost, including calendar-time projections and Claude/AI ROI analysis when applicable.

Core Features & Use Cases

  • Measure LOC by language and code category (production, tests, docs) to drive initial hours estimates.
  • Apply overhead multipliers for architecture/design, debugging, review/refactoring, documentation, integration/testing, and learning curves to derive total engineering hours.
  • Research current-year market rates for the stack to build low/median/high rate scenarios and justify recommended rates.
  • Convert raw hours to calendar time using organizational overhead factors across company types (solo, startup, growth, enterprise).
  • Compute full-team costs using role ratios and multipliers to produce a complete cost picture for stakeholders.
  • Optionally compute Claude/AI ROI to quantify value added by AI-assisted development.
  • Assemble outputs into a stakeholder-ready template for reporting and planning.

This approach emphasizes measured LOC and complexity rather than ticket volume or story points, and requires explicit assumptions and ranges.

Quick Start

Count LOC in your codebase with a tool like tokei or cloc and feed the results into Claude Code to generate the estimate.

Frequently Asked Questions about codebase-cost-estimator

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I estimate the cost to build an existing codebase from lines of code?▼

Estimating codebase build cost involves measuring LOC by language, applying overhead multipliers for architecture and testing, researching market rates, and converting hours to calendar time using organizational factors to produce stakeholder-ready cost projections.

What overhead multipliers are applied when calculating development hours from LOC?▼

Overhead multipliers account for architecture and design, debugging, code review and refactoring, documentation, integration testing, and learning curves. These factors transform raw LOC-based hours into total engineering hours for accurate cost projection.

How do I convert development hours into calendar time for a software project estimate?▼

Converting development hours to calendar time applies organizational overhead factors across company types including solo, startup, growth, and enterprise. This adjusts raw engineering hours to reflect realistic project delivery timelines for stakeholder reporting.

Can I calculate AI ROI when estimating codebase reconstruction costs?▼

Yes, you can optionally compute Claude and AI ROI to quantify value added by AI-assisted development. This analysis compares AI-accelerated delivery costs against baseline manual development estimates to demonstrate AI ROI for stakeholders.

What is the best way to measure lines of code for a cost estimation template?▼

The best way to measure lines of code for cost estimation is using tools like tokei or cloc to count LOC by language and code category including production, tests, and docs. Feed these results into Claude to generate the estimate.

Does this codebase cost estimation approach use story points or ticket volume?▼

No, this codebase cost estimation approach emphasizes measured lines of code and architectural complexity rather than ticket volume or story points. It requires explicit assumptions and ranges to build defensible build-cost estimates for stakeholders.

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