rebuild 0.1.13

Historical deployment analysis — walk git history, deploy per day, test all endpoints, capture screenshots, restore working fragments

rebuild

AI Cost Tracking

• 🤖 LLM usage: $1.9500 (13 commits)
• 👤 Human dev: ~$419 (4.2h @ $100/h, 30min dedup)

Generated on 2026-05-01 using openrouter/qwen/qwen3-coder-next

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Code Evolution Intelligence Engine

Historical deployment analysis & Code Intelligence — walk git history day by day, deploy per commit, test all endpoints, capture screenshots, and analyze code evolution to find duplicates, rank quality, and generate refactor plans.

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📖 Documentation

• Full Usage Guide: Step-by-step instructions.
• Architecture: Detailed layered design.
• Changelog: Latest v0.1.10 features.

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🚀 c2004 Case Study: Analyzing a Complex Ecosystem

Applying rebuild to the massive c2004 project (88 subdirectories, thousands of files).

1. Eliminating Cross-Component Duplication

Scenario: Identifying structural clones between the main backend and auxiliary modules like connect-test or frontend.

rebuild analyze duplicates /path/to/c2004

Result: Found 1,597 duplicate groups across Python and JS/TS files.

2. Architecture Visualization & Cycle Detection

Scenario: Mapping dependencies between connect-manager, workshop, and scenario to find architectural bottlenecks.

rebuild analyze services /path/to/c2004/backend --export

Result: Generated interactive D3.js map highlighting circular dependencies in the service layer.

3. AI-Powered Refactor Planning

Scenario: Generating an automated refactor plan with an AI Executive Summary for the team.

rebuild refactor plan /path/to/c2004/backend --ai
rebuild refactor pr /path/to/c2004/backend

Result: 122 high-impact suggestions with automated Markdown PR descriptions.

4. Health & Quality Timeline

Scenario: Tracking how code complexity affects system stability over time.

rebuild dashboard --repo /path/to/c2004

Result: Visualized correlation between technical debt and API pass rates.

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What it does

1. Intelligence Layer — Detects structural duplicates (Python/JS/TS), builds service graphs, and ranks code quality across history.
2. Decision Engine — Generates and executes refactoring plans with AI support.
3. Walk — Iterates through git history day by day (Incremental support).
4. Deploy — Starts the service per commit (Isolated Docker environments).
5. Scan & Test — Automated endpoint discovery and TestQL execution.
6. Visualization — D3.js interactive graphs and health dashboards.

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Examples

Explore ready-to-run scenarios in examples/:

• 01-dry-run-walk: Standard walk + intelligence.
• 02-docker-compose-project: Full pipeline with Docker isolation.
• 03-restore-endpoint: Discovering "truth" and extracting endpoints.

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License

Licensed under Apache-2.0.