rebuild 0.1.11

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

rebuild

AI Cost Tracking

• 🤖 LLM usage: $1.6500 (11 commits)
• 👤 Human dev: ~$366 (3.7h @ $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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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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Quick start

🚀 Bootstrapping

# Initialize project config and .env
rebuild init

🧠 Intelligence & Analysis

# Find duplicates across Python and JS/TS
rebuild analyze duplicates .

# Export interactive architecture graph
rebuild analyze services --export

# Generate AI-powered refactor plan and PR description
rebuild refactor plan . --ai
rebuild refactor pr .

🏃 Execution Pipeline

# Analyze last 30 days history (Incremental)
rebuild walk . --days 30

# Launch TUI
rebuild tui

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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.