rebuild 0.1.25

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

rebuild

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.

SUMD stats (v0.1.23): 3722 functions · 161 classes · 167 files · CC̄ = 3.9

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AI Cost Tracking

• 🤖 LLM usage: $7.3500 (49 commits)
• 👤 Human dev: ~$1384 (13.8h @ $100/h, 30min dedup)

Generated on 2026-05-07 using openrouter/qwen/qwen3-coder-next

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📖 Documentation

Document	Description
Getting Started	Installation, quickstart, configuration
Walk Command	Core walk workflow — deploy modes, output, rebuild.yaml
Analyze	Duplicates, service graph, truth, vector search, multi-repo
Refactor	AI-powered refactor plans + execution
Auto-PR	Automated GitHub/GitLab PR creation
Plugins	Custom scanners & reporters via entry points
CLI Reference	All commands and options
Config Reference	Full rebuild.yaml field reference
Architecture	5-layer design, services, data flow
Case Study: c2004	Real-world analysis of a large ecosystem
c2004 Testing Log	Detailed test log with commands & results
Usage Guide	Step-by-step guide (all features)
Changelog	Release history (current: v0.1.23)
Roadmap	Completed phases 10–16, future plans

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🚀 Quick Start

# Install
pip install rebuild

# Initialise config
rebuild init /path/to/my-service

# Walk last 14 days (dry-run, no deploy)
rebuild walk /path/to/my-service --deploy none --days 14 --dry-run

# Serve results
rebuild serve --results-dir /path/to/my-service/.rebuild

See Installation and Quick Start for details.

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What it does

#	Layer	Description
1	Intelligence	Structural & semantic duplicates (Python/JS/TS), service graphs, vector search, quality ranking
2	Decision Engine	AI-powered refactoring plans + automated execution
3	Walk	Git history iteration (Incremental, Replay, Accelerator modes)
4	Deploy	Docker Compose / Replay / Accelerator hot-reload per commit
5	Scan & Test	OpenAPI, FastAPI routes, Traefik discovery; auth, param substitution, body injection
6	Visualization	D3.js graphs, health dashboards, SSE streaming, evolution playback
7	Automation	Auto PR, DSL scripting, NLP commands, Plugins, Notifications

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🚀 c2004 Case Study

Applying rebuild to the massive c2004 project (88 subdirectories, thousands of files). See full details: Case Study · Testing Log

1. Duplication Detection

rebuild analyze duplicates /path/to/c2004

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

2. Architecture Graph

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

Result: Interactive D3.js map highlighting circular dependencies.

3. AI Refactor Planning

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

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

4. Health Dashboard

rebuild dashboard --repo /path/to/c2004

Result: Correlation between technical debt and API pass rates.

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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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Project Status

All Phases 10–16 completed. See TODO.md for full roadmap.

Milestone	Status
c2004 Testing & Intelligence (Phase 10)	✅ Done
Semantic Embeddings & Vector Search (Phase 11)	✅ Done
Multi-Repo & Real-time (Phase 12)	✅ Done
UI/UX (Phase 13)	✅ Done
Production Readiness (Phase 14)	✅ Done
c2004 Integration (Phase 15)	✅ Done
Production Release (Phase 16)	✅ Done — PyPI, CI/CD, Docker, MkDocs, Plugins, Notifications

Current: v0.1.23 · 634 tests · 72% coverage

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License

Licensed under Apache-2.0.