This is the .github organization repository for dofaromg. It provides default community health files, security workflows, and organizational resources that are automatically applied across all repositories in this organization.

• GitHub Actions Security Scanning: Automated security scans for all workflow files using Semgrep
• Workflow File: .github/workflows/action_scanning.yml
• Semgrep Rules: Custom security rules for GitHub Actions in semgrep-rules/actions/

• Code of Conduct: Based on Google's Open Source Community Guidelines
• Contributing Guidelines: Instructions for contributors including CLA requirements
• Security Policy: Vulnerability reporting process via https://g.co/vulnz

This repository provides the following organizational capabilities:

1. Automated Security Scanning: All repositories in the organization automatically inherit security scanning workflows
2. Standardized Community Guidelines: Consistent code of conduct and contributing guidelines across all projects
3. Security Vulnerability Management: Centralized security reporting and handling process

• For Repository Owners: These files automatically apply to repositories without their own versions
• For Contributors: Follow the guidelines in CONTRIBUTING.md and CODE_OF_CONDUCT.md
• For Security: Report vulnerabilities using the process outlined in SECURITY.md

這是 dofaromg 組織的 .github 倉庫。它提供預設的社群健康文件、安全工作流程和組織資源，這些資源會自動應用到組織內的所有倉庫。

• GitHub Actions 安全掃描: 使用 Semgrep 對所有工作流程文件進行自動化安全掃描
• 工作流程文件: .github/workflows/action_scanning.yml
• Semgrep 規則: 在 semgrep-rules/actions/ 中為 GitHub Actions 定制的安全規則

• 行為準則: 基於 Google 開源社群準則
• 貢獻指南: 包括 CLA 要求在內的貢獻者說明
• 安全政策: 通過 https://g.co/vulnz 的漏洞報告流程

這個倉庫提供以下組織能力：

1. 自動化安全掃描: 組織內的所有倉庫都會自動繼承安全掃描工作流程
2. 標準化社群準則: 在所有專案中保持一致的行為準則和貢獻指南
3. 安全漏洞管理: 集中式的安全報告和處理流程

• 對於倉庫擁有者: 這些文件會自動應用到沒有自己版本的倉庫
• 對於貢獻者: 遵循 CONTRIBUTING.md 和 CODE_OF_CONDUCT.md 中的指南
• 對於安全: 使用 SECURITY.md 中概述的流程報告漏洞

├── .github/
│   └── workflows/
│       └── action_scanning.yml    # Security scanning workflow
├── semgrep-rules/
│   └── actions/
│       └── pull_request_target_needs_exception.yml  # Security rules
├── CODE_OF_CONDUCT.md             # Community guidelines
├── CONTRIBUTING.md                # Contribution guidelines
├── SECURITY.md                    # Security policy
└── README.md                      # This file

1. Repository Creators: New repositories automatically inherit these defaults
2. Contributors: Read CONTRIBUTING.md before making contributions
3. Security Researchers: Follow the process in SECURITY.md for reporting vulnerabilities
4. Developers: The security workflows will automatically scan your GitHub Actions

This repository serves as the organizational hub for community standards and automated security processes.

The mrl/ package implements the MRL law_0 transformation pipeline — a system that absorbs SQL relational databases and decomposes them into a cloud of self-describing law_0 particle units, breaking free of SQL's fixed-schema First Normal Form paradigm.

Every row becomes a particle:

{
  "id":        "a3f9…",
  "kind":      "orders",
  "value":     { "id": 10, "product": "Widget", "amount": 9.99 },
  "relations": [
    { "kind": "orders__customers", "target_id": "b7c2…" }
  ]
}

mrl/
├── __init__.py          # Public API
├── particle.py          # Particle + Relation dataclasses (law_0 schema)
├── sql_ingest.py        # SQLIngestor — reads schema & rows from any SQL DB
├── sql_to_law0.py       # transform() — rows → particles, FKs → relation edges
├── particle_store.py    # ParticleStore — file-system particle library
├── query.py             # ParticleQuery — graph-traversal query layer
└── __main__.py          # CLI entry point
tests/
└── test_law0.py         # 37 unit tests (in-memory SQLite)

pip install -r requirements.txt

# Transform a SQLite database into a particle library
python -m mrl sqlite:///mydb.sqlite ./particle_lib

# Query particles of a specific kind after ingestion
python -m mrl sqlite:///mydb.sqlite ./particle_lib --query-kind orders

# Restrict to specific tables
python -m mrl sqlite:///mydb.sqlite ./particle_lib --tables customers orders

from mrl import SQLIngestor, transform, ParticleStore, ParticleQuery

# 1. Ingest + transform
ingestor  = SQLIngestor("sqlite:///mydb.sqlite")
particles = transform(ingestor)

# 2. Persist to particle file library
store = ParticleStore("./particle_lib")
store.write(particles)

# 3. Query (graph traversal replaces SQL JOINs)
q      = ParticleQuery(store)
orders = q.by_kind("orders")
customer_of_order = q.follow(orders[0], relation_kind="orders__customers")
graph  = q.traverse(orders[0], max_depth=3)
path   = q.shortest_path(orders[0].id, customer_of_order[0].id)

mrl/ 套件實現了 MRL law_0 轉換管線 — 一個吸收 SQL 關聯式資料庫並將其分解為自描述 law_0 粒子單元雲端的系統，突破 SQL 底層第一定律（固定結構的第一正規化）。

pip install -r requirements.txt
python -m mrl sqlite:///mydb.sqlite ./particle_lib

pytest tests/test_law0.py -v