arch-distributed

Purpose

This skill equips OpenClaw to analyze, design, and implement distributed systems concepts, including CAP theorem, consensus algorithms like Raft and Paxos, eventual consistency, distributed transactions (2PC and SAGA), and CRDTs. Use it to generate code snippets, evaluate trade-offs, or simulate behaviors in distributed architectures.

When to Use

Apply this skill when building scalable applications facing network partitions, such as microservices, cloud databases, or blockchain systems. Use it for CAP theorem decisions (e.g., prioritizing availability over consistency), consensus in fault-tolerant clusters, or handling eventual consistency in data replication. Avoid it for non-distributed tasks like single-server apps.

Key Capabilities

• Analyze CAP theorem: Evaluate system designs for consistency, availability, and partition tolerance using predefined checks.
• Implement consensus: Generate Raft or Paxos logic, including leader election and log replication.
• Handle eventual consistency: Simulate resolution mechanisms like anti-entropy or read-repair.
• Manage transactions: Produce 2PC for atomic commits or SAGA for long-running processes with compensating actions.
• Work with CRDTs: Create implementations for counters or sets that merge without conflicts.

Usage Patterns

Invoke this skill via OpenClaw's CLI or API by specifying the skill ID and parameters. For CLI, use openclaw run arch-distributed --input <JSON_FILE> to process a configuration file. In code, import OpenClaw's SDK and call openclaw.skills.execute('arch-distributed', params={}). Always provide a JSON input with keys like "topic" (e.g., "cap") and "action" (e.g., "analyze"). For example, to check CAP trade-offs, structure input as: {"topic": "cap", "system": {"consistency": "strong", "availability": "high"}}. Chain skills by piping outputs, e.g., run this after a database design skill.