A precision tool for architects to determine the optimal consistency model and implementation mechanism for distributed operations.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install bookforge-consistency-model-selector
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install bookforge-consistency-model-selector using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Consistency Model Selector?
The consistency-model-selector is a specialized skill for Openclaw Skills designed to navigate the complexities of distributed data systems. It helps developers move beyond the oversimplified pick-two-of-three CAP theorem narrative to select precise guarantees—such as linearizability, causal consistency, or eventual consistency—based on the specific correctness requirements of an operation.
By analyzing system requirements or codebase artifacts, this skill identifies whether an operation requires consensus-backed primitives like Raft or Paxos, evaluates the appropriateness of two-phase commit (2PC), and recommends specific tools like ZooKeeper or etcd. It ensures that system architects do not over-provision consistency at the cost of latency, nor under-provision it at the cost of data integrity.
Consistency Model Selector Use Cases
- Designing new distributed data systems and selecting replication mechanisms.
- Deciding between ZooKeeper, etcd, or custom leader election logic.
- Evaluating whether two-phase commit is suitable for cross-service transactions.
- Debugging production issues like stale reads, split-brain, or uniqueness constraint failures.
- Determining if leaderless (Dynamo-style) replication provides sufficient guarantees for a specific workload.
How Consistency Model Selector Works
- Analyze Requirements: The skill identifies the operation type (e.g., read, write, lock acquisition) and its impact on system correctness.
- Identify Minimum Guarantees: It maps the operation to a required model—eventual, causal, or linearizable—ensuring the weakest sufficient model is used to optimize performance.
- Consensus Evaluation: It checks the operation against a 6-problem checklist (e.g., uniqueness constraints, total order broadcast) to determine if a consensus algorithm is mandatory.
- Mechanism Selection: The skill recommends specific implementation strategies, such as version vectors for causal consistency or Raft/Zab for linearizability.
- Risk Assessment: For linearizable operations, it documents the CAP trade-off consequences, specifically how the system will behave during a network partition.
Consistency Model Selector Setup
To use this skill within your Openclaw Skills environment, ensure you have the necessary CLI tools installed. You can install this skill and its dependencies using the following commands:
# Install the core consistency selector skill
clawhub install bookforge-consistency-model-selector
# Install recommended companion skills
clawhub install bookforge-replication-strategy-selector
clawhub install bookforge-distributed-failure-analyzer
Consistency Model Selector Data Schema & Taxonomy
The skill organizes its recommendations using a structured per-operation taxonomy, typically presented in a matrix format:
| Attribute | Description |
|---|---|
| Operation | The specific task (e.g., Seat Booking, Leader Election) |
| Model | Recommended consistency level (Linearizability, Causal, Eventual) |
| Mechanism | The technical implementation (e.g., Raft, Quorum Reads, Version Vectors) |
| Consensus Req | Boolean indicator if a consensus protocol is required |
| Failure Mode | Documented behavior during network partitions (CAP trade-off) |
Consistency Model Selector Advanced Features
- Consensus Equivalence Mapping: Automatically identifies if a problem is reducible to consensus, preventing brittle custom implementations.
- 2PC Failure Catalog: Provides a detailed risk analysis for two-phase commit, including in-doubt transaction blocking scenarios.
- Fencing Token Guidance: Recommends tokens (like zxid) for distributed locks to prevent stale leader interference.
- Hybrid Input Analysis: Can process both raw codebases (docker-compose, DB configs) and high-level architectural documents.
SKILL.md
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