b3ehive is a multi-agent framework that pits three AI agents against each other to implement, evaluate, and select the highest-quality code solution through objective competition.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install b3ehive
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 b3ehive 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 b3ehive?
b3ehive is a PCTF-compliant multi-agent competition system designed to eliminate bias and maximize code quality in automated development. By spawning three isolated AI agents—each focusing on a specific architectural priority like simplicity, speed, or robustness—this Openclaw Skills implementation ensures that every coding task is approached from multiple technical perspectives.
The system facilitates a rigorous cross-evaluation process where agents critique each other's work based on objective metrics such as cyclomatic complexity, time complexity, and fault tolerance. This competitive environment results in a data-driven selection of the optimal implementation, providing developers with not just code, but a comprehensive comparison report and a transparent decision rationale for the final choice.
b3ehive Use Cases
- Generating high-performance algorithms where speed and resource management are critical.
- Implementing mission-critical backend logic that requires robust error handling and fault tolerance.
- Automating the code review process by utilizing AI agents to provide objective peer-evaluations for Openclaw Skills.
- Rapidly prototyping multiple architectural approaches to find the most maintainable code structure.
How b3ehive Works
- Phase 1: Parallel Spawn: Three agents independently implement the task, each assigned a specific focus: Simplicity, Speed, or Robustness.
- Phase 2: Cross-Evaluation: Agents perform technical audits on their competitors, generating six detailed evaluation reports across five distinct dimensions.
- Phase 3: Objective Scoring: Each agent produces a scorecard for themselves and their peers, justifying numeric scores with specific code evidence.
- Phase 4: Final Delivery: A decision engine calculates score margins to select a single winner, a hybrid solution, or the simplest implementation as the final output.
b3ehive Setup
To get started with this skill, you can run it directly through your terminal or as part of the wider suite of Openclaw Skills. Ensure your configuration file includes the necessary model provider details.
# Basic usage with a task description
b3ehive "Implement a thread-safe rate limiter"
# Run with specific language constraints
b3ehive "Implement quicksort" --lang python --max-lines 50
# Using the Openclaw CLI interface
openclaw skills run b3ehive --task "Your specific coding task"
b3ehive Data Schema & Taxonomy
The skill maintains a strict directory structure within the workspace to ensure isolation and clear audit trails for each agent's contribution.
| Path | Content Description |
|---|---|
run_[a/b/c]/implementation/ |
The runnable source code produced by the specific agent. |
run_[a/b/c]/Checklist.md |
A markdown checklist verifying all task constraints were met. |
run_[a/b/c]/evaluation/ |
Cross-evaluation reports critiquing the other agents. |
final/ |
The directory containing the winning implementation selected by the system. |
COMPARISON_REPORT.md |
A markdown analysis comparing all approaches with objective metrics. |
DECISION_RATIONALE.md |
A transparent breakdown of why the specific winner was selected. |
b3ehive Advanced Features
- Configurable evaluation weights for five dimensions: simplicity, speed, stability, corner cases, and maintainability.
- Automated quality gates that include linter checks, test execution, and coverage threshold validation.
- Support for hybrid delivery strategies that can merge the best features of multiple implementations when scores are close.
- Built-in runtime assertions to ensure each phase of the multi-agent chain completes successfully within Openclaw Skills.
SKILL.md
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