R

rewardkit

VERIFIED

by community

No community reviews yet
143,890,500installs
Updated Sep 2026

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Description

Help the user write task verifiers with Reward Kit. Reward Kit is a lightweight Python package that turns a directory of criteria files into a reward score. Each criterion is a Python function call or a TOML judge file; folders become separate rewards. ## Setup in a Harbor task Put criteria alongside `test.sh` in the task's `tests/` directory: ``` tests/ ├── test.sh ├── checks.py # programmatic criteria └── judge.toml # optional LLM/agent judge ``` `tests/test.sh`: ```bash #!/bin/bash uvx --from 'harbor-rew

Install rewardkit

Run this in your OpenClaw agent to add rewardkit from the ClawHub registry.

terminal
$openclaw skills install rewardkit

Requires ClawHub registry access. Review the security analysis below before installing.

Security Analysis

安全85/100

Open Source

Code is publicly available for audit.

Community Verified

Reviewed by the ClawHub community.

Community Reviews

Real user ratings only — separate from the editorial assessment and ClawHub signal.

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Frequently asked questions

Is rewardkit safe to install?

rewardkit has a SkillsReview security score of 85/100. It is open source and community-verified on ClawHub. Check the full Security Analysis on this page before installing.

How much does rewardkit cost?

rewardkit is free to install for OpenClaw via ClawHub.

What are the best alternatives to rewardkit?

You can compare rewardkit side by side with similar OpenClaw skills on the SkillsReview comparison page to find the best fit for your workflow.

How do I install rewardkit?

Install rewardkit from ClawHub at clawhub.ai/skills/rewardkit, or use the install action on this page to copy the command for your OpenClaw agent.

Community Signal

ClawHub Community Score6110.00 / 5.00
Installs143,890,500
Last UpdateSep 16, 2026
Actively maintained (0d ago)
ClawHub community score is a third-party marketplace signal. It is shown separately from SkillsReview editorial assessment and real user review averages.
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