H

harness

VERIFIED

by community

No community reviews yet
144,126,000installs
Updated Aug 2026

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Description

AI harness for stable LLM workflows. Topics — pipeline (clarify → ground → plan → generate → verify, dispatches to code-workflow) [pipeline.md], guardrails (denylist + scope + conditional-reject, self-contained for openclaw headless) [guardrails.md], recovery (fail-analyze → adapt → fallback, self-contained) [recovery.md]. Use when enforcing stable AI agent workflows, applying guardrails to autonomous execution, or recovering from verification failures. "harness", "AI harness", "pipeline guardrails", "fail recovery", "workflow stability", "agent harness" triggers

Install harness

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

terminal
$openclaw skills install harness

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 harness safe to install?

harness 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 harness cost?

harness is free to install for OpenClaw via ClawHub.

What are the best alternatives to harness?

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

How do I install harness?

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

Community Signal

ClawHub Community Score6120.00 / 5.00
Installs144,126,000
Last UpdateAug 6, 2026
Actively maintained (3d 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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