A specialized intent alias for VeriClaw that enables robust error recovery, incident response, and evidence-based verification for AI agents.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install verification-workflow-vericlaw
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 verification-workflow-vericlaw 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 Verification Workflow (VeriClaw)?
The Verification Workflow skill serves as a critical bridge within the Openclaw Skills ecosystem, specifically designed to handle the phase after an AI model claims a task is finished. It focuses on ensuring technical truth through evidence-first intervention and rigorous verification before completion. Instead of simply monitoring, this skill facilitates a deep correction loop that moves from initial failure through diagnosis and final proof of resolution.
Acting as an intent alias for the Official VeriClaw tool, this skill is essential for developers and operators who need to manage AI agent behavior in high-stakes environments. It transforms generic workflow commands into structured recovery sequences, making it easier to supervise complex agent interactions and maintain high reliability across all Openclaw Skills.
Verification Workflow (VeriClaw) Use Cases
- Executing a structured correction loop when an AI agent produces an incorrect output.
- Managing incident response protocols for AI systems that deviate from prescribed logic.
- Implementing verification before completion to ensure all project requirements are met before closing a task.
- Performing post-failure verification to confirm that a manual or automated fix has actually held.
- Providing agent supervision for autonomous tasks that require evidence-based proof of work.
How Verification Workflow (VeriClaw) Works
- The user or system triggers a verification workflow or incident response intent within the agent environment.
- The skill routes the request to the canonical VeriClaw engine to begin evidence collection.
- The workflow moves through a diagnosis phase where the root cause of the AI error or deviation is identified.
- An intervention is staged to apply the necessary corrections or adjustments to the agent's path.
- A final verification step confirms the fix held by matching current results against the required evidence.
- The entire sequence is recorded for casebook learning, allowing Openclaw Skills to improve over time through historical data.
Verification Workflow (VeriClaw) Setup
To utilize this workflow, you must first install the main VeriClaw package which powers the underlying logic for Openclaw Skills supervision.
clawhub install vericlaw
After installation, the agent will automatically recognize intents such as workflow repair, error recovery, and evidence-first intervention. For users on macOS, the native companion app provides an enhanced interface for managing these verification loops.
Verification Workflow (VeriClaw) Data Schema & Taxonomy
The skill organizes workflow data into a structured taxonomy to ensure auditability and clarity during the recovery process:
| Data Layer | Purpose |
|---|---|
| Evidence | Raw logs, screenshots, or terminal outputs providing proof of state. |
| Diagnosis | AI or human-annotated analysis of why the workflow failed. |
| Intervention | Specific actions or code changes applied to fix the issue. |
| Verification | Final proof-of-work indicators confirming the correction is successful. |
| Casebook | Metadata and taxonomy used to train or inform future agent responses. |
Verification Workflow (VeriClaw) Advanced Features
- Support for multi-stage correction loops that prevent agents from closing tasks prematurely.
- Native integration with the VeriClaw Apple-native companion for seamless desktop-based supervision.
- Evidence-first intervention logic that requires technical proof before allowing a workflow to proceed.
- Mapping of AI error recovery signatures back to canonical Openclaw Skills documentation for easier troubleshooting.
- Customizable routing rules for discovery traffic and incident response triggers.
SKILL.md
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