A professional framework for diagnosing business pain points, mapping workflows, and implementing tiered AI automation through a structured 6-week deployment lifecycle.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install client-onboarding-agent
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 client-onboarding-agent 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 Client Onboarding Agent?
The Client Onboarding Agent is a comprehensive diagnostic and deployment framework designed specifically for professional service providers and developers managing AI agent integrations. Rather than a simple sales tool, this skill serves as a rigorous technical discovery process that identifies operational bottlenecks, maps data flow, and establishes realistic automation boundaries. It ensures that every AI deployment is grounded in the technical and regulatory reality of the client's business environment.
By utilizing Openclaw Skills, developers can move away from vague project scopes and toward binary completion contracts. The framework emphasizes a shift from Day 1 template-driven interactions to a Week 6 deep-memory state, where agents operate with high autonomy and client-specific context. This methodology mitigates risk by categorizing tasks into tiers—ranging from unsupervised low-risk actions to human-in-the-loop restricted processes—ensuring safety and reliability in enterprise settings.
Client Onboarding Agent Use Cases
- Onboarding new high-ticket clients for custom AI agent deployments or managed services.
- Conducting deep-dive business audits to identify high-ROI automation opportunities.
- Transitioning manual, repetitive workflows into tiered autonomous processes.
- Establishing clear performance benchmarks and completion criteria for stakeholder alignment.
- Managing client expectations during the critical 6-week learning phase of an AI agent.
How Client Onboarding Agent Works
- Conduct a four-round diagnostic process covering pain points, workflow mapping, constraint identification, and solution design.
- Map existing data flows and identify handoff points between human operators and technical systems.
- Identify technical, financial, regulatory, organizational, data, and timeline constraints using a structured matrix.
- Prioritize automation tasks based on an Impact vs. Feasibility score.
- Assign every automation to a Risk Tier (Low, Medium, High, or Restricted) to define supervision requirements.
- Deploy agents using a staggered model approach to optimize costs while maintaining high reasoning capabilities.
Client Onboarding Agent Setup
To integrate this framework into your Openclaw Skills workflow, document your client sessions using the provided Markdown templates.
# Initialize the onboarding workspace
mkdir client-onboarding && cd client-onboarding
# Create the diagnostic artifacts
touch pain_point_map.md workflow_diagram.md constraint_matrix.md
Ensure you have defined your Model Staggering strategy in your environment configuration to manage API costs across Haiku, Sonnet, and Opus-class models.
Client Onboarding Agent Data Schema & Taxonomy
The skill organizes its output into four primary artifacts and a tiered supervision registry:
| Artifact | Contents | Purpose |
|---|---|---|
| Pain Point Map | Critical, Significant, and Chronic issues | Identifies the 'Why' behind the automation |
| Workflow Diagram | Trigger -> Step -> Decision -> Output | Maps the technical 'How' and logic flow |
| Constraint Matrix | 6-category risk assessment | Defines technical and regulatory boundaries |
| Deployment Plan | Phased 6-week roadmap | Outlines the execution and ROI timeline |
| Completion Contract | Binary done criteria and evidence | Eliminates scope creep and ensures delivery |
Client Onboarding Agent Advanced Features
- Staggered Model Logic: Automatically routes tasks to different model tiers (Fast vs. Strategic) to reduce API costs by up to 80%.
- Tiered Promotion System: Built-in protocols for promoting agents from 'Human-in-the-loop' to 'Autonomous' based on error-free performance windows.
- Completion Contracts: Formalized 'binary done' criteria that include observable evidence and staged approval workflows.
- 6-Week Narrative Framework: Integrated client communication templates to manage the transition from template-based to memory-based AI performance.
SKILL.md
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