An interactive learning and assessment system that transforms product data and store policies into gamified training sessions for retail employees.
The fastest way to install a skill directly from the registry.
npx clawhub@latest install training-quiz
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
Copy this prompt to OpenClaw to install it automatically.
Help me install training-quiz using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Get the raw skill files in a ZIP archive.
The Training Quiz skill is a powerful tool within the Openclaw Skills ecosystem designed to bridge the gap between static documentation and active employee knowledge. By leveraging existing product catalogs, policy entries, and FAQs, it automatically generates dynamic learning content. This eliminates the need for manual quiz creation, allowing teams to focus on growth while the AI handles the educational heavy lifting.
This skill turns the product knowledge base into an interactive learning system for staff. It adapts difficulty, tracks progress, and celebrates improvement, making training feel less like a chore and more like a competitive game. Whether onboarding new hires or refreshing veteran staff on new arrivals, Training Quiz ensures information retention through varied testing modes and real-time feedback.
To deploy the Training Quiz skill, ensure your knowledge base is populated with the required JSON arrays.
products[], policy_entries[], and faqs[] are updated in your knowledge source.python scripts/generate_questions.py --source knowledge_base.json
The skill organizes training data and employee progress using a structured metadata taxonomy. Progress is tracked per employee ID to ensure personalized learning paths.
| Field | Type | Description |
|---|---|---|
staff_id |
String | Unique identifier for the employee |
sessions |
Array | Log of individual quiz attempts including date, mode, and score |
weak_categories |
List | Topics (e.g., pricing, policy) where accuracy is below threshold |
cumulative_accuracy |
Number | Overall percentage across all sessions |
badges |
Array | Achievement tokens like "7day_streak" or "policy_master" |
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