A strategic framework for product teams to select the most efficient prototype type and fidelity level based on specific discovery risks.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install bookforge-discovery-prototype-selection
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 bookforge-discovery-prototype-selection 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 Discovery Prototype Selection?
The discovery-prototype-selection skill is a specialized module for Openclaw Skills designed to guide product managers, designers, and engineers through the critical decision-making process of prototyping. Based on the principles from Marty Cagan's INSPIRED, this skill ensures that teams don't waste time building high-fidelity models when a simple feasibility spike or a Wizard of Oz simulation would suffice.
By utilizing this skill within the Openclaw Skills ecosystem, teams can systematically map active risks—such as technical feasibility, user usability, or business viability—to the appropriate prototype artifact. It emphasizes the philosophy that prototypes are disposable discovery tools meant to foster shared understanding and deep thinking, rather than being early versions of production code.
Discovery Prototype Selection Use Cases
- Determining whether a feasibility spike, user prototype, or live-data experiment is required for a new feature.
- Choosing the correct fidelity level for usability testing to minimize design debt.
- Aligning stakeholders on a product vision using high-fidelity simulations.
- Validating AI/ML workflows using Wizard of Oz techniques before investing in model development.
- Preventing ambush estimation by providing engineers the space for discovery-led investigation.
How Discovery Prototype Selection Works
- The agent analyzes the risk assessment output to identify the primary risk (Feasibility, Usability, Value, or Viability).
- It selects the optimal prototype type (User, Feasibility, Live-data, or Hybrid) using a specialized decision matrix.
- The skill determines the required fidelity level based on the specific testing purpose (e.g., High-fidelity for qualitative value, Low-fidelity for workflow usability).
- It identifies the necessary creators (Designer vs. Engineer) and establishes the technical constraints for the build.
- The agent runs an anti-pattern check to ensure the prototype isn't being mistaken for a final product or a value validation tool.
- It generates a comprehensive Prototype Plan document to align the entire product team.
Discovery Prototype Selection Setup
To add this skill to your local environment, use the following command within the Openclaw Skills CLI:
clawhub install bookforge-discovery-prototype-selection
Ensure you have the prerequisite risk assessment skill installed for full workflow integration:
clawhub install bookforge-product-discovery-risk-assessment
Discovery Prototype Selection Data Schema & Taxonomy
The skill generates a structured Prototype Plan and organizes data as follows:
| Data Point | Description |
|---|---|
| Risk Profile | Identification of Feasibility, Usability, Value, or Viability focus. |
| Prototype Type | Selection of Feasibility, User, Live-Data, or Hybrid (Wizard of Oz). |
| Fidelity Level | Rated as Low, Medium, or High based on the testing goal. |
| Resource Mapping | Assignment of Engineer or Designer roles and estimated timeframes. |
| Validation Question | The specific, testable hypothesis the prototype is designed to answer. |
| Anti-Pattern Log | A checklist ensuring discovery code isn't treated as production delivery. |
Discovery Prototype Selection Advanced Features
- Hybrid Prototyping Support: Guidance for setting up Wizard of Oz tests for AI/ML features where humans simulate backend logic.
- Risk-to-Type Mapping: Automated logic that prevents mismatching risks (e.g., using a designer's mockup to test an algorithmic feasibility risk).
- Stakeholder Alignment Hooks: Specific protocols for using prototypes as communication artifacts rather than just testing tools.
- Integration with Openclaw Skills: Seamlessly transitions from risk assessment to prototype planning and subsequent value-testing techniques.
SKILL.md
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