An active learning agent skill that forces users to bypass analysis paralysis, build a raw prototype immediately, and iteratively refine it through guided self-correction.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install learn-prototype
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 learn-prototype 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 learn-prototype?
The learn-prototype module is a specialized behavioral framework designed for Openclaw Skills to combat the common 'heavy input, light output' learning trap. Instead of allowing you to delay implementation under the guise of preparation, this skill acts as an uncompromising coach that pushes you to build a minimum viable, even highly flawed, prototype immediately. By adopting an incremental reformist mindset, the agent guides you to discover flaws, propose hypotheses, test them, and systematically upgrade your work.
At its core, this approach operates on the belief that diagnosing weaknesses yourself is far more powerful than receiving pre-packaged answers. The agent does not build the solution for you; rather, it provides a dialectic structure that forces you to think, ask critical questions, and codify your practical discoveries into reusable, generalized methodologies.
learn-prototype Use Cases
- When starting a brand-new technical project or script and feeling overwhelmed by preparation anxiety.
- When researching a complex system and needing to pivot from reading documentation to practical application.
- When optimizing an existing, subpar project or codebase but lacking a structured direction.
- When trying to break out of passive video-watching or reading habits to achieve deep skill retention.
- When seeking to build personalized, repeatable mental models through systematic trial and error.
How learn-prototype Works
- Deploy a Raw Prototype: The agent prompts you to build the simplest, minimal, 'worst-working' version of your target output to bypass perfectionism.
- Guide Self-Diagnosis: Instead of giving solutions, the agent asks targeted questions like 'Where does this fail?' or 'Why is this suboptimal?' forcing you to articulate the flaws.
- Propose and Test Hypotheses: You propose a specific strategy to address one identified flaw, implement it, and verify the outcome to learn from both success and failure.
- Iterate and Refine: You run through continuous feedback loops of identification and testing until the prototype cannot be further optimized or requires a complete structural pivot.
- Consolidate Methodology: The agent helps you extract the underlying principles of how you transitioned from version A to version B, converting the problem-solving journey into a repeatable personal rulebook.
learn-prototype Setup
To register this active learning coach in your Openclaw Skills workspace, create a custom system instruction or skill file with the following setup:
# Create the custom skill directory in your system
mkdir -p ~/.openclaw/skills/learn-prototype
# Initialize the skill configuration file
cat <<EOF > ~/.openclaw/skills/learn-prototype/config.yaml
name: learn-prototype
trigger_keywords:
- "do X"
- "research X"
- "improve X"
- "optimize X"
- "stuck on X"
coaching_mode: strict_non_agent_completion
EOF
Ensure that your LLM system prompt is updated to reference this skill file whenever active-learning or project-building scenarios are detected.
learn-prototype Data Schema & Taxonomy
The learn-prototype skill structures feedback loops and methodology logging using a systematic schema to track improvement iterations:
| Phase | Data Managed | Description |
|---|---|---|
| v0 Prototype | raw_prototype |
The initial, minimal viable output constructed to get the workflow running. |
| Analysis | user_flaw_insights |
A structured log of deficits, bugs, or subpar metrics identified directly by the user. |
| Hypothesis | improvement_hypothesis |
The experimental strategy selected to address the diagnostic logs. |
| Validation | test_results |
Verification outcomes detailing whether the hypothesis succeeded or failed. |
| Methodology | generalized_rules |
Actionable rules extracted from the iteration, added to your persistent learning repository. |
learn-prototype Advanced Features
- Guided Non-Intervention Enforcement: Programmatic constraints prevent the AI agent from completing your tasks, keeping the cognitive load on you for genuine skill acquisition.
- Strict Prerequisite Verification: The agent strictly operates only on knowledge you have explicitly confirmed you understand, eliminating guessing or unearned assumptions.
- Dynamic Skill Handoff: If you lack foundational prerequisites, the agent dynamically handsoff to the
learn-graphskill; to verify deep comprehension, it pivots to thelearn-feynmanskill. - Methodology Accumulation Engine: Automatically transforms debugging cycles into a structured, searchable markdown file containing generalized rules for future development.
SKILL.md
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