The Iteration Pattern implements the Ralph loop to solve incomplete AI execution through continuous cycles of verification and optimization.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install iteration-pattern
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 iteration-pattern 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 Iteration Pattern?
The Iteration Pattern is a robust workflow methodology designed for Openclaw Skills that moves away from one-shot AI generation toward a reliable, multi-turn optimization process. It utilizes the Ralph loop (Execute → Check → Optimize) to ensure that the AI agent does not simply claim a task is finished, but actually meets specific, verifiable quality standards. This pattern is essential for complex technical tasks where accuracy and completeness are non-negotiable.
By defining strict completion standards and testing methods upfront, this skill guides the AI through a series of refinements. It is particularly effective for developers using Openclaw Skills to build other agents, write production-level code, or manage intricate data structures that require programmatic validation before delivery.
Iteration Pattern Use Cases
- Creating complex Openclaw Skills that require multi-step YAML and logic validation.
- Generating production-ready code that must pass specific CLI-based test suites.
- Developing technical documentation where cross-file consistency and syntax accuracy are critical.
- Scenarios where the AI agent frequently provides incomplete or superficial outputs on the first attempt.
How Iteration Pattern Works
- Define the specific task target and the final deliverables (e.g., file paths and formats).
- Establish a set of measurable completion standards using a checklist format.
- Map each standard to a specific testing method, such as a shell command or a script.
- Execute the initial version of the task or project.
- Conduct itemized testing to verify which standards are met and which failed.
- Optimize the output based on the failure reports and specific error feedback.
- Repeat the testing and optimization cycle until all criteria are satisfied or the iteration limit is reached.
- Generate a final completion report summarizing the iteration count and final status.
Iteration Pattern Setup
To implement this pattern within your Openclaw Skills environment, ensure you have the necessary validation tools installed and follow the structured requirement template.
# Standard check for YAML syntax validity
python -c "import yaml; yaml.safe_load(open('SKILL.md'))"
# Verify your skill is recognized by the system
skillhub list
# Check for non-empty fields in your configuration
grep -E "name:|description:" SKILL.md
Iteration Pattern Data Schema & Taxonomy
The Iteration Pattern organizes data using a structured requirement specification to track the lifecycle of a task.
| Component | Description | Data Format |
|---|---|---|
| Task Goal | The intended output and storage location | Markdown Header |
| Standards | List of verifiable quality requirements | Checklist (- [ ]) |
| Test Methods | Commands or logic used to verify each standard | Markdown Table |
| Iteration Log | Record of improvements and test results per round | Numbered List |
| Final Conclusion | Final status (Complete/Partial/Failed) | Summary Block |
Iteration Pattern Advanced Features
- Multi-stage validation hooks that allow the agent to run local scripts to verify code execution.
- Customizable iteration limits (recommended 3-5 rounds) to optimize token usage and time.
- Specialized templates for Skill Creation that automatically include YAML and structure checks for Openclaw Skills.
- Programmatic failure analysis that feeds specific error logs back into the optimization loop for faster convergence.
SKILL.md
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