A multi-layered validation framework that protects code integrity by enforcing strict mechanical quality gates on AI-generated improvements.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install auto-improvement-gate
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 auto-improvement-gate 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 Improvement Gate?
Improvement Gate is a robust quality control system designed for AI coding agents to ensure every modification meets high-quality standards before finalization. By implementing a sequential 7-layer mechanical gate, it evaluates everything from basic schema structure and syntax compilation to advanced regression analysis and human review requirements. This skill is a foundational part of the Openclaw Skills ecosystem, providing a safety net that prevents low-quality or high-risk code from being merged into a project.
The system operates on a fail-fast principle: if any required layer fails, the gate immediately rejects the change or triggers an automated revert of file modifications. This ensures that the agent remains within safe operational boundaries, making it an essential tool for developers who require high-confidence automation.
Improvement Gate Use Cases
- Validating whether an executed candidate change should be retained or reverted.
- Managing an asynchronous human-in-the-loop approval queue for high-risk modifications.
- Automated linting and compilation checks for AI-generated code snippets.
- Enforcing multi-agent consensus through integrated review gates.
How Improvement Gate Works
- The orchestrator triggers the gate.py script, passing in ranking and execution artifacts.
- The system sequentially executes up to 7 layers of checks, starting with SchemaGate to ensure data integrity.
- Technical checks like CompileGate and LintGate verify that the code is syntactically correct and follows style guidelines.
- Analytical checks like RegressionGate and ReviewGate compare the change against historical performance and multi-agent verdicts.
- The DoubtGate performs a linguistic analysis to identify high-risk hedging language in the generated output.
- Based on the cumulative results and risk levels, the skill issues a final decision: keep, revert, reject, or pending_promote for human review.
Improvement Gate Setup
To integrate this component into your Openclaw Skills workflow, ensure your environment is configured to point to your state-root. Use the following CLI commands to run the gate validation:
python3 scripts/gate.py \
--ranking ranking.json \
--execution execution.json \
--state-root /path/to/state
To view and manage the human review queue, use the review script:
# List all pending reviews
python3 scripts/review.py --state-root /path/to/state --list
# Approve a specific candidate
python3 scripts/review.py --state-root /path/to/state --complete [CANDIDATE_ID] --decision approve
Improvement Gate Data Schema & Taxonomy
The Improvement Gate generates a detailed Gate Receipt in JSON format to document the decision logic.
| Attribute | Type | Description |
|---|---|---|
| decision | string | The outcome: keep, revert, pending_promote, or reject |
| gate_layers | object | Boolean results and logs for each of the 7 validation layers |
| next_step | string | The recommended next action for the orchestrator (e.g., propose_candidates) |
| rollback | object | Details on whether a file restoration was attempted due to failure |
Improvement Gate Advanced Features
- Sequential Layer Execution: Automatically stops at the first required-layer failure to save compute and prevent further errors.
- Risk-Aware Escalation: Automatically flags candidates as pending_promote if they involve high-risk categories like prompt engineering or core code changes.
- Flexible Layer Filtering: Users can run a subset of gates using the --layers flag for faster development cycles.
- Integrated Rollback Logic: Automatically restores file backups via the rollback_pointer if a critical check fails after execution.
SKILL.md
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