A comprehensive zero-regression workflow for triaging, reproducing, and fixing bugs within OpenClaw environments.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install bug-fixing
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 bug-fixing 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 Bug Fixing OpenClaw Skill?
This skill provides a rigorous, 7-phase methodology designed for AI coding agents to handle everything from trivial typos to complex, cross-module regressions. By implementing the Openclaw Skills standard for debugging, it ensures every fix passes strict validation gates, including root cause analysis and side-effect prediction.
The framework is specifically tuned for the OpenClaw architecture, addressing common pitfalls in FastAPI, React, and LangChain integrations while maintaining a strict zero-regression policy. It forces agents to move beyond symptom-level fixes by requiring mechanistic proof of root causes and runtime verification of deployment.
Bug Fixing OpenClaw Skill Use Cases
- Resolving broken features or incorrect behavior in full-stack applications.
- Debugging intermittent console errors and performance degradations.
- Fixing UI bugs that require runtime evidence and DOM inspection.
- Managing complex database migrations and schema drift issues.
- Systematic investigation of high-risk backend zones like tool call parsing and agent executors.
How Bug Fixing OpenClaw Skill Works
- Triage the issue to determine severity (P0-P3) and tier (Trivial to Complex) to control workflow depth.
- Gather required evidence such as logs, screenshots, and stack traces to reproduce the bug.
- Perform Root Cause Analysis using a hypothesis ladder and the 5 Whys technique to identify the mechanistic cause.
- Trace the impact chain across code, data, time, and events to prevent regressions in Openclaw Skills deployments.
- Implement a minimal fix, ensuring it addresses the root layer rather than symptoms.
- Verify the fix through regression testing and runtime deployment checks, including clearing bytecode caches.
- Update the knowledge library and perform a self-reflection to improve future debugging accuracy.
Bug Fixing OpenClaw Skill Setup
To use this within your environment, ensure the Openclaw Skills directory structure is present. Initialize your reference files to track historical data:
mkdir -p references
touch references/bug-records.md references/bug-patterns.md references/blind-spots.md
Ensure your agent has access to grep, glob, and shell execution tools to perform deep-code scanning and regression testing.
Bug Fixing OpenClaw Skill Data Schema & Taxonomy
The skill manages project history and debugging intelligence through specific markdown files and metadata:
| File | Purpose |
|---|---|
| references/bug-records.md | Logs the history of all project-specific fixes and regressions. |
| references/blind-spots.md | Single source of truth for AI blind spot registry and project traps. |
| references/bug-patterns.md | Universal library of categorized bug patterns and strategies. |
| references/system-rca.md | Detailed guides for cross-layer and multi-process debugging. |
| references/regression-matrix.md | Defines the verification criteria for zero-regression fixes. |
Bug Fixing OpenClaw Skill Advanced Features
- Severity-adaptive workflow depth that scales from quick fixes for typos to full RCA for critical outages.
- Automated knowledge file initialization that builds a project-specific memory of bug patterns.
- UI Bug Protocol requiring DOM inspection and screenshot evidence before proposing code changes.
- Cross-path verification for dual implementations, ensuring fixes are applied to both direct executors and graph-based nodes.
- Self-reflection scoring system (1-5) to measure first-time correctness and minimal change efficiency.
SKILL.md
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