An intelligent workflow tool that transforms bug descriptions into verified synthetic training data for AI agents.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install bug-data-generator
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-data-generator 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 Data Generator?
The Bug Data Generator is a specialized utility designed to bridge the gap between identifying an AI logic error and generating the corrective training data required for fine-tuning. By analyzing bug descriptions and root causes, this skill automatically proposes instruction sequences that reflect the intended behavior. It provides a structured bridge for developers working with Openclaw Skills to refine agent performance through high-fidelity synthetic datasets.
This skill eliminates the manual labor of hand-crafting JSONL entries by providing an interactive review process. Users can iterate on generated instructions, ensuring the training data perfectly matches the target scenario before the final output is produced in the latest v2.0.0 JSONL format.
Bug Data Generator Use Cases
- Creating corrective training datasets when an agent calls the wrong tool.
- Generating bulk instruction sets for smart home scene generation testing.
- Automating the transition from bug report to synthetic training sample.
- Validating instruction logic before committing to large-scale data generation within the Openclaw Skills ecosystem.
How Bug Data Generator Works
- The user inputs a detailed bug description and the root cause of the failure.
- The skill analyzes the input to determine the correct tool and generates a candidate list of user instructions.
- The agent presents the instruction list to the user for review, modification, or expansion.
- Once the user confirms the list, the skill invokes the data-generator to produce a final JSONL file.
- The resulting file follows a strict schema optimized for training next-generation AI agents.
Bug Data Generator Setup
To begin using this skill within your Openclaw Skills environment, ensure you have the core generator tools accessible. No complex installation is required beyond providing the bug context:
# Example of triggering the analysis phase
# Input: "The user asked to turn on the AC in 5 minutes, but the system executed immediately."
# Input: "Reason: Called dev_control instead of scene_generator."
Bug Data Generator Data Schema & Taxonomy
The skill generates a complex JSONL structure designed for instruction tuning. Below is the metadata taxonomy used for the output:
| Component | Type | Description |
|---|---|---|
| conversations | Array | Contains the human-assistant-observation-assistant flow. |
| current_user_instruction | Tag | Embedded within the human message for specific intent identification. |
| tool_call | JSON | The exact parameters sent to the target tool without filler text. |
| tool_response | XML Tag | The raw output returned by the system for the agent to process. |
| system / history | Fields | Kept empty to maintain compatibility with modern fine-tuning pipelines. |
Bug Data Generator Advanced Features
- Multi-turn iteration allowing users to add, delete, or modify specific instructions before finalization.
- Support for the v2.0.0 JSONL format including advanced context tags like current_time and local_device.
- Template-based generation for diverse scenarios including delays, timers, and recurring tasks.
- Decoupled logic that ensures compatibility even when the underlying data-generator undergoes updates.
- Seamless integration for developers building robust Openclaw Skills for complex automation tasks.
SKILL.md
Loading
Related Openclaw Skills
Security Audit (seguranca-auditoria)
A specialized security auditing utility designed to scan and verify Openclaw Skills for malicious code, prompt injections, and data exfiltration risks.
Content Security Filter
A high-performance security layer designed to detect prompt injection, malware patterns, and adversarial attacks in external content.
Amm CLI Analyzer
A specialized command-line utility for analyzing Automated Market Maker mechanisms and protocol security.
Sansheng Liubu Multi-Agent Skill Map
A sophisticated governance-to-execution framework that maps the classical Three Provinces and Six Ministries organizational model to modern multi-agent AI systems.
Feishu Messaging & Documents
An AI agent skill designed to automate Feishu messaging, group management, and document workflows via the Lark Open API.
Cex Analyzer
A specialized CLI skill designed to analyze Cex operations, evaluate protocol security, and manage on-chain conceptual data.
Featured*
