A powerful AI-driven development runtime that automates planning, file operations, terminal commands, and bug-fixing with memory-enhanced hybrid search.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install super-ai-dev-runtime
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 super-ai-dev-runtime 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 AI Dev Runtime?
AI Dev Runtime is an advanced, memory-enhanced toolkit designed to boost software development efficiency. Operating as a pivotal entry in the catalog of Openclaw Skills, it integrates robust file operations (such as multi-file editing and patch application), codebase analysis, terminal command execution, and automated test running into a unified workflow. By combining these capabilities, the skill acts as a localized execution engine for autonomous AI coding agents.
At its core, AI Dev Runtime leverages a hybrid semantic-keyword search to accurately locate relevant files and code blocks. Furthermore, its adaptive learning memory tracks past solutions and codebase patterns over time. This makes it a highly capable agent runtime that streamlines debugging, feature implementation, and codebase analysis directly within your command-line or development IDE workspace.
AI Dev Runtime Use Cases
- Automated Bug Patching: Seamlessly analyze, patch, and verify code bugs in a single unified execution cycle using Openclaw Skills.
- Complex Codebase Analysis: Perform hybrid semantic and keyword searches to build deep structural understanding and explore call hierarchies.
- Batch Task Execution: Execute multi-step coding plans that span across multiple files and terminal test suites.
- Learning from Historical Fixes: Use adaptive historical memory to retrieve past bug resolutions (like login issues or server errors) and feed them back into active agent sessions.
How AI Dev Runtime Works
- Task Initialization: The agent triggers the
/ai-dev-runtime <task>slash command or a batch execution sequence via the API. - Context & Memory Retrieval: Prior to execution, the agent queries the memory storage for previous similar fixes to optimize accuracy.
- Semantic/Keyword Codebase Search: The runtime performs high-precision semantic and keyword analysis to pinpoint target files, reading code blocks and identifying call hierarchies.
- Adaptive Execution and Verification: The system edits the code (via single or multi-file operations/patches), executes terminal commands, runs the test suite, and applies fixes iteratively until successful.
AI Dev Runtime Setup
To leverage this and other Openclaw Skills, configure the host environment variables and ensure the runtime server is active:
- Set the runtime environment variables:
export AI_DEV_RUNTIME_URL="http://localhost:8000"
export AI_DEV_RUNTIME_API_KEY="your_optional_api_key"
Spin up the underlying AI Dev Runtime HTTP server on the designated host machine.
Register the command-tool within your Openclaw client config to enable execution.
AI Dev Runtime Data Schema & Taxonomy
The runtime manages and formats data payload exchanges using structured schemas. Below is an overview of the tools and parameters:
| Tool Name | Operation Type | Key Metadata / Payload |
|---|---|---|
ai_dev_runtime_command |
Slash Command | String task defining the high-level workflow goal. |
ai_dev_runtime_invoke |
Low-level Tool | Actions including read_file, search, edit, edit_multi, apply_patch, find_references, call_hierarchy, run_terminal, run_tests. |
ai_dev_runtime_run_task |
Batch Execution | Structured array of multi-step coding commands. |
ai_dev_runtime_fix_bug |
Bug-fix Pipeline | Integrates prior_memory, patch files, and verification scripts. |
ai_dev_runtime_analyze |
Semantic Search | Dual semantic-keyword queries alongside learning memory registers. |
AI Dev Runtime Advanced Features
- Hybrid Search Engine: Melds keyword precision with semantic embedding lookups to understand the structural context of legacy code bases.
- Adaptive Learning Memory: Allows developers to inject historical fix snippets (
prior_memory) into newly initiated agent cycles, bypassing repetitive debugging. - Multi-File Patching (edit_multi): Safely coordinates complex structural changes across multiple modules simultaneously to prevent intermediate compile breaks.
- Integrated Loop Verification: Automatically executes tests post-edit and feeds failures back into the loop to trigger automated self-correction sequences within Openclaw Skills.
SKILL.md
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