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Install in OpenClaw
/install radxa-docs
Description
Use when one Radxa skill should cover the full offline documentation workflow: detect the current board model, map it to the correct product series, deploy o...
Usage Guidance
This skill mostly does what it claims (detect board, map to Radxa series, and query local docs). However, its documented 'deploy' and 'update' steps will clone and execute third-party repositories (MDMaker from GitHub and a GitCode fallback). Before allowing deployment or automatic runs, manually inspect the referenced repositories (https://github.com/ZIFENG278/MDMaker.git and the radxa-docs repo) and the build/update Python scripts for unexpected behavior. If you must run the deploy flow, prefer running it interactively in a sandboxed environment or VM, pin commits/tags instead of cloning HEAD, and avoid enabling any autonomous or always-on behavior that would let the agent perform network pulls and execute code without explicit approval.
Capability Analysis
Type: OpenClaw Skill
Name: radxa-docs
Version: 1.0.1
The skill bundle automates the deployment of a documentation management tool by cloning a repository from a personal GitHub account (https://github.com/ZIFENG278/MDMaker.git) and executing downloaded Python scripts (build_md_docs.py) on the host. While these actions are aligned with the stated purpose of managing Radxa documentation, the execution of unverified remote code from a non-official source introduces a significant supply chain risk. The hardware detection script (scripts/detect_device.sh) and device mapping files (references/device-series-map.md) appear benign and function as described.
Capability Assessment
Purpose & Capability
The name/description (detect board, map to series, mirror and query offline Radxa docs) matches the provided assets: a device-detection script, a device-series map, and an SKILL.md describing how to mirror and query docs. Required capabilities (local files, git operations when deploying) are consistent with the purpose.
Instruction Scope
SKILL.md stays within the declared purpose (detect device, map to series, check local mirror, deploy/update on user request, query docs). However, the deployment steps instruct cloning external repositories and running Python build/update scripts — actions that reach outside the stated local-docs scope and will execute code pulled from the network. The skill does not instruct reading unrelated system files beyond normal device detection paths.
Install Mechanism
There is no install spec (instruction-only), but the documented deploy flow clones 'https://github.com/ZIFENG278/MDMaker.git' and runs its Python scripts in a venv, with a fallback to a domestic mirror (gitcode.com). Cloning and executing scripts from external repos (and switching remotes if GitHub fails) means arbitrary code will be pulled and run. This is proportionate to a 'deploy docs' action but is higher-risk: the sources are third-party and not pinned or verified, and the fallback host is a different trust domain.
Credentials
The skill requests no environment variables, no credentials, and no privileged config paths. The device-detection script only reads standard system files (/proc, /sys, hostname) appropriate to detecting hardware. There are no unexplained secret or credential requests.
Persistence & Privilege
always:false and no special privileges are requested. The skill will create and use a per-user directory (~/.openclaw/MDMaker) and a Python venv when the user asks to deploy, which is a reasonable local footprint. The agent-autonomy flag is default (disable-model-invocation:false) — this is normal but means the agent could run the deploy steps autonomously unless callers limit it; consider requiring explicit user consent before network operations and code execution.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install radxa-docs - After installation, invoke the skill by name or use
/radxa-docs - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.1
radxa-local-docs 1.0.1
- Added device detection script (`scripts/detect_device.sh`) to reliably determine the current hardware model using multiple methods.
- Added Radxa device/model-to-series mapping reference (`references/device-series-map.md`) for precise board identification and offline doc routing.
- Bundled OpenAI agent config (`agents/openai.yaml`).
- Now combines device detection, model mapping, local documentation mirror deployment/update, and board-specific offline doc query into a single workflow.
- All model-to-series logic and local doc mirror procedures are maintained locally for robust offline support.
v1.0.0
radxa-docs 1.0.0
- Initial release of the radxa-doc skill for querying locally deployed Radxa hardware product documentation.
- Supports structured directory navigation for single board computers, compute modules, and network devices.
- Provides detailed usage scenarios, directory mapping, document naming conventions, and hardware-to-series matching for efficient documentation lookup.
- Includes shell commands and workflow for indexing and reading documentation files.
- Full documentation covers Chinese and English, multiple product series, and typical troubleshooting/coding use cases.
Metadata
Frequently Asked Questions
What is radxa docs?
Use when one Radxa skill should cover the full offline documentation workflow: detect the current board model, map it to the correct product series, deploy o... It is an AI Agent Skill for Claude Code / OpenClaw, with 100 downloads so far.
How do I install radxa docs?
Run "/install radxa-docs" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is radxa docs free?
Yes, radxa docs is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does radxa docs support?
radxa docs is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created radxa docs?
It is built and maintained by xzl01 (@xzl01); the current version is v1.0.1.
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