“AMD Ross” is not identified in the AMD documentation cited here, so the right fix depends on what you mean: an AMD agent skill that is missing, an OpenHands/Agent Canvas connection to Lemonade, or a context-limit error. First identify the agent and model-server setup; then follow only the matching troubleshooting path below. These checks are specific to the documented AMD Skills and OpenHands/Lemonade setups, not universal fixes for every AMD or ROCm tool.
First identify which part is failing
Tool access can fail at different layers: the agent may not discover an installed skill, an agent executable may not be available, a container backend may be unhealthy, or the model endpoint may be unreachable. A context-limit error is different again, and incomplete output is not necessarily a connection failure.
- Skill missing or not invoked: check the agent, installation location, and skill description.
- OpenHands or Agent Canvas cannot connect: check the executable, Docker/backend, endpoint, and Lemonade server in that order.
- Context or token limit: reduce conversation history first; adjust the documented Lemonade setting only if needed.
- Output is poor or incomplete: narrow the task or choose a larger model, then inspect the agent’s changes and command output.
AMD’s Skills overview describes interoperability with Cursor, Claude Code, OpenAI Codex, and Gemini CLI. It also says the catalog is evolving and some entries may be tech preview. Check the status of the particular skill and product rather than assuming every listed item is a stable release.
If the agent cannot see or invoke an AMD skill
AMD defines a skill as a self-contained folder of instructions and supporting materials. A SKILL.md file contains frontmatter, including a name and short description. The description helps the agent decide when the skill applies; the full instructions load when the agent determines they are relevant or when the user explicitly invokes the skill. See AMD’s skill concept documentation.
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Install it for the agent you actually use
The AMD skills repository documents an installer using the skills CLI. To inspect available choices, run:
npx skills add amd/skills --list
To select a particular skill and agent, use the CLI’s --skill and --agent options:
npx skills add amd/skills --skill <skill-name> --agent <agent-name>
Use a skill name and agent value shown by the CLI rather than guessing them. The repository’s general installation command is npx skills add amd/skills.
Check the destination and folder contents
If you installed the skill manually, verify that it is in the directory expected by your agent. AMD’s repository gives these examples:
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| Agent | Personal or project skill directory listed by AMD |
|---|---|
| Cursor | ~/.cursor/skills/ or .cursor/skills/ |
| Claude Code | ~/.claude/skills/ |
| Codex | $HOME/.agents/skills or $REPO_ROOT/.agents/skills |
These are repository-documented examples; directories can differ with agent version or configuration. Confirm the skill folder contains SKILL.md and that its frontmatter includes a useful name and description.
Check whether the task matches the skill description
A skill can be present but not activate if its description does not match the user’s task. Try invoking the skill by name in plain language if your agent supports that behavior. If it still does not appear, record the agent and version, skill name, and installation directory. AMD’s cited sources do not establish a universal skill-discovery command or debug log for all supported agents.
If OpenHands or Agent Canvas cannot reach Lemonade
Use this section only if you are following AMD’s OpenHands and local LLM playbook. It describes a particular Agent Canvas/OpenHands setup with Lemonade; its endpoint and container advice should not be assumed to apply to other agents or model servers.
1. Check the Agent Canvas executable
If the terminal says agent-canvas cannot be found, reinstall the Agent Canvas npm package and verify that npm’s global binary directory is on your PATH. Open a new terminal after changing the path. If a global npm installation fails because of permissions, AMD advises configuring a user-owned global directory.
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2. Check Docker and backend health
For the containerized setup, start Docker Desktop and run:
docker version
Confirm the output contains both Client and Server sections. If the container starts but the backend remains unhealthy, allow time for initial startup and inspect its logs with:
docker logs <container>
If the UI loads while the backend reports unhealthy, wait for the agent server to initialize, refresh, and inspect logs. AMD’s playbook suggests restarting the stack if it remains unhealthy.
3. Use the container’s route to the Windows host
In the Windows-hosted Lemonade setup, a container must not use 127.0.0.1 to reach the Windows host: inside the container, loopback refers to the container itself. AMD’s playbook specifies this model endpoint instead:
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http://host.docker.internal:13305/api/v1
Use that address in the documented container configuration. It is not a general endpoint for other operating systems, network layouts, or Lemonade installations.
4. Verify Lemonade is serving
On the host, check the server status:
lemonade status
AMD’s playbook also gives this host-side health check:
curl -fsS "http://127.0.0.1:13305/api/v1/health"
If Lemonade is not healthy on the host, resolve that before troubleshooting the agent’s endpoint configuration. A working host health check does not by itself prove that a container is using the correct host address.
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For the Lemonade configuration in AMD’s playbook, start a fresh conversation first. This removes older conversation history from the context without changing the server’s configured limit.
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If the error persists, the playbook suggests restarting Lemonade with a larger ctx_size. It identifies 65536 as the default in that setup and gives 131072 as an example larger value, subject to available memory. These are playbook-specific settings, not universal context limits or recommended values for every model and server. Increasing the context size may require more memory; use a value supported by your model and hardware.
If the agent’s output is incomplete or low quality
An agent that responds but produces weak or partial work may have a task-scoping or model-capability problem rather than a tool-connection failure. AMD’s playbook suggests either switching to a larger model in Lemonade or giving the agent a smaller, more concrete task. Then inspect the changed files and command output; if the result is wrong, guide the agent with a specific follow-up. These steps can help diagnose or improve the result, but they do not guarantee a fix.
Check whether a named AMD skill is available
The AMD skills catalog is built in the open and may change. Before relying on a skill-specific fix, check its current repository entry and status. For example, the AMD skills repository currently describes rocm-doctor as planned; it should not be treated as an available released troubleshooting tool unless the repository’s status changes.
If none of these branches matches your setup, “AMD Ross” alone is not enough to identify the product or error. Establish whether the intended tool is AMD Skills, ROCm, or a named agent integration, along with the agent, version, and model server, before applying product-specific commands.
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