ruvllm_microlora_adapt
Adapt MicroLoRA weights with quality feedback. Use when sending every prompt to the Anthropic API is wrong because you need local inference — air-gapped environments, MicroLoRA-fine-tuned per-task adapters, or sub-cent per-call cost. For general Claude work native Task is the right call.
This record as markdown: /tools/io-github-ruvnet-claude-flow/ruvllm-microlora-adapt.md
What ruvllm_microlora_adapt does on Claude Flow
AI agents invoke ruvllm_microlora_adapt to trigger actions in Claude Flow. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call: builds kicked off, notifications sent, workflows started.
Why ruvllm_microlora_adapt is rated High
This tool modifies machine learning model weights (MicroLoRA adaptation) based on feedback, which constitutes a stateful execution operation that alters model behavior. It triggers a training/fine-tuning process rather than simply reading or writing static data.
From the tool's definition Adapt MicroLoRA weights with quality feedback... MicroLoRA-fine-tuned per-task adapters... local inference
Risk signalsBulk/mass operation — affects multiple targets
Attacks that exploit this kind of access
The rule that runs ruvllm_microlora_adapt safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Claude Flow, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For ruvllm_microlora_adapt, this is the rule to start with:
ruvllm_microlora_adapt stays usable, but rate-capped: a runaway agent can't fire it dozens of times a minute. Everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect Claude Flow, apply this rule, and every ruvllm_microlora_adapt call is checked against it from then on.
Questions about ruvllm_microlora_adapt
Adapt MicroLoRA weights with quality feedback. Use when sending every prompt to the Anthropic API is wrong because you need local inference — air-gapped environments, MicroLoRA-fine-tuned per-task adapters, or sub-cent per-call cost. For general Claude work native Task is the right call. It is categorised as a Execute tool in the Claude Flow MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Claude Flow MCP server in PolicyLayer and add a rule for ruvllm_microlora_adapt: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Claude Flow. Nothing to install.
ruvllm_microlora_adapt is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
Yes. Add a rate_limit block to the ruvllm_microlora_adapt rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the PolicyLayer policy for ruvllm_microlora_adapt. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
ruvllm_microlora_adapt is provided by the Claude Flow MCP server (claude-flow). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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