M5 Delegate

M5 Local Delegation

Use this skill when local inference is requested or an applicable delegation policy calls for a self-contained leaf. The goal is to save frontier tokens and produce useful real delegation data for the home-server project.

When To Use It

Once activated, suitable leaves include:

• Summarize, classify, extract, rewrite, draft.
• Short, bounded reasoning where an approximate answer is acceptable.
• Single-shot code generation with a clear spec.
• Batch harnesses that can run on the M5 box itself.

Avoid it:

• Work needing the full current repo or conversation context.
• Security-critical reasoning or tasks where a wrong answer is costly to detect.
• Multi-step code edits unless a caged code-loop style tool is explicitly available.
• Private content unless it will stay on a local-only path.

Model/Runtime Guidance

• Prefer a fast non-thinking model such as mellum for short classify/extract/summarize/simple-codegen.
• Use qwen3-coder-next-80b for harder coding or agentic-coding leaves.
• Use gemma4 for general or multimodal tasks when relevant.
• Discover available models and current tool schemas before choosing; the names above are examples, not an availability guarantee. Size output budgets to the task and detect truncated or empty results.
• Batch independent same-model calls when supported to avoid unnecessary model swaps; measure runtime rather than assuming fixed latency.

Access Paths

Prefer purpose-built MCP tools if they are available in the current agent session, such as list_models, ask, or code_loop_*.

Always pass the cloud delegator model when you know it:

• MCP ask: include delegator_model_id, for example "delegator_model_id": "openai/gpt-5.5".
• Direct /delegate: include delegatorModelId, for example "delegatorModelId": "openai/gpt-5.5".
• Homeserver CLI: pass --delegator <cloud-model-id>.

Use the actual frontier/conductor model for the current task, not a generic default. This is what lets the M5 ledger measure actual savings rather than only premium-baseline savings. If the cloud delegator is unknown, omit the field instead of guessing.

If writing a harness that talks to the gateway directly:

• Heavy/batch harnesses should run on the M5 box and call llama-swap on loopback: http://127.0.0.1:8091/v1. This needs no bearer token.
• For an authenticated laptop call, load the canonical bases with eval "$(m5-auth --env --tailnet)" (or omit --tailnet when the public gateway is required).
• Call gateway-root routes as "$M5_GATEWAY_URL/delegate" or "$M5_GATEWAY_URL/ledger". Call the OpenAI-compatible route as "$M5_OPENAI_BASE_URL/chat/completions".
• Never derive the gateway root by stripping /v1 from M5_BASE_URL. M5_BASE_URL remains a compatibility alias for M5_OPENAI_BASE_URL; it is not valid for /delegate or /ledger.
• Get the owner bearer token only through m5-auth and macOS Keychain. Never scrape MCP config and never write tokens to tracked or scratch files.

One-Shot Prompt Shape

The runtime sees only the prompt string. Make it self-contained:

1. One sentence of role/framing.
2. Clearly delimited input.
3. Exact requested output.
4. Constraints, schema, length cap, and things to avoid.

For JSON or structured output, specify the schema and ask for only that output.

Feedback Loop

When a delegated answer matters, capture whether it was useful:

• pass: used as-is or with trivial edits.
• partial: useful but needed edits.
• redo: wrong shape, retry might work.
• wrong: wrong runtime choice; escalate.

This feedback is part of the routing-evidence loop that the project exists to improve.