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name: backend-protocol description: "Inspect and use Nesa backend protocol structs, model registry handlers, prompt construction, and safe encrypted LLM request previews." metadata: disco-role: operating disable-model-invocation: true license: NO_LICENSE

Backend Protocol

Use this sub-skill when the user asks how Nesa builds encrypted LLM requests, validates sampling parameters, selects model handlers, sanitizes tokens, or streams responses from the remote service.

Typical triggers:

• "what fields are in LLMInference?"
• "why does LLMParams reject my sampling args?"
• "how is the Nesa model registry keyed?"
• "preview the encrypted Llama request without calling the endpoint"
• "debug remote stream response parsing"

Safe workflow

1. Read references/api-reference.md for structs, defaults, validation, and handlers.
2. Use scripts/inspect_protocol_defaults.py to confirm local package behavior in the current environment.
3. Build request previews with scripts/build_llm_request_preview.py before contacting any remote service.
4. Contact the stream endpoint only after the user approves a network/service call and provides required model/tokenizer context.
5. If a field or registry key fails, read references/troubleshooting.md.

Core protocol facts

• Messages are role/content pairs.
• Roles include assistant, user, ai, and system.
• LLMParams validates sampling ranges and normalizes stop_token_ids.
• Low/zero temperature forces greedy sampling behavior and constrains related parameters.
• SessionID includes an ee boolean to mark encrypted sessions.
• LLMInference contains stream flag, correlation id, messages, model id, optional model params, and optional session.
• Response chunks carry choices and delta content; delta content may be a string or an integer token.
• The remote encrypted LLM path tokenizes locally and sends token IDs as message content to the configured stream endpoint.

Model registry facts

The registry checks model-specific keys first, then task-type keys. Source evidence includes handlers for encrypted DistilBERT local classification and an encrypted Llama remote streaming path. If a user provides a model name with /, check whether the web UI normalizes it to an underscore form before registry lookup.

References and scripts

• references/api-reference.md: structs, methods, signatures, defaults, and validation behavior.
• references/request-flow.md: local tokenizer, prompt-template, request preview, and SSE streaming flow.
• references/troubleshooting.md: parameter, registry, dependency, endpoint, and response parsing failures.
• scripts/inspect_protocol_defaults.py: safe import/default/validation check.
• scripts/build_llm_request_preview.py: self-contained JSON payload preview with no network call.

Boundaries

• Do not use this sub-skill for one-click installation or UI launch; route to web-ui-runtime.
• Do not use this sub-skill for local sentiment output interpretation; route to encrypted-distilbert.
• Do not treat request previews as proof the Nesa remote stream endpoint is reachable.