Local Model Onboarding Readiness 20260815 - Release Notes

What It Does

Local Model Onboarding Readiness helps teams decide whether a local model is ready to be introduced into an OpenClaw workflow. It checks redacted evidence for model identity, runtime version, hardware profile, memory and storage requirements, context limits, quantization choice, tool-calling checks, structured-output checks, latency samples, fallback plans, and rollback plans.

The workflow is read-only, local, and deterministic. It gives operators a clear PASS, REVIEW, BLOCKED, or ERROR signal before they spend time integrating a model that may not meet operational requirements.

Use Cases

• Operators evaluating local models before onboarding them into agent workflows.
• Engineering teams documenting compatibility, resource needs, fallback, and rollback evidence.
• Reviewers comparing candidate models without exposing machine details or private data.

Requirements

• Python 3 with standard-library modules only.
• Redacted onboarding notes supplied via --text or one explicit local notes file.
• No API keys required.
• OpenClaw workspace capable of running local skill scripts.

Example Usage

python3 scripts/local_model_onboarding_readiness.py --text "model identifier recorded; runtime version recorded; hardware profile documented; memory requirement measured; storage requirement measured; context limit known; quantization choice documented; tool calling check passed; structured output check passed; latency sample captured; fallback plan ready; rollback plan ready" --json

Expected output: JSON containing verdict: "PASS", matched onboarding signals, and mutated_files: []. Gaps return REVIEW; multiple risk indicators or low coverage return BLOCKED.

Price

$4.99