Multi-Model Router

Automatically routes tasks to the most suitable local or cloud AI model based on privacy, context length, cost, and performance requirements.

This package mostly does what it says (analyze prompts and pick a model), but there are practical issues to resolve before installing: 1) The code depends on an external tokenizer ('gpt-tokenizer') yet no package.json or install instructions are provided — confirm and install required Node dependencies or request the package.json from the author. 2) The privacy guarantee in SKILL.md is a policy claim the router alone cannot enforce — verify your OpenClaw deployment maps the listed local alias (e.g., ollama/...) to an actually-local runtime and that the platform prevents escalation to cloud models for sensitive tasks. 3) The skill writes audit logs and user-preferences to disk (logs/routing-audit.log, config/user-preferences.json); review those files for any metadata you consider sensitive and ensure log access is appropriately restricted. 4) Because provenance is unknown (source/homepage not authoritative), consider running the skill in a sandboxed environment first, review/complete missing packaging info (package.json, dependency list), and request the author or repository for more context before using in production.

Type: OpenClaw Skill Name: multi-model-router Version: 1.0.0 The skill bundle implements a legitimate multi-model routing system designed to optimize for privacy, cost, and context length. It uses local regex-based analysis in `scripts/analyzer.js` to detect sensitive information (such as API keys or passwords) and route those requests to local models (e.g., Ollama) to prevent cloud exposure. The implementation includes robust error handling, audit logging to local files, and comprehensive test suites, with no evidence of data exfiltration, malicious execution, or harmful prompt injection.