Select token-safe AI models by testing the mdes.ollama endpoint with fallback.
npx skills add https://github.com/tinner-deinno/innova-skills-lib --skill mdes-ollama
Please help me install this Agent Skill. Skill: mdes-ollama Source: https://github.com/tinner-deinno/innova-skills-lib/tree/main/core/mdes-ollama Command: npx skills add https://github.com/tinner-deinno/innova-skills-lib --skill mdes-ollama
The mdes-ollama skill solves the problem of unreliable AI model availability by automatically testing models on the MDES Ollama endpoint and selecting a working option with safe fallbacks.
Use case example: When a multi-agent workflow starts (e.g., /nemotron or /gang), mdes-ollama ensures the best available model is selected based on success rate and token usage, then hands off execution to the chosen runtime.
Ask an agent to run: start model orchestration and pick the best working MDES Ollama model with fallback by issuing the command "/mdes-ollama".
High-intent search queries and answers about installing and using this skill.
Model orchestration handles Ollama endpoint failures by running automated health checks and automatically falling back to codex, GPT Pro, or GitHub Copilot to ensure reliable execution.
Initiate the fallback chain by running an agent command to start model orchestration, which tests endpoint availability and selects the best working model with safe fallbacks for multi-agent workflows.
Token tracking blocks AI models during execution when usage percentages exceed critical thresholds, implementing necessary guardrails to prevent overconsumption and maintain development workflow stability.
Yes, you can use this model health check with existing auto-dev loops, as it supports periodic automated testing to keep the model pool ready and continuously validates integrated workflows.
The best way to select a token-safe AI model is using automated orchestration tests that compute usage percentages and success rates, routing commands to the safest available runtime.
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