Integrate Mux with Terminal-Bench to automate AI agent benchmarking and failure analysis.
npx skills add https://github.com/neilmovva/mux --skill tbench-neilmovva
Please help me install this Agent Skill. Skill: tbench Source: https://github.com/neilmovva/mux/tree/main/.mux/skills/tbench Command: npx skills add https://github.com/neilmovva/mux --skill tbench-neilmovva
This Skill integrates Mux with Terminal-Bench to automate the benchmarking of AI agents, analyze their performance, and identify areas for improvement.
Run the full benchmark suite using the make command.
High-intent search queries and answers about installing and using this skill.
Benchmark AI agents by running automated benchmark suites against them to gauge performance on coding tasks and compare results with established models. This process facilitates detailed performance analysis and identifies areas where agents underperform.
Analyze LLM evaluation failures by identifying specific tasks where AI agents underperform compared to others. This failure analysis highlights weaknesses in agent performance and helps pinpoint areas for improvement.
Yes, you can configure environment variables for concurrency, timeouts, and task selection during AI agent benchmarking. You can also specify agent-specific arguments for model selection and experiment enablement.
The best way to submit benchmark results is by preparing and submitting them directly to platforms like Terminal-Bench. This automates the execution of benchmark suites and facilitates the publication of your AI agent performance data.
No, you do not need to run a full suite if you configure task selection environment variables. You can run targeted tests against a new LLM to gauge its performance and compare it against established models.
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