Automate τ²-bench evaluation setup and domain guidance for HarnessX.
npx skills add https://github.com/Darwin-Agent/HarnessX --skill tau2-playbook
Please help me install this Agent Skill. Skill: tau2-playbook Source: https://github.com/Darwin-Agent/HarnessX/tree/main/recipe/tau2_evolver/skills/tau2-playbook Command: npx skills add https://github.com/Darwin-Agent/HarnessX --skill tau2-playbook
τ²-bench-specific benchmark guidance for calibrating and evaluating multi-domain dialogue systems within HarnessX, including domain-specific policies, tool usage patterns, and reward signaling.
Configure and run a τ²-bench evaluation within HarnessX to benchmark multi-domain dialogue performance with domain-specific tool usage and reward signals.
High-intent search queries and answers about installing and using this skill.
You can evaluate multi-turn customer-service dialogues by configuring domain-specific policies, tool usage patterns, and reward signals within HarnessX to drive reliable scoring across retail, airline, and telecom tasks.
You can prevent tool call failures by applying PhaseAwareToolFilter, LoopDetection, and ToolFailureGuard mechanisms to improve stability and enforce phase-aware tool gating across dialogue tasks.
Yes, you can benchmark retail, airline, and telecom dialogue systems by integrating per-domain cheat-sheets, policy hints, and harness configurations to guide reliable domain-specific reward scoring and evaluation.
The best way to set up a tau2-bench evaluation harness is to use reusable harness recipes, system_append_processor, and guidance files to accelerate experimentation and standardize task metadata via the Tau2 evolver pipeline.
Yes, HarnessX supports phase-aware tool gating for dialogue evaluation through the PhaseAwareToolFilter component, which restricts tool availability based on task phases to ensure valid actions during multi-turn customer-service interactions.
Multi-turn dialogue evaluation gets stuck in repetitive loops due to unhandled tool failures or lack of phase-aware gating; applying LoopDetection and ToolFailureGuard mechanisms improves stability and breaks cyclic tool execution.
devendrapratapsinghDesign industry-specific enterprise architectures using TOGAF, ArchiMate, and FHIR standards.
founderphantomFine-tune language models with GRPO/TRL using custom reward signals.
raphaelchristiDisplay evolution progress and scores from the Harness Evolver workflow.
Supporter09Coordinate distributed reinforcement-learning post-training for large language models.
