Coordinate AEGIS improvement plans across ARC-Challenge, GSM8K, and GRPO tasks.
npx skills add https://github.com/zapabob/SO8T --skill plan-mode
Please help me install this Agent Skill. Skill: plan-mode Source: https://github.com/zapabob/SO8T/tree/main/OpenCode_src/skills/plan_mode Command: npx skills add https://github.com/zapabob/SO8T --skill plan-mode
Plan-mode provides a structured, repeatable plan to scientifically improve AEGIS model performance across ARC-Challenge, GSM8K, and GRPO-based rewards, coordinating multiple evaluation loops.
Initialize AEGISImprovementPlan and run ARC improvements, GSM8K sanity checks, GRPO optimization, and v2.5 integration steps in sequence.
High-intent search queries and answers about installing and using this skill.
Automating ARC-Challenge improvements involves running a modular plan engine that coordinates robust answer extraction, timeout optimization, and prompt consistency checks across iterative benchmarking workflows to systematically raise model performance.
GSM8K sanity checks require scripts that verify data contamination, assess multi-seed stability, and run zero-shot evaluations, coordinated through a structured improvement pipeline to ensure reliable cross-task generalization.
GRPO reward design works by applying multi-objective optimization scripts within an end-to-end pipeline, coordinating reward function adjustments alongside benchmark evaluations to systematically raise model robustness.
Yes, a v2.5 integration pipeline can execute end-to-end improvement workflows that combine ARC-Challenge, GSM8K, and GRPO tasks, running ABC test automation and cross-task generalization experiments in sequence.
A modular plan engine like AEGISImprovementPlan is required to coordinate multi-seed evaluations, data contamination checks, and benchmark scripts, executing each component in sequence for systematic performance gains.
Cross-task generalization requires iterative benchmarking workflows because robustness must be validated across ARC-Challenge, GSM8K, and GRPO tasks through repeated evaluation loops, ABC test scenarios, and multi-seed assessments.
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