By hubert-sys
Autonomous optimization loop — iterates on prompts, skills, templates, or code overnight. Two evaluation modes: deterministic (eval.py with proxy heuristics) for mechanical checks, or AI judge (LLM rubric scoring) for creative/subjective quality. Uses 4-way agent separation for unbiased evaluation.
Eval Agent for AutoResearch. Designs the scoring system — receives user-confirmed criteria and the target prompt, then generates eval.py + test_cases.json (deterministic mode) or rubric.md + test_cases.json (AI judge mode). The main agent never sees the eval artifacts in detail.
Judge Agent for AutoResearch. Scores outputs against a locked rubric for quality assessment. Operates with fresh context every iteration — knows NOTHING about iteration count, prompt changes, or optimization goals. Only follows the rubric.
Test Runner Agent for AutoResearch. Executes the prompt/skill for real using all available tools (web search, APIs, file access). Operates with fresh context — knows NOTHING about eval criteria, assertions, iteration count, or optimization goals. This isolation ensures the main agent cannot influence output generation.
Uses power tools
Uses Bash, Write, or Edit tools
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