Transforms high-level innovation PRDs into actionable experiment plans with specific hypotheses, success metrics, and evaluation strategies.
The Innovation Experiment Designer skill bridges the gap between abstract project goals and empirical testing for AI-driven products. It empowers developers and product managers to validate RAG configurations, agentic workflows, and automation logic by generating structured test plans. Each plan includes falsifiable hypotheses, instrumentation guides, sample size recommendations, and go/no-go thresholds, ensuring that innovation projects are backed by concrete evidence rather than assumptions.
主要功能
01Success metric and guardrail definition
02Instrumentation and logging guidance
03ROI-based experiment prioritization
04Automated hypothesis generation from PRDs
050 GitHub stars
06RAG and agent evaluation framework design
使用场景
01Comparing performance across different AI agent workflow designs
02Validating new RAG retrieval strategies against a baseline
03Creating evidence-based reports for innovation project stakeholders