experiment-validation

What problem does it solve?

Validate product feature hypotheses end-to-end and produce defensible Go/No-Go decisions by structuring experiment planning, data collection, guardrail enforcement, multi-agent inquiry, and report generation into a repeatable workflow.

Core Features & Use Cases

• Upstream reconciliation: automatically read and reconcile Design and Define artifacts to extract hypotheses, success/kill criteria, constraints, and risks.
• Experiment planning & tracking: create prioritized experiment plans (L3-L4), confirm statistical and guardrail parameters, track progress, and persist working files for large contexts.
• Data-led decisioning & inquiry: collect and summarize experimental data, run multi-agent advocate/challenger/observer inquiry sessions, and produce a validated report with traceable success-standard provenance.
• Integration & persistence: optionally query Feishu project MCP workitems, persist interim _wip files, generate final validate-v{ver}-v{N}-{YYYY-MM-DD}.md, and write learnings to assets.

Quick Start

Start a validation run by specifying the product and feature and confirming resources so the skill can read Design/Define artifacts, collect experiment data, run the inquiry session, and generate the validate report.