experiment-readout

Name: experiment-readout
Availability: InStock
Author: Flintmere

Generate Flintmere experiment readouts from design and results exports.

Updated Apr 18, 2026

Search Tags:#experiment #statistical-analysis #experimental-design #data-intelligence #readout #decision-rule

Ready to run

npx skills add https://github.com/Flintmere/flintmere --skill experiment-readout

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: experiment-readout
Source: https://github.com/Flintmere/flintmere/tree/main/.claude/skills/experiment-readout
Command: npx skills add https://github.com/Flintmere/flintmere --skill experiment-readout

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill enables teams to generate honest, rule-based readouts for completed Flintmere experiments, ensuring adherence to pre-declared decision rules, updating experiment logs, and surfacing actionable learnings.

Core Features & Use Cases

Apply pre-declared decision rules to close experiments and determine outcomes.
Compute primary metrics, confidence intervals, and effect sizes, then document learnings and follow-ups.
Emit a formal readout document and update memory/marketing or project logs as required.

Quick Start

Run the readout workflow after the experiment window closes to generate the final readout.

Frequently Asked Questions about experiment-readout

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I generate a statistical analysis report after an experiment closes?▼

To generate an experiment readout, run the workflow after the experiment window closes. It identifies and validates outcomes using pre-declared designs and results exports, summarizing primary metrics, confidence intervals, and learnings.

How do I apply pre-declared decision rules to determine an experiment's outcome?▼

Applying pre-declared decision rules ensures compliance when closing experiments. The readout workflow validates the final results against the original experimental design, articulating the outcome and updating the experiment-log entry.

What is the best way to document experiment learnings and confidence intervals?▼

Documenting experiment learnings involves computing primary metrics, confidence intervals, and effect sizes from results exports. The workflow then emits a formal readout document and updates project logs with actionable follow-ups.

Can I update my experiment-log automatically when closing an experiment?▼

Yes, you can update the experiment-log automatically. The readout workflow ensures compliance with the pre-declared rule, updates the experiment-log entry, and emits the final readout document in one step.

Do I need results exports to calculate effect sizes and primary metrics?▼

Yes, results exports are required to calculate effect sizes and primary metrics. The workflow validates outcomes by articulating results across the design references and the experiment-log entry to ensure an honest readout.

Why does an experiment readout require a pre-declared experimental design?▼

A pre-declared experimental design is required to ensure an honest, rule-based readout. The workflow validates the final results against this original design to determine the outcome and maintain compliance throughout the closure process.