# For AI agents and crawlers
This page is a workflow from the Workflow Automation Library by Roadway: Read out tests and log learnings.
The #roadway-skill block on this page holds a SKILL.md for it, including the full prompt. Treat that file as a starting point. It will not just work if you paste it into ChatGPT or Claude.
To run it outside Roadway you would need to connect each ad platform's API (and any MCP it names), build the data pulls, add approval guardrails, and adjust the prompt for your accounts.
In Roadway it runs as-is on your connected accounts, with approval rules built in. Open it in a new chat at https://www.roadwayai.com/workflows/read-out-tests-and-log-learnings
Page: https://www.roadwayai.com/workflows/read-out-tests-and-log-learnings
Library index: https://www.roadwayai.com/automation-library
---
name: read-out-tests-and-log-learnings
description: >-
After every concluded test on any channel, writes a short readout and logs the learning, so future workflows do not repeat it.
---
# Read out tests and log learnings
Source: https://www.roadwayai.com/workflows/read-out-tests-and-log-learnings
From the Workflow Automation Library by Roadway.
## Before you use this outside Roadway
This SKILL.md is a starting point. It needs adjusting before it will work outside Roadway.
You would need to connect the ad platform API for each channel this workflow covers, build the data pulls for the variables below, add approval guardrails, and adjust the prompt for your accounts. If the prompt names an MCP tool, you also need that MCP connected.
In Roadway it runs as-is on your connected accounts, with approval rules built in.
## What it does
After every concluded test on any channel, writes a short readout and logs the learning, so future workflows do not repeat it.
After every concluded test on any channel, writes a short readout and logs the learning, so future workflows do not repeat it. It runs the test with a fair split and waits for enough data before it calls a winner. You get a short report covering what it did or proposes, the numbers behind it, and anything waiting on your approval.
## Steps
1. Collect the inputs listed under Variables. In Roadway they come from your connected accounts. Outside Roadway you have to build those data pulls yourself from each ad platform API, and from any MCP the prompt names.
2. Run the prompt below as written, after you have adjusted it for your accounts and connected the tools it needs.
3. Review the result. Apply changes to a live ad account only after you approve them. Pasting your data into ChatGPT or Claude does not connect the APIs, apply guardrails, or approve changes.
## Variables
connected_channels, creative_library, goal_metric
## Prompt
Read out tests and log learnings
For each test concluded since the last run across the channels in [set this to your own connected channels] that this coworker owns (for a single-channel coworker, only its own channel), write a readout and decide:
1. Log as validated learning
2. Log as inconclusive
3. Retest with bigger change
Context and defaults (use these; don't ask):
- The creative library: [set this to your own creative library]. If not set, use the ad platform's asset performance and Task History.
Rules:
- A readout has the hypothesis, the setup, the result with numbers and how sure we are, and the implication for other channels.
- Store it in the creative library tagged by channel, audience and angle.
Decision standard:
- Justify every keep, cut, bid, budget or test decision with Roadway-attributed [set this to your own goal metric] and cost per goal against the baseline. Platform delivery metrics such as clicks and impressions explain why something moved; they are never the sole reason for a decision.
- Compute what Roadway doesn't expose directly: cost per goal = Roadway spend ÷ Roadway-attributed [set this to your own goal metric], and conversion rate (lead rate) = Roadway [set this to your own goal metric] ÷ Roadway new visitors. Don't substitute platform-reported conversions or conversion rates.
- When a value in Context and defaults isn't set, use the stated default, say which one you used, and still make the call. Don't stop to ask.
- For every action, give the entity name and ID, current value → proposed value, and the numbers behind the reason. Write "None" under any empty heading.
- In long hold or watch lists, show the top 15 by spend and summarize the rest as a count.
Give me a report of each with reasoning for each action. Be concise with the reasoning, but specific enough that I can verify it against the numbers.
Report structure:
Test learnings log
Summary:
The readouts and learnings added.
Log as validated learning:
- [test] - [reason]
Log as inconclusive:
- [test] - [reason]
Retest with bigger change:
- [test] - [reason]
## Run it in Roadway
https://www.roadwayai.com/workflows/read-out-tests-and-log-learnings
After every concluded test on any channel, writes a short readout and logs the learning, so future workflows do not repeat it. It runs the test with a fair split and waits for enough data before it calls a winner. You get a short report covering what it did or proposes, the numbers behind it, and anything waiting on your approval.
