Reader persona: a founder, operator, marketer, product manager, or customer success lead who has access to metrics but often does not know which questions are worth asking first.
Job to be done: use an AI agent to turn scattered worries, customer signals, and business goals into a weekly list of metric questions that are safe to answer with dashboards, exports, or read-only database queries.
Most teams do not have a metrics problem.
They have a question problem.
The data exists somewhere: Stripe, PostHog, a CRM, a product database, support tools, spreadsheets, or a hand-built dashboard.
But the week starts with vague worries:
- Are trials converting?
- Did the new onboarding flow help?
- Are customers getting stuck?
- Is churn getting worse?
- Which channel is bringing useful users?
- Did last week’s content do anything?
Those are real concerns, but they are not yet good analysis questions.
An AI agent can help by turning them into a weekly metric question list.
This is especially useful before you connect an agent to analytics tools or read-only database skills. The agent should help you define the question before it touches the data.
The weekly workspace
Create a folder for the week:
metric-question-list/
2026-W20/
business-goals.md
customer-signals.md
product-changes.md
marketing-activity.md
questions.mdEach input file should be short.
Example business-goals.md:
# Business goals this week
- Improve trial-to-activated-user conversion.
- Understand whether Telegram onboarding confusion is hurting first-run success.
- Find one content topic that produced qualified signups.Example customer-signals.md:
# Customer signals
- Two users asked whether the agent was stuck during long browser tasks.
- One trial user said they did not know what to try first.
- A customer success lead liked the renewal-call brief workflow.Example product-changes.md:
# Product changes
- Added clearer progress text for long-running tasks.
- Updated the first-run onboarding copy.The agent does not need every raw event at this stage.
It needs enough context to ask better questions.
The agent prompt
Use this prompt:
Create a weekly metric question list from the files in this folder.
Rules:
- Do not invent data.
- Do not answer the questions yet.
- Turn vague concerns into measurable questions.
- Prefer aggregate questions over raw customer-level analysis.
- For each question, specify the likely data source.
- Mark sensitive questions that may involve PII or customer records.
- Suggest a safe first query or dashboard view, but do not run it.
Output questions.md with:
1. Top 5 metric questions
2. Why each question matters
3. Data source needed
4. Safe first measurement
5. Risk/approval notes
6. Questions to skip this weekThe line “do not answer the questions yet” matters.
At this stage, you want planning, not premature analysis.
What good output looks like
A useful questions.md might look like this:
# Weekly metric question list — 2026-W20
## 1. Did first-run activation improve after the onboarding copy update?
Why it matters:
Trial users said they did not know what to try first. If the copy change helped, first-run success should improve.
Data source needed:
Product analytics or app database.
Safe first measurement:
Compare the percentage of new users who complete one successful workflow within 24 hours, before vs. after the copy change.
Risk/approval notes:
Aggregate query only. No raw customer records needed.
## 2. Are long-running browser tasks creating trust issues?
Why it matters:
Multiple users asked whether the agent was stuck.
Data source needed:
Support notes, task logs, completion status, maybe product analytics.
Safe first measurement:
Count browser tasks longer than five minutes and compare completion rate, cancellation rate, and support mentions.
Risk/approval notes:
Avoid inspecting task contents unless approved.This is much better than asking the agent, “How are metrics?”
It creates analysis work that a human can approve.
Turn worries into measurable questions
Use this mapping:
| Vague worry | Better metric question |
|---|---|
| People are confused | Which step produces the highest support mention or drop-off? |
| Trials are weak | What percentage of trials reach first successful workflow within 24 hours? |
| Content is not working | Which posts brought visitors who clicked the main CTA? |
| Customers might churn | Which accounts have lower usage plus recent support frustration? |
| Sales calls are messy | Which objections repeat across qualified opportunities? |
| The agent feels slow | Which workflow types exceed the expected completion time? |
The agent is good at this translation.
It can take messy notes and propose a cleaner question set.
Keep the list short
Five questions is enough.
Three is often better.
A weekly metric list should help the team decide, not create a research backlog nobody will read.
Use this priority rule:
Question score = decision value + evidence availability - privacy risk - analysis complexityA boring question with clear data often beats an ambitious question that requires three exports, two joins, and a debate about definitions.
Add approval notes before data access
Every question should include a risk note.
Examples:
Approval: no approval needed for aggregate dashboard view.Approval: ask before querying raw customer records.Approval: ask before exporting CSV or sharing outside internal workspace.Approval: public claim requires human review and source link.This makes the metric workflow safer when you later connect tools such as read-only database skills, spreadsheets, analytics APIs, or browser automation.
The agent already knows which questions are safe and which ones need a pause.
Use the list during the week
Once the questions are approved, the agent can help answer them one by one.
Good follow-up prompt:
Answer question 1 only.
Use aggregate data only.
If you need raw customer rows, stop and ask.
Return the result, method, caveats, and one recommended next action.For marketing questions:
Answer the content question using only public analytics and CTA clicks.
Do not attribute individual users.
Do not make a public performance claim.For customer success questions:
Create a risk summary by account segment.
Do not include personal emails or private message contents.
Flag accounts that need human review.Notice the pattern: one question, clear data boundary, explicit stop condition.
Weekly review format
At the end of the week, the agent should produce a short closeout:
# Weekly metric closeout
## Answered questions
- Q1: First-run activation improved from X to Y after onboarding change.
- Q2: Long browser tasks over five minutes have higher support mentions.
## Unanswered questions
- Q4 skipped because required raw account-level data.
## Decisions made
- Add progress checkpoints to browser tasks.
- Write a help doc explaining long-running workflows.
## Next week's questions
- Did the progress checkpoint reduce confusion?This closes the loop.
Metrics should lead to decisions, not just more charts.
How this fits ClawMama
ClawMama is useful here because the workflow is conversational.
A founder can send rough notes in Telegram during the week, then ask the agent to produce the question list before touching analytics or databases.
A practical setup:
Monday: agent drafts the metric question list.
Human: approves the top 3 questions.
Agent: proposes safe aggregate queries or dashboard checks.
Human: approves sensitive access.
Friday: agent summarizes answers and next actions.With an OpenClaw/Hermes-style agent, you can combine this with read-only tools, browser workflows, and human approvals. ClawMama gives new users $2 credits and access to the latest ChatGPT model, which is enough to test the workflow with a spreadsheet or exported dashboard before connecting deeper systems.
Bottom line
Do not start the week by asking an agent to “analyze the metrics.”
Start by asking it to make the questions better.
A weekly metric question list keeps analysis focused, safer, and more useful. It also gives the human a clean approval point before the agent touches sensitive data.