CLI and workspace hygiene · Lesson 11 of 12 · 5 min

Write an operator note

A useful operator note distinguishes observation, interpretation and next action. Include identifiers and limits so the next person can reproduce the investigation.

Open the lab Course map

Mastery 0/3Run itExplain itApply it

learner@clairos:~ $

↑ history · Tab complete · Ctrl+C cancel · Esc, Tab to leaveExit 0

Your objectives

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1
Read the case evidence
Read /opt/ai-lab/cases/aiops-cli-workspace-011.json. Separate the recorded facts from your assumptions.
Hint & worked example
A useful operator note distinguishes observation, interpretation and next action. Include identifiers and limits so the next person can reproduce the investigation.
The complete case JSON is read successfully.
```
cat /opt/ai-lab/cases/aiops-cli-workspace-011.json
```
Common mistake to avoid: Write only “AI is slow” and discard the request identifier.
2
Isolate the deciding signal
Select the next_action field from the case JSON. What does it establish—and what does it not establish?
Hint & worked example
Record the measured window, tentative hypothesis and next trace to inspect.
A successful JSON query isolates next_action.
```
jq .next_action /opt/ai-lab/cases/aiops-cli-workspace-011.json
```
Common mistake to avoid: Using a different case or an unrelated signal as the justification.
3
Record your operational decision
Choose the justified code: record-next-action, change-without-evidence, or ignore-the-signal. Save that one code followed by a newline in /home/learner/lab/aiops-cli-workspace-011-decision.txt.
Hint & worked example
Record the measured window, tentative hypothesis and next trace to inspect.
The decision artifact contains the correct code, not just a matching command in history.
```
printf '%s\n' 'record-next-action' > /home/learner/lab/aiops-cli-workspace-011-decision.txt
```
Common mistake to avoid: Write only “AI is slow” and discard the request identifier.

Understand the pattern

A useful operator note distinguishes observation, interpretation and next action. Include identifiers and limits so the next person can reproduce the investigation.

Interpret the evidence

Record the measured window, tentative hypothesis and next trace to inspect.

A tempting mistake

Write only “AI is slow” and discard the request identifier.

All names, measurements, prices, versions, and thresholds in this exercise are fictional training data. The evidence supports this bounded scenario, not a claim about a live provider.

Worked example and expected result

$ cat /opt/ai-lab/cases/aiops-cli-workspace-011.json

{
  "fixture": "Fictional training data; not a live measurement.",
  "observation": "p95=480ms over 5m",
  "hypothesis": "retrieval delay",
  "next_action": "inspect trace req-014"
}

Exit status: 0. The command completed successfully in the training fixture.

$ jq .next_action /opt/ai-lab/cases/aiops-cli-workspace-011.json

"inspect trace req-014"

Exit status: 0. The command completed successfully in the training fixture.

$ printf '%s\n' 'record-next-action' > /home/learner/lab/aiops-cli-workspace-011-decision.txt

(no output; exit 0)

Exit status: 0. The command completed successfully in the training fixture.

Try a variation

Name the assumption that would have to change for “record-next-action” to stop being the justified action. Do not silently replace missing evidence with a guess.

Explain it

What does the evidence in “Write an operator note” justify?Make the change now and gather the missing evidence afterward.Record the measured window, tentative hypothesis and next trace to inspect.Write only “AI is slow” and discard the request identifier.

Apply it elsewhere

A colleague concludes: “Write only “AI is slow” and discard the request identifier.” What should you do?Reject that conclusion; return to the case evidence and its stated limits.Accept it because the training command completed.Treat this fixture as a live measurement.

Keep the momentum.

Complete the lab, then explain and apply the pattern.

Next: Workspace hygiene checkpoint Try an incident drill

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Keep learning

Case methodology: evidence, limits, decision