When someone reports a problem and wants it fixed, ask one round of the minimal high-information diagnostic questions — exact error, how to reproduce, when it started, how widespread, what changed — before proposing a fix.
NewStale
Automatic
◆Support pack
What it changes
13 of 14 test cases passed with the skill.
Pass-rate lift
—
Tokens
+354%
Agent turns
0%
Response time
+1%
Model gemini-3.5-flashMeasured 7/8/2026v113 of 14 cases passed with skill
Tokens, turns and time describe resource use, not a dollar price.
Lift withheld: the two benchmark arms were not comparable.
What's inside
The instructions your model reads when the skill loads.
SKILL.md63 linessingle file
Ask the diagnostic questions before proposing a fix
Someone hands you a problem — "checkout is broken," "the nightly job started failing," "users can't
log in" — and asks how to fix it. Left to itself, the model tends to jump straight to a plausible-
sounding fix: it guesses a cause and lists remedies before it knows the exact symptom, whether the
problem reproduces, or what changed. A fix aimed at an unconfirmed cause is a guess. Most of the
guesses are wrong, and each wrong one costs a round trip.
The discipline: when a problem is reported and the cause is not yet established, spend your first
response gathering the small set of facts that most narrows the cause — then propose a fix. Ask one
tight round of questions, not a slow one-at-a-time drip.
The five high-information questions
Ask the ones the report has not already answered. These are ordered by how much they typically
narrow the cause:
Exact symptom. What is the literal error text, status code, or precise observed behavior —
not "it's broken" but the actual message, and where it appears (log line, UI, API response).
Reproduction. Does it happen every time or intermittently? What are the smallest steps,
inputs, or account that trigger it? A bug you can reproduce on demand is half solved.
Timeline. When did it start? What was the last known-good moment? Did it ever work, or is
this a first-time setup?
Scope / blast radius. Who and what is affected — one user or everyone, one environment or all,
one record or the whole table, one region or global? Scope separates "config typo" from "outage."
What changed. What happened right before it started — a deploy, a config or flag flip, a
dependency bump, a data migration, an infra or upstream-provider change? Most regressions trace to
the most recent change.
How to ask
Batch them. Put the 3–6 relevant questions in one message. Do not ask one, wait, ask the next.
Lead with the questions. Don't bury them under a wall of speculative causes. It is fine to name
your leading hypothesis in a line, but the ask comes first, not a pre-committed diagnosis.
Skip what's answered. If the report already gives the exact error and a repro, don't re-ask —
move to the questions that are still open (timeline, scope, what changed).
Prioritize by information value. Ask the questions that split the space of causes in half.
Avoid low-value questions ("have you tried restarting?", "are you sure it's actually broken?").