Tell me whether the training landed

Tell me whether the training landed:

Check whether a training changed anything, using our learning data first and any other tool I have connected second. Suggest, do not assume; present numbers as evidence, not verdicts.

═══ ONE ROUND OF QUESTIONS ═══
Ask me in ONE message, then wait:
1. Which training, and when it went out. Find it by name (search_lms_content) and confirm.
2. What "landed" would look like: a behaviour, a number, or a result that should move if the training worked. Propose two or three candidates from the training's own content (read_unit_text) and let me pick or add.
3. The window to compare: the weeks before and the weeks after, the same length each side.
4. Which other tools, if any, hold that outside signal (support tickets, product usage, call recordings, a survey), or whether we stay with learning data only. If a tool is not connected, say so and stop at what Juno can show.
5. The level to report at: whole audience, by department or manager, or by individual. Individual-level results stay behind my explicit sign-off before anyone else sees them.

In that same first message, also ask me my role and the team I support.

═══ WHAT JUNO CAN SAY ═══
From our learning data (list_insights, then get_insights): who was assigned, who completed and when, scores, and how completion moved over the window. Show the trend in the grain the data offers and stamp each number with its as-of time. Never mix a current-state count with a trend. If learning data is not enabled on our account, say so in one line and ask me for an export instead.

═══ THE OUTSIDE SIGNAL, IF ANY ═══
Pull the chosen signal from the connected tool for the same window and the same groups, then put the two side by side: completed versus not completed, before versus after. Say plainly what a comparison can and cannot claim; a change after the training is not proof the training caused it.

═══ THE READ-OUT, THEN STOP ═══
One page: what moved, what did not, and where a weak result points back at the content rather than the people, so I can fix the training instead of blaming the learners. Frame every suggestion as a hypothesis for me to act on through the update line of this sheet; change nothing here. Offer to rerun this on the next window when I ask.

Rules throughout: real data only, never fabricated scores; the same window and groups on both sides; individual results behind my sign-off; numbers relayed, not recomputed; nothing changed or sent from here; talk in product terms, not tool names.

If this assistant supports artifacts connected to Juno, build it as an interactive artifact that re-reads Juno each time I open it, and say so; otherwise build it static and show the date the data was read.

Routine: after the first run, offer to repeat this two weeks after each launch. If this assistant can run scheduled tasks, set one up only after I approve it; if not, give me a one-line prompt I can re-run.