result = assistant.forecast(data, test_size=36, profile=profile, plan=plan)
answer = assistant.ask(
    "Explain the evaluation metrics. Is the MASE good, "
    "and what do the predictions look like?",
    context=result,
)

The MASE is 0.62, below 1: the forecast beats the one-step naive forecast, which repeats the last observed value. On average the predictions miss by 45.8 users per hour (MAE), and each of the 36 comes with an 80% interval from bootstrapped residuals.

backtest = assistant.backtest(data, cv, profile=profile, plan=plan)
answer = assistant.ask(
    "Explain the backtesting strategy and the metrics. "
    "Is the model good enough to deploy?",
    context=backtest,
)

Trained once and never refit, the model forecasts 17 folds of 36 hours with a MAE of 49.5 users per hour and a MASE of 0.65, so it beats the one-step naive forecast. Whether that error is small enough to deploy depends on what a miss costs you. The errors of each fold are not in the result, so I cannot say how stable it is over time.

candidates = [
    ("LightGBM, recursive", {"forecaster": "ForecasterRecursive"}),
    ("LightGBM, direct", {"forecaster": "ForecasterDirect"}),
    ("Ridge, recursive", {"forecaster": "ForecasterRecursive",
                          "estimator": "Ridge"}),
]
comparison = assistant.compare(data, cv, profile=profile, candidates=candidates)
answer = assistant.ask(
    "Explain the comparison. Is the margin between the top "
    "candidates meaningful, or are they practically equivalent?",
    context=comparison,
)

LightGBM, recursive ranks first with a MAE of 49.5, 6.7% ahead of LightGBM, direct (53.1), and it beats the seasonal naive baseline by 22.4%. The spread across folds is not in the result, so I cannot tell whether the gap between the top two is meaningful or they are practically equivalent.

answer = assistant.ask(
    "Walk me through this plan: why these lags and window "
    "features, and how will the prediction interval be produced?",
    context=profile,
    plan=plan,
)

The 28 lags come from the partial autocorrelation of the series: the last hours (1, 2, 3), around the same hour a day earlier (23 to 25) and a week earlier (167, 169). Rolling means over 3, 24 and 168 hours add the recent, daily and weekly level. The 80% interval is built by bootstrapping the residuals of the model.

# No context: a general question, answered from the skills
answer = assistant.ask(
    "For hourly demand with strong daily and weekly seasonality, "
    "when should I prefer a direct forecasting strategy over a "
    "recursive one?"
)

Prefer a direct strategy when the horizon is long enough for recursive errors to pile up, or when each step depends on different lags (the next hour on the last one, the same hour tomorrow on lag 24). The cost is one model per step: ForecasterDirect trains 24 of them for a 24-hour horizon. To decide on your data, rank both with assistant.compare().