Address PR #366 CI review round 2 (2 P3): align ROADMAP wording with corrected scope; cover new exports in import-surface test

P3 #1 (ROADMAP wording drift):
ROADMAP.md still said the new fields "gate WooldridgeDiD QMLE /
ContinuousDiD prerequisites pre-fit" and mentioned "time-invariance",
which contradicted the round-1 corrections to TreatmentDoseShape's
docstring + autonomous guide §2 + §5.2. Reworded to match: the new
fields add descriptive distributional context only;
`outcome_shape.is_count_like` informs (not gates) the WooldridgeDiD
QMLE judgment, and the authoritative ContinuousDiD pre-fit gates
remain `has_never_treated`, `treatment_varies_within_unit`, and
`is_balanced`. "Time-invariance" wording removed (the field was
dropped in round 1).

P3 #2 (import-surface test coverage):
`test_top_level_import_surface()` previously only verified
`profile_panel`, `PanelProfile`, `Alert`. Extended to also cover the
two new public exports `OutcomeShape` and `TreatmentDoseShape`,
asserting both their importability and their presence in
`diff_diff.__all__`.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

Author: igerber

ROADMAP.md

@@ -138,7 +138,7 @@ Long-running program, framed as "building toward" rather than with discrete ship
 - Baker et al. (2025) 8-step workflow enforcement in `diff_diff/practitioner.py`.
 - `practitioner_next_steps()` context-aware guidance.
 - Runtime LLM guides via `get_llm_guide(...)` (`llms.txt`, `llms-full.txt`, `llms-practitioner.txt`, `llms-autonomous.txt`), bundled in the wheel.
-- `profile_panel(df, ...)` returns a `PanelProfile` dataclass of structural facts about the panel - factual, not opinionated. Pairs with the `"autonomous"` guide variant (reference-shaped: estimator-support matrix + per-design-feature reasoning) so agents describe the data then consult a bundled reference rather than calling a deterministic recommender. `PanelProfile.outcome_shape` and `PanelProfile.treatment_dose` extensions expose count-likeness, bounded-support, dose support, and time-invariance facts that gate WooldridgeDiD QMLE / ContinuousDiD prerequisites pre-fit. The autonomous guide §5 walks through three end-to-end PanelProfile -> reasoning -> validation worked examples.
+- `profile_panel(df, ...)` returns a `PanelProfile` dataclass of structural facts about the panel - factual, not opinionated. Pairs with the `"autonomous"` guide variant (reference-shaped: estimator-support matrix + per-design-feature reasoning) so agents describe the data then consult a bundled reference rather than calling a deterministic recommender. `PanelProfile.outcome_shape` and `PanelProfile.treatment_dose` extensions add descriptive distributional context (count-likeness / bounded-support hints on numeric outcomes; dose support and zero-dose presence on continuous treatments). They are descriptive only — `outcome_shape.is_count_like` informs the WooldridgeDiD-QMLE-vs-linear-OLS judgment but does not gate it, and the authoritative ContinuousDiD pre-fit gates remain the existing `has_never_treated`, `treatment_varies_within_unit`, and `is_balanced` fields. The autonomous guide §5 walks through three end-to-end PanelProfile -> reasoning -> validation worked examples.
 - Package docstring leads with an "For AI agents" entry block so `help(diff_diff)` surfaces the agent entry points automatically.
 - Silent-operation warnings so agents and humans see the same signals at the same time.
 

tests/test_profile_panel.py

@@ -402,14 +402,23 @@ def test_all_nan_treatment_is_categorical():
 
 
 def test_top_level_import_surface():
-    """profile_panel, PanelProfile, and Alert must be importable from the
-    top-level namespace so `help(diff_diff)` points at real symbols."""
+    """profile_panel, PanelProfile, Alert, OutcomeShape, and
+    TreatmentDoseShape must be importable from the top-level namespace
+    so `help(diff_diff)` points at real symbols."""
     import diff_diff
 
     assert callable(diff_diff.profile_panel)
     assert diff_diff.PanelProfile.__name__ == "PanelProfile"
     assert diff_diff.Alert.__name__ == "Alert"
-    for name in ("profile_panel", "PanelProfile", "Alert"):
+    assert diff_diff.OutcomeShape.__name__ == "OutcomeShape"
+    assert diff_diff.TreatmentDoseShape.__name__ == "TreatmentDoseShape"
+    for name in (
+        "profile_panel",
+        "PanelProfile",
+        "Alert",
+        "OutcomeShape",
+        "TreatmentDoseShape",
+    ):
         assert name in diff_diff.__all__, f"{name} missing from __all__"