Merge pull request #239 from igerber/ai-practitioner-guide

Add AI practitioner guardrails (Baker et al. 2025)

Author: igerber

CLAUDE.md

@@ -138,6 +138,7 @@ category (`Methodology/Correctness`, `Performance`, or `Testing/Docs`):
 | `CONTRIBUTING.md` | Documentation requirements, test writing guidelines |
 | `.claude/commands/dev-checklists.md` | Checklists for params, methodology, warnings, reviews, bugs (run `/dev-checklists`) |
 | `.claude/memory.md` | Debugging patterns, tolerances, API conventions (git-tracked) |
+| `docs/llms-practitioner.txt` | Baker et al. (2025) 8-step practitioner workflow for AI agents |
 | `docs/performance-plan.md` | Performance optimization details |
 | `docs/benchmarks.rst` | Validation results vs R |
 

TODO.md

@@ -80,6 +80,7 @@ Deferred items from PR reviews that were not addressed before merge.
 | ~376 `duplicate object description` Sphinx warnings — caused by autodoc `:members:` on dataclass attributes within manual API pages (not from autosummary stubs); fix requires restructuring `docs/api/*.rst` pages to avoid documenting the same attribute via both `:members:` and inline `autosummary` tables | `docs/api/*.rst` | — | Low |
 | Plotly renderers silently ignore styling kwargs (marker, markersize, linewidth, capsize, ci_linewidth) that the matplotlib backend honors; thread them through or reject when `backend="plotly"` | `visualization/_event_study.py`, `_diagnostic.py`, `_power.py` | #222 | Medium |
 | Survey bootstrap test coverage — **Resolved**. Added FPC census zero-variance, single-PSU NaN, full-design bootstrap for CS/ContinuousDiD/EfficientDiD, and TROP Rao-Wu vs block bootstrap equivalence tests. | `tests/test_survey_phase*.py` | — | Resolved |
+| Doc-snippet smoke tests only cover `.rst` files; new `.txt` AI guides are outside CI validation | `tests/test_doc_snippets.py` | #239 | Low |
 
 ---
 

diff_diff/__init__.py

@@ -168,6 +168,7 @@
     plot_staircase,
     plot_synth_weights,
 )
+from diff_diff.practitioner import practitioner_next_steps
 from diff_diff.datasets import (
     clear_cache,
     list_datasets,
@@ -344,4 +345,6 @@
     "load_dataset",
     "list_datasets",
     "clear_cache",
+    # Practitioner guidance
+    "practitioner_next_steps",
 ]