Document new SyntheticDiDResults validation diagnostics in bundled guides

PR #309 added four validation diagnostics to SyntheticDiDResults:
`get_weight_concentration()`, `get_loo_effects_df()`, `in_time_placebo()`,
and `sensitivity_to_zeta_omega()`. Because this PR is the first place the
guides ship inside the wheel, we want them faithful to main's API at the
moment of merge.

- `llms-full.txt`: add the four methods to the SyntheticDiDResults
  Methods line and a short "Validation diagnostics" subsection describing
  each.
- `llms-practitioner.txt`: split the former `SyntheticDiD/TROP` bullet so
  SyntheticDiD now points at the built-in helpers (with the jackknife
  caveat for LOO); TROP keeps the generic guidance.

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

Author: igerber

diff_diff/guides/llms-full.txt

@@ -1029,6 +1029,12 @@ Returned by `SyntheticDiD.fit()`.
 
 **Methods:** `summary()`, `print_summary()`, `to_dict()`, `to_dataframe()`, `get_unit_weights_df()`, `get_time_weights_df()`
 
+**Validation diagnostics** (call after `fit()`):
+- `get_weight_concentration(top_k=5)` - effective N and top-k weight share; flags fragile synthetic controls dominated by a few donor units
+- `get_loo_effects_df()` - leave-one-out influence per treated unit (requires `variance_method="jackknife"` at fit and >=2 treated units with positive effective support)
+- `in_time_placebo()` - re-estimate on shifted fake treatment dates in the pre-period; near-zero placebo ATTs indicate a credible design
+- `sensitivity_to_zeta_omega()` - re-estimate across a grid of unit-weight regularization values; checks ATT robustness to the auto-selected zeta_omega
+
 ### TripleDifferenceResults
 
 Returned by `TripleDifference.fit()`.

diff_diff/guides/llms-practitioner.txt

@@ -296,7 +296,8 @@ to your estimator's API. Examples:
 - **StackedDiD**: vary `clean_control` definition
 - **EfficientDiD**: compare `control_group='never_treated'` vs `'last_cohort'`
 - **ImputationDiD/TwoStageDiD**: leave-one-cohort-out, cross-estimator comparison
-- **SyntheticDiD/TROP**: in-time or in-space placebo (fake treatment date, leave-one-unit-out)
+- **SyntheticDiD**: built-in diagnostics on the results object - `results.in_time_placebo()`, `results.get_loo_effects_df()` (requires `variance_method="jackknife"` at fit time), `results.sensitivity_to_zeta_omega()`, and `results.get_weight_concentration()`
+- **TROP**: in-time or in-space placebo (fake treatment date, leave-one-unit-out)
 
 ```python
 from diff_diff import run_all_placebo_tests