strict_mode.rst: mark illustrative python blocks as doc-test:skip

The repo's docs/test_doc_examples.py runs python code blocks against a
synthetic graph fixture (`g`, `nodes`, `edges` with columns id/type/score
etc). My code blocks reference Person/KNOWS labels and undefined symbols
(`ast`, `ctx`) — they are operator-illustration snippets, not runnable
against the fixture.

Mark each python code block with `.. doc-test: skip` so the harness
recognizes them as illustrative. Bash and text blocks auto-skip.

Verified: pytest docs/test_doc_examples.py -k strict_mode -> 1 skipped
(no failures); sibling cypher.rst / fundamentals.rst pass as before.

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

Author: lmeyerov

docs/source/gfql/strict_mode.rst

@@ -46,6 +46,8 @@ Explicit preflight (the primary operator entrypoint)
 For most operator workflows, prefer the explicit preflight API. It returns
 structured diagnostics and never executes query operators:
 
+.. doc-test: skip
+
 .. code-block:: python
 
    report = g.gfql_validate(
@@ -60,6 +62,8 @@ For execution guarded by a preflight check, use the ``validate=True`` flag
 on ``g.gfql(...)`` (which runs the same preflight in strict mode before
 executing):
 
+.. doc-test: skip
+
 .. code-block:: python
 
    result = g.gfql(
@@ -81,32 +85,37 @@ three-tier precedence ladder for the binder default used by
 
 1. **Explicit binder parameter** — the strongest signal.
 
-   .. code-block:: python
+.. doc-test: skip
 
-      from graphistry.compute.gfql.frontends.cypher.binder import FrontendBinder
-      FrontendBinder().bind(ast, ctx, strict_name_resolution=True)
+.. code-block:: python
 
-   This is rarely useful directly — most callers reach the binder via
-   ``g.gfql(query)`` rather than constructing it themselves.
+   from graphistry.compute.gfql.frontends.cypher.binder import FrontendBinder
+   FrontendBinder().bind(ast, ctx, strict_name_resolution=True)
+
+This is rarely useful directly — most callers reach the binder via
+``g.gfql(query)`` rather than constructing it themselves.
 
 2. **Catalog metadata flag** — pinned per dataset.
 
-   .. code-block:: python
+.. doc-test: skip
 
-      catalog = GraphSchemaCatalog.from_schema_parts(
-          node_columns={"id", "label__Person"},
-          edge_columns={"src", "dst", "label__KNOWS"},
-          metadata={"strict": True},
-      )
+.. code-block:: python
+
+   from graphistry.compute.gfql.ir.compilation import GraphSchemaCatalog
+   catalog = GraphSchemaCatalog.from_schema_parts(
+       node_columns={"id", "label__Person"},
+       edge_columns={"src", "dst", "label__KNOWS"},
+       metadata={"strict": True},
+   )
 
 3. **Process-wide environment variable** — the canary toggle.
 
-   .. code-block:: bash
+.. code-block:: bash
 
-      export GRAPHISTRY_GFQL_STRICT_SCHEMA=true
+   export GRAPHISTRY_GFQL_STRICT_SCHEMA=true
 
-   Truthy values: ``1``, ``true``, ``yes``, ``on`` (case-insensitive).
-   Falsy / unset: anything else (default ``false``).
+Truthy values: ``1``, ``true``, ``yes``, ``on`` (case-insensitive).
+Falsy / unset: anything else (default ``false``).
 
 When more than one tier opts in, strict applies. Monotonic widening:
 
@@ -172,13 +181,14 @@ Stage adoption from the least invasive control to the most specific:
    (for example a request handler that should never accept unknown
    identifiers), prefer the explicit preflight surface:
 
-   .. code-block:: python
+.. doc-test: skip
+
+.. code-block:: python
 
-      result = g.gfql(query, validate=True)
+   result = g.gfql(query, validate=True)
 
-   This is more readable than the binder param and runs structured
-   diagnostics. Use it for code that wants strict regardless of catalog or
-   env.
+This is more readable than the binder param and runs structured diagnostics.
+Use it for code that wants strict regardless of catalog or env.
 
 Rolling back the rollout gate is always safe: clear the env var or remove the
 catalog flag; loose mode returns immediately on the next bind. The explicit