review: dedupe regex, centralize column names, harden collate, more tests

* **#2 — dedupe `_PLACEHOLDER_RE`.** The same regex was compiled in
  `recipe.py` and `language_render.py`. Promote to module-level
  `PLACEHOLDER_RE` in `recipe.py` (its primary owner — declares
  template syntax) and import from `language_render.py`.
* **#3 — centralize language column names.** `io_utils.py` had
  hardcoded `{"language_persistent", "language_events"}` literals at
  two sites. Replace with `LANGUAGE_COLUMNS` import so a future column
  rename can't silently desync.
* **#4 — defensive collate preserved-keys.** `lerobot_collate_fn`
  silently filtered language fields from samples that didn't have
  them, which would hand downstream consumers a preserved list
  shorter than the tensor batch. Now: if any sample carries a key,
  every sample in the batch must carry it; otherwise raise a
  `ValueError` so the upstream rendering bug surfaces at the boundary.
* **#5 — `_scalar` rejects non-singleton lists.** Previously a zero-
  or multi-element list fell through and triggered confusing
  `float([])` errors downstream. Now raises `ValueError` with the
  actual length.
* **#6 — refactor `_extract_complementary_data`.** Replace 11 lines
  of `key = {... if ... else {}}` plus an 11-line splat dict with a
  single `_COMPLEMENTARY_KEYS` tuple iterated once.
* **#7 — document `EXTENDED_STYLES`.** Was an empty `set()` with no
  comment. Add a docstring explaining it's an intentional extension
  point: downstream modules append project-local styles before
  `column_for_style` is called.
* **#9 — `tools.mdx` notes the runtime layer is future work.** The
  page referenced `src/lerobot/tools/`, `registry.py`, and
  `get_tools(meta)` — none exist in this PR. Added a callout at the
  start of "How to add your own tool" plus a note on the
  implementations paragraph.
* **#10 — tests for YAML round-trip, malformed rows, blend
  validation.** `test_recipe.py` grew from 1 case to 12 covering:
  blend-or-messages exclusivity, target-turn requirement, blend
  emptiness, weight presence/positivity, nested-blend rejection,
  `from_dict` with nested blends, `from_yaml` / `load_recipe`
  agreement, top-level non-mapping rejection. Added a malformed-row
  test for `_normalize_rows` that asserts non-dict entries raise
  `TypeError`.

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

docs/source/tools.mdx

@@ -66,9 +66,11 @@ prompt_str = tokenizer.apply_chat_template(
 )
 ```
 
-**The implementations** — runnable Python — live under
-`src/lerobot/tools/`, one file per tool. The canonical `say`
-implementation wraps Kyutai's pocket-tts model.
+**The implementations** — runnable Python — will live under
+`src/lerobot/tools/`, one file per tool. The runtime dispatcher and
+the canonical `say` implementation (wrapping Kyutai's pocket-tts) land
+in a follow-up PR; this PR ships only the catalog storage and
+fallback constant.
 
 ## Per-row tool _invocations_
 
@@ -114,6 +116,14 @@ the matching implementation.
 
 ## How to add your own tool
 
+> **Note:** Steps 2 and 3 below describe the runtime layer
+> (`src/lerobot/tools/`, the `Tool` protocol, `TOOL_REGISTRY`,
+> `get_tools(meta)`) which lands in a follow-up PR. Today (this PR
+> only), Step 1 is enough to make the tool visible to the chat
+> template via `meta.tools` so the model can learn to _generate_ the
+> call. Executing the call at inference is what the follow-up PR
+> wires up.
+
 Three steps. Concrete example: a `record_observation` tool the policy
 can call to capture an extra observation outside the regular control
 loop.