annotate: address review feedback — bug fixes, docs/code drift, naming, cleanup

Bugs
  * validator: don't re-raise on unknown style. The second column_for_style
    lookup (used to route persistent vs event) now sits in try/except so an
    unknown style is recorded by _check_column_routing and skipped instead
    of crashing the whole validation pass.
  * general_vqa._target_cameras: when restrict_to_default_camera is set but
    the configured camera_key isn't one the provider exposes, warn and fall
    back to all cameras instead of returning a phantom key that KeyErrors
    deep in frame decode.
  * interjections: clamp interjection timestamps to frame_timestamps[0]
    rather than a hardcoded 0.0 (datasets can start at non-zero t).

Docs / code drift
  * annotation_pipeline.mdx: drop the phantom 'vocabulary discovery / phase
    0 / --vocabulary.* / canonical_vocabulary.json' section (none of it
    exists); describe the real describe->segment + coverage-stitch flow.
    Soften the src/lerobot/tools/ + TOOL_REGISTRY reference to 'not part of
    this PR' (matches tools.mdx, which already marks the runtime layer as
    not-yet-implemented). Fix the --push_to_hub/--new_repo_id wording. Note
    the default is now a single h200. Add a 'Contributing new modules'
    section inviting module / prompt / quality contributions.
  * executor docstring: six phases, no phantom phase 0.

run_hf_job.py
  * add the Apache 2.0 license header (was flagged repeatedly).
  * default to a single GPU: flavor=h200, parallel_servers=1, num_gpus=1
    (scale to h200x4 noted in the docstring).
  * pin the install to @main instead of the feature branch (won't break
    after merge).

Naming / cleanup
  * rename dest_repo_id -> new_repo_id across config / script / example /
    test to match the LeRobot dataset edit tools.
  * rename prompt templates module_N_*.txt -> descriptive (plan_*,
    interjections_*, vqa.txt) and update every load_prompt() call.
  * remove dead _messages_to_prompt (used only by the removed in-process
    backends).
  * declare _warned_decode_fail (frames) and _warned_no_camera (vqa) as
    real init=False dataclass fields instead of getattr monkey-patches.
  * scope bandit B607 to the two ffmpeg subprocess.run sites via
    '# nosec B607' and drop it from the global skip list.

Tests
  * fix stale canned-VLM markers ('ONE realistic interruption' ->
    'compact interjection', 'Update the memory' -> 'compressed semantic
    memory') and drop the dead 'concise hierarchical PLAN' plan responders
    (plan generation is deterministic now) in run_e2e_smoke,
    test_pipeline_recipe_render, test_modules.
  * run_e2e_smoke now asserts interjection + speech rows are produced so a
    stale marker can't silently pass again.
  * drop remaining 'PR 1' / 'PR 2' references from test comments / names.

Verified: tests/annotations + tests/datasets/test_language +
tests/scripts/test_lerobot_annotate (31 passed); make-style E2E smoke
(interjections=1 speech_atoms=2); pre-commit (ruff, mypy, bandit,
prettier) clean.

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

tests/annotations/test_modules.py

@@ -151,7 +151,7 @@ def test_module2_mid_episode_emits_paired_interjection_and_speech(
         {
             "acknowledgement the robot": {"text": "OK."},
             # Marker matches the distinctive line of
-            # ``module_2_interjection.txt`` ("Write ONE compact
+            # ``interjections_interjection.txt`` ("Write ONE compact
             # interjection ..."). Keep this in sync with that prompt's
             # wording — the canned responder matches on substring.
             "Write ONE compact interjection": {
@@ -245,7 +245,6 @@ def test_module1_attaches_video_block_to_subtask_prompt(fixture_dataset_root: Pa
             {"text": "wipe the counter", "start": 0.5, "end": 1.1},
         ]
     }
-    plan_payload = {"plan": "1. grasp\n2. wipe"}
     memory_payload = {"memory": "wiped once"}
 
     def responder(messages):
@@ -255,9 +254,7 @@ def test_module1_attaches_video_block_to_subtask_prompt(fixture_dataset_root: Pa
             for block in m.get("content", []):
                 if isinstance(block, dict) and block.get("type") == "text":
                     text = block.get("text", "")
-        if "concise hierarchical PLAN" in text:
-            return plan_payload
-        if "Update the memory" in text:
+        if "compressed semantic memory" in text:
             return memory_payload
         return payload