diff --git a/skills/core/hyperframes.md b/skills/core/hyperframes.md
index b56378e2..3902eae1 100644
--- a/skills/core/hyperframes.md
+++ b/skills/core/hyperframes.md
@@ -288,54 +288,76 @@ Same pattern as Remotion.
 Things the upstream docs don't warn about but will cost you a 60-minute
 render to discover. Fix at author time, not at render time.
 
-### Video elements need explicit size, in BOTH HTML attrs and `!important` CSS
+### Full-frame background video: source-resolution trap (NOT a framework bug)
 
-HyperFrames runtime applies inline `style="width:...px; height:...px"` to
-every `<video>` element based on the video's **intrinsic** dimensions.
-That inline style beats any class-selector CSS you write, so even a
-`video.bg-video { width: 100%; height: 100% }` rule gets silently ignored
-and the video renders at whatever size the decoded stream reports —
-usually a small centered box inside an otherwise-black clip.
+Symptom: background video renders as a small centered box with black
+around it, even though you've told HyperFrames the clip is 1920×1080.
+Six renders of debugging revealed this is **almost always a source
+quality issue, not a HyperFrames framework bug.** Fix the input, not
+the CSS.
 
-The bug is invisible in some scenarios:
-- Low `filter: brightness(0.25-0.35)` makes the small video box look
-  mostly black.
-- Fullscreen typography layered on top hides the problem.
-- `object-fit: cover` on a class selector does NOT fix it because the
-  inline width/height wins first.
+Root cause (observed on Pexels + Pixabay stock): many free stock
+clips ship at 640×360 or 960×540 even when you ask for the "large"
+size tier. If your pre-transform does
+`scale=1920:1080:force_original_aspect_ratio=decrease,pad=1920:1080:...`
+you'll produce a 1920×1080 file whose visible content is a centered
+640×360 rectangle with black padding. HyperFrames plays this clip
+faithfully — the small-video-in-black-frame you see is a correctly
+rendered letterboxed input.
 
-It becomes obvious the moment you go footage-forward (brightness > 0.5,
-lower-third typography) — the small centered video box appears against
-a black frame that was supposed to be full-bleed b-roll.
+Diagnostic first: before blaming CSS, run
+`ffprobe -v error -select_streams v -show_entries stream=width,height`
+on the source clip and on your pre-transformed clip. If the pre-
+transform already shows a 640×360 active region inside 1920×1080
+padding, the problem is upstream.
 
-**Correct pattern** for a full-frame background video:
+Fix the pre-transform to scale-to-COVER and crop, not scale-to-FIT
+and pad:
+
+```
+-vf "scale=1920:1080:force_original_aspect_ratio=increase,\
+     crop=1920:1080,unsharp=3:3:0.6"
+```
+
+This upscales the small clip to at least cover 1920×1080, crops the
+overflow, and sharpens (unsharp) to offset perceived softness. The
+output is full-bleed and HyperFrames will render it edge-to-edge.
+
+The canonical wrapper-div pattern (`patterns.md`) is still the right
+HTML shape for backgrounds:
 
 ```html
-<video
-  class="clip bg-video"
-  data-start="0" data-duration="8" data-track-index="0"
-  src="assets/hero.mp4"
-  muted playsinline preload="metadata"
-  width="1920" height="1080"
-></video>
+<div style="position:absolute;top:0;left:0;width:1920px;height:1080px;overflow:hidden;">
+  <video
+    data-start="0" data-duration="60" data-track-index="0"
+    src="..." muted playsinline
+    style="width:100%;height:100%;object-fit:cover;"
+  ></video>
+</div>
 ```
 
-```css
-video.bg-video {
-  position: absolute !important; inset: 0 !important;
-  width: 100% !important; height: 100% !important;
-  object-fit: cover !important;
-  /* filter optional */
-}
-```
+Use it because `overflow: hidden` on the wrapper crops gracefully when
+the video's aspect differs slightly from 16:9, and because
+`object-fit: cover` on the inner `<video>` handles the (rare) case
+where source aspect isn't 16:9. It is **not** a workaround for a
+framework layout bug — the framework will size `<video class="clip">`
+correctly too if the source file has content filling the full frame.
 
-Both the explicit `width="1920" height="1080"` HTML attrs AND the
-`!important` CSS are required. Either one alone isn't enough.
+Apply visual treatments (`filter`, `border-radius`, etc.) on the
+wrapper or on a scoped selector like `.bg-slot video { filter: ... }`.
 
-Alternative: wrap the `<video>` in a `<div class="clip">` and put
-`data-start`/`data-duration` on the wrapper. Let the video fill its div
-via `position: absolute; inset: 0` without the framework's inline-style
-interference. Slightly more DOM but more robust.
+Invisible-vs-obvious failure mode: this trap hides when you stack
+dim filters (`brightness(0.25–0.35)`) or full-bleed typography on top —
+the letterbox reads as "moody darkness." It becomes obvious the moment
+you go footage-forward (brightness > 0.5, lower-third typography). If
+you're planning footage-forward from the start, probe your source
+resolutions in the asset stage.
+
+When you truly can't source HD clips, switch pipelines: use FFmpeg
+hybrid-compositing (scale-to-cover + crop + unsharp in an external
+b-roll reel, then overlay HyperFrames typography via chromakey). That's
+the pattern `projects/quantum-willow-multiverse/` settled on after six
+HyperFrames renders with 640×360 Pexels sources.
 
 ### Always preview-scrub footage-forward scenes before render
 
diff --git a/tests/tools/test_hyperframes_compose.py b/tests/tools/test_hyperframes_compose.py
index b461eb94..35655880 100644
--- a/tests/tools/test_hyperframes_compose.py
+++ b/tests/tools/test_hyperframes_compose.py
@@ -627,6 +627,171 @@ def test_decision_log_accepts_render_runtime_selection_with_both_options():
     jsonschema.validate(log, schema)
 
 
+def test_transcript_comparison_catches_literal_punctuation_leak(tmp_path):
+    """Regression: Chirp3-HD (and some other TTS engines) read literal `...`
+    as the word 'dot' in audio output. This failure is invisible to
+    volume-based audio spotchecks but ships audio that literally says
+    'dot dot dot' twelve times. The transcript_comparison check catches
+    this automatically before the video is marked pass."""
+    import json
+
+    # Real-world example: user's script had `...` everywhere for dramatic
+    # pause, Chirp read them all as "dot", transcript contains "dot dot dot"
+    # phrases the script never had.
+    script_text = (
+        "A computer just did in five minutes what would take every machine on Earth, "
+        "running since the Big Bang, ten septillion years to finish. "
+        "We may have gotten help from parallel universes."
+    )
+    transcript_data = {
+        "word_timestamps": [
+            {"word": "A", "start": 0.0, "end": 0.1},
+            {"word": "computer", "start": 0.1, "end": 0.5},
+            {"word": "just", "start": 0.5, "end": 0.7},
+            {"word": "did", "start": 0.7, "end": 0.9},
+            {"word": "in", "start": 0.9, "end": 1.0},
+            {"word": "five", "start": 1.0, "end": 1.3},
+            {"word": "minutes", "start": 1.3, "end": 1.7},
+            {"word": "dot", "start": 1.7, "end": 1.9},    # leak!
+            {"word": "dot", "start": 1.9, "end": 2.1},    # leak!
+            {"word": "dot", "start": 2.1, "end": 2.3},    # leak!
+            {"word": "what", "start": 2.5, "end": 2.8},
+            {"word": "would", "start": 2.8, "end": 3.0},
+            {"word": "take", "start": 3.0, "end": 3.3},
+            {"word": "every", "start": 3.3, "end": 3.6},
+            {"word": "machine", "start": 3.6, "end": 4.0},
+            {"word": "on", "start": 4.0, "end": 4.2},
+            {"word": "Earth", "start": 4.2, "end": 4.6},
+            {"word": "running", "start": 4.7, "end": 5.1},
+            {"word": "since", "start": 5.1, "end": 5.4},
+            {"word": "the", "start": 5.4, "end": 5.5},
+            {"word": "Big", "start": 5.5, "end": 5.8},
+            {"word": "Bang", "start": 5.8, "end": 6.2},
+            {"word": "ten", "start": 6.2, "end": 6.5},
+            {"word": "septillion", "start": 6.5, "end": 7.3},
+            {"word": "years", "start": 7.3, "end": 7.7},
+            {"word": "to", "start": 7.7, "end": 7.9},
+            {"word": "finish", "start": 7.9, "end": 8.3},
+            {"word": "dot", "start": 8.3, "end": 8.5},    # another leak
+            {"word": "We", "start": 9.0, "end": 9.2},
+            {"word": "may", "start": 9.2, "end": 9.4},
+            {"word": "have", "start": 9.4, "end": 9.6},
+            {"word": "gotten", "start": 9.6, "end": 9.9},
+            {"word": "help", "start": 9.9, "end": 10.3},
+            {"word": "from", "start": 10.3, "end": 10.5},
+            {"word": "parallel", "start": 10.5, "end": 11.0},
+            {"word": "universes", "start": 11.0, "end": 11.7},
+        ]
+    }
+    transcript_path = tmp_path / "transcript.json"
+    transcript_path.write_text(json.dumps(transcript_data), encoding="utf-8")
+
+    result = VideoCompose._compare_transcript_to_script(transcript_path, script_text)
+
+    # Must catch the punctuation leak
+    assert result["spurious_punctuation_words"], (
+        "transcript_comparison failed to detect the 'dot' leak from literal ... punctuation."
+    )
+    leak_counts = {
+        entry["word"]: entry["count"]
+        for entry in result["spurious_punctuation_words"]
+    }
+    assert leak_counts.get("dot") == 4, f"Expected 4 'dot' leaks, got {leak_counts}"
+
+    # Must produce a CRITICAL-severity issue message
+    issue_text = " ".join(result["issues"]).lower()
+    assert "tts punctuation leak" in issue_text
+    assert "not in the script" in issue_text
+
+    # Must NOT mark the transcript as matching
+    assert result["transcript_matches_script"] is False
+
+
+def test_transcript_comparison_passes_clean_audio(tmp_path):
+    """Clean audio with no punctuation leaks must NOT trigger a false
+    positive."""
+    import json
+
+    script_text = "The quick brown fox jumps over the lazy dog."
+    transcript_data = {
+        "word_timestamps": [
+            {"word": "The", "start": 0.0, "end": 0.1},
+            {"word": "quick", "start": 0.1, "end": 0.4},
+            {"word": "brown", "start": 0.4, "end": 0.7},
+            {"word": "fox", "start": 0.7, "end": 1.0},
+            {"word": "jumps", "start": 1.0, "end": 1.3},
+            {"word": "over", "start": 1.3, "end": 1.6},
+            {"word": "the", "start": 1.6, "end": 1.7},
+            {"word": "lazy", "start": 1.7, "end": 2.0},
+            {"word": "dog", "start": 2.0, "end": 2.3},
+        ]
+    }
+    transcript_path = tmp_path / "transcript.json"
+    transcript_path.write_text(json.dumps(transcript_data), encoding="utf-8")
+
+    result = VideoCompose._compare_transcript_to_script(transcript_path, script_text)
+    assert result["spurious_punctuation_words"] == []
+    assert result["transcript_matches_script"] is True
+    assert result["word_accuracy"] >= 0.9
+    # issues may still have informational content but no CRITICAL TTS leak
+    assert not any("tts punctuation leak" in i.lower() for i in result["issues"])
+
+
+def test_transcript_comparison_graceful_when_inputs_missing(tmp_path):
+    """When transcript or script is unavailable, the check should NOT
+    crash — it should record the skip in issues so the silence is visible."""
+    # No transcript
+    result = VideoCompose._compare_transcript_to_script(None, "some script text")
+    assert any("not provided" in i for i in result["issues"])
+
+    # No script
+    dummy = tmp_path / "t.json"
+    dummy.write_text('{"word_timestamps": []}', encoding="utf-8")
+    result = VideoCompose._compare_transcript_to_script(dummy, "")
+    assert any("not provided" in i for i in result["issues"])
+
+    # Transcript file missing
+    result = VideoCompose._compare_transcript_to_script(tmp_path / "nonexistent.json", "script")
+    assert any("not provided" in i for i in result["issues"])
+
+
+def test_run_final_review_includes_transcript_comparison_section(tmp_path):
+    """Regression: the `transcript_comparison` section must ALWAYS appear in
+    the final_review output — even when the caller doesn't provide a
+    transcript. A missing section = silent governance failure."""
+    import subprocess
+
+    # Build a minimal MP4 so _run_final_review can probe it.
+    mp4 = tmp_path / "out.mp4"
+    subprocess.run(
+        [
+            "ffmpeg", "-y",
+            "-f", "lavfi", "-i", "color=c=#000000:s=320x240:d=2",
+            "-f", "lavfi", "-i", "sine=frequency=440:duration=2",
+            "-c:v", "libx264", "-pix_fmt", "yuv420p",
+            "-c:a", "aac", "-shortest", str(mp4),
+        ],
+        capture_output=True, check=True, timeout=30,
+    )
+
+    review = VideoCompose()._run_final_review(
+        mp4,
+        edit_decisions={
+            "version": "1.0",
+            "renderer_family": "animation-first",
+            "render_runtime": "hyperframes",
+            "cuts": [{"id": "c1", "source": "x", "in_seconds": 0, "out_seconds": 2}],
+        },
+    )
+    assert "transcript_comparison" in review["checks"], (
+        "final_review must always include a transcript_comparison section. "
+        "When the caller doesn't provide a transcript, the section should "
+        "still appear with a 'skipped' issue entry — not be omitted."
+    )
+    tc = review["checks"]["transcript_comparison"]
+    assert any("not provided" in i for i in tc["issues"])
+
+
 def test_hyperframes_root_composition_has_data_start_and_duration(tmp_path):
     """Regression: the generated root composition was missing data-start
     and data-duration, violating the HyperFrames contract (SKILL.md table)."""
diff --git a/tools/video/video_compose.py b/tools/video/video_compose.py
index ea22a6bf..90d99596 100644
--- a/tools/video/video_compose.py
+++ b/tools/video/video_compose.py
@@ -105,6 +105,32 @@ class VideoCompose(BaseTool):
                     "edit_decisions.metadata.proposal_render_runtime."
                 ),
             },
+            "narration_transcript_path": {
+                "type": "string",
+                "description": (
+                    "Path to a word-level transcript JSON (from `transcriber` "
+                    "tool output). Optional but STRONGLY recommended: when "
+                    "combined with script_path/script_text, final_review "
+                    "runs transcript_comparison and catches TTS failures "
+                    "like 'Chirp3-HD reads ... as the word dot'. Without "
+                    "it, content-level audio bugs ship silently."
+                ),
+            },
+            "script_path": {
+                "type": "string",
+                "description": (
+                    "Path to the source narration script (plain text). "
+                    "Used by transcript_comparison to diff against the "
+                    "transcribed audio. Provide this OR script_text."
+                ),
+            },
+            "script_text": {
+                "type": "string",
+                "description": (
+                    "Inline source narration script. Used by "
+                    "transcript_comparison when a file path is unavailable."
+                ),
+            },
             "subtitle_path": {"type": "string"},
             "subtitle_style": {
                 "type": "object",
@@ -1043,7 +1069,13 @@ class VideoCompose(BaseTool):
         # --- Post-render: mandatory final self-review ---
         if render_result.success and output_path.exists():
             final_review = self._run_final_review(
-                output_path, edit_decisions, inputs.get("proposal_packet")
+                output_path,
+                edit_decisions,
+                inputs.get("proposal_packet"),
+                narration_transcript_path=inputs.get("narration_transcript_path"),
+                script_text=inputs.get("script_text") or self._read_text_file(
+                    inputs.get("script_path")
+                ),
             )
 
             # Attach final_review to the ToolResult data so the compose-director
@@ -1160,7 +1192,13 @@ class VideoCompose(BaseTool):
         # Post-render: mandatory final self-review (identical contract to the Remotion path).
         if output_path.exists():
             final_review = self._run_final_review(
-                output_path, edit_decisions, inputs.get("proposal_packet")
+                output_path,
+                edit_decisions,
+                inputs.get("proposal_packet"),
+                narration_transcript_path=inputs.get("narration_transcript_path"),
+                script_text=inputs.get("script_text") or self._read_text_file(
+                    inputs.get("script_path")
+                ),
             )
             if render_result.data is None:
                 render_result.data = {}
@@ -1214,7 +1252,13 @@ class VideoCompose(BaseTool):
 
         if render_result.success and output_path.exists():
             final_review = self._run_final_review(
-                output_path, edit_decisions, inputs.get("proposal_packet")
+                output_path,
+                edit_decisions,
+                inputs.get("proposal_packet"),
+                narration_transcript_path=inputs.get("narration_transcript_path"),
+                script_text=inputs.get("script_text") or self._read_text_file(
+                    inputs.get("script_path")
+                ),
             )
             if render_result.data is None:
                 render_result.data = {}
@@ -1353,11 +1397,156 @@ class VideoCompose(BaseTool):
     # Final self-review — mandatory post-render inspection
     # ------------------------------------------------------------------
 
+    # Punctuation/SSML-leak words that should NEVER appear in rendered audio.
+    # When a TTS engine reads a literal "..." as the word "dot", or a "—" as
+    # "hyphen", those leak into the transcript. Catching these in the final
+    # review is the difference between catching a bad voice render in-tool
+    # vs. shipping a video that says "dot dot dot" twelve times. CRITICAL.
+    _TTS_PUNCTUATION_LEAK_WORDS = {
+        "dot", "dots", "ellipsis", "period", "periods",
+        "comma", "commas", "semicolon", "colon",
+        "dash", "hyphen", "emdash", "endash",
+        "parenthesis", "bracket", "brace",
+        "asterisk", "slash", "backslash",
+        "exclamation", "question mark",
+    }
+
+    @staticmethod
+    def _read_text_file(path: str | Path | None) -> str | None:
+        """Read a small text file if given a path; None-safe and exception-safe."""
+        if not path:
+            return None
+        try:
+            return Path(path).read_text(encoding="utf-8")
+        except Exception:
+            return None
+
+    @classmethod
+    def _tokenize(cls, text: str) -> list[str]:
+        """Split text into comparable word tokens (lowercased, punctuation
+        stripped, numeric-word-aware). Empty tokens dropped."""
+        import re
+
+        # Preserve hyphenated words as single tokens ("many-worlds" -> "many-worlds").
+        # Drop everything except letters, digits, hyphens, apostrophes.
+        cleaned = re.sub(r"[^A-Za-z0-9\-' ]+", " ", text.lower())
+        return [t for t in cleaned.split() if t and t != "-"]
+
+    @classmethod
+    def _compare_transcript_to_script(
+        cls,
+        transcript_path: Path,
+        script_text: str,
+    ) -> dict[str, Any]:
+        """Compare a word-level transcript against the source script.
+
+        Purpose: catch TTS failures that look fine on audio-volume/duration
+        checks but produce garbage content. The canonical example is
+        Chirp3-HD reading ellipses ("...") literally as the word "dot" — our
+        volume check says "narration present, not clipped" and the video
+        ships. This check diffs the actual transcribed audio against what
+        was supposed to be said, and flags:
+
+        - Spurious punctuation-leak words ("dot", "comma", "hyphen", etc.)
+          that appear in audio but not script → CRITICAL
+        - Overall word-accuracy ratio against script → SUGGESTION if < 0.9
+
+        Returns the transcript_comparison section of final_review, or a
+        placeholder with an issue describing why the check couldn't run
+        (missing transcript, missing script) so the review never goes
+        silently quiet on this contract.
+        """
+        result: dict[str, Any] = {
+            "transcript_matches_script": False,
+            "word_accuracy": None,
+            "script_word_count": 0,
+            "transcript_word_count": 0,
+            "spurious_punctuation_words": [],
+            "issues": [],
+        }
+
+        if not transcript_path or not Path(transcript_path).is_file():
+            result["issues"].append(
+                "transcript_comparison skipped: narration_transcript not provided"
+            )
+            return result
+        if not script_text:
+            result["issues"].append(
+                "transcript_comparison skipped: script_text not provided"
+            )
+            return result
+
+        try:
+            transcript_data = json.loads(Path(transcript_path).read_text(encoding="utf-8"))
+        except Exception as e:
+            result["issues"].append(f"transcript_comparison could not parse transcript: {e}")
+            return result
+
+        transcript_words = [
+            w.get("word", "").strip() for w in transcript_data.get("word_timestamps", [])
+        ]
+        transcript_tokens = cls._tokenize(" ".join(transcript_words))
+        script_tokens = cls._tokenize(script_text)
+
+        result["script_word_count"] = len(script_tokens)
+        result["transcript_word_count"] = len(transcript_tokens)
+
+        if not script_tokens or not transcript_tokens:
+            result["issues"].append(
+                f"transcript_comparison: empty token set "
+                f"(script={len(script_tokens)}, transcript={len(transcript_tokens)})"
+            )
+            return result
+
+        # --- Punctuation-leak detection (TTS reading literal punctuation) ---
+        script_set = set(script_tokens)
+        leak_occurrences: dict[str, int] = {}
+        for token in transcript_tokens:
+            if token in cls._TTS_PUNCTUATION_LEAK_WORDS and token not in script_set:
+                leak_occurrences[token] = leak_occurrences.get(token, 0) + 1
+
+        if leak_occurrences:
+            formatted = ", ".join(
+                f"{w!r}×{n}" for w, n in sorted(leak_occurrences.items(), key=lambda x: -x[1])
+            )
+            result["spurious_punctuation_words"] = [
+                {"word": w, "count": n} for w, n in leak_occurrences.items()
+            ]
+            result["issues"].append(
+                f"TTS punctuation leak: transcript contains {formatted} — "
+                f"these words are NOT in the script, which means the voice "
+                f"engine is reading literal punctuation aloud. Rewrite the "
+                f"script to eliminate the corresponding characters (ellipses, "
+                f"em-dashes, etc.) and regenerate narration."
+            )
+
+        # --- Word accuracy via set overlap (cheap & ordering-insensitive) ---
+        # We don't penalize small word-order differences or minor TTS
+        # hallucinations; we just want to know "did 90%+ of the script's
+        # content make it into the audio." Using set overlap on the script
+        # side is robust to transcription noise.
+        matched = sum(1 for t in script_tokens if t in set(transcript_tokens))
+        accuracy = matched / max(1, len(script_tokens))
+        result["word_accuracy"] = round(accuracy, 3)
+        result["transcript_matches_script"] = accuracy >= 0.9 and not leak_occurrences
+
+        if accuracy < 0.9:
+            result["issues"].append(
+                f"Low transcript-to-script match: only {accuracy:.0%} of script "
+                f"words appear in the transcribed audio ({matched}/"
+                f"{len(script_tokens)}). Narration may be truncated, mispronounced, "
+                f"or the wrong script was used."
+            )
+
+        return result
+
     def _run_final_review(
         self,
         output_path: Path,
         edit_decisions: dict[str, Any] | None = None,
         proposal_packet: dict[str, Any] | None = None,
+        narration_transcript_path: str | Path | None = None,
+        script_text: str | None = None,
     ) -> dict[str, Any]:
         """Run post-render self-review and produce a final_review artifact.
 
@@ -1706,12 +1895,24 @@ class VideoCompose(BaseTool):
 
         issues.extend(subtitle_check.get("issues", []))
 
-        # --- 6. Determine overall status ---
+        # --- 6. Transcript-vs-script comparison ---
+        # Catches content-level TTS failures (the classic "Chirp reads `...`
+        # as the word 'dot'" trap) that volume-based audio checks miss.
+        # Only runs when caller provides both the transcript and script; when
+        # skipped, issues list records that so the silence is visible.
+        transcript_comparison = self._compare_transcript_to_script(
+            Path(narration_transcript_path) if narration_transcript_path else None,
+            script_text,
+        )
+        issues.extend(transcript_comparison.get("issues", []))
+
+        # --- 7. Determine overall status ---
         critical_issues = [
             i for i in issues
             if any(kw in i.lower() for kw in [
                 "silent downgrade", "delivery promise violation",
                 "effectively silent", "ffprobe failed", "suspiciously short",
+                "tts punctuation leak",  # reading literal punctuation aloud
             ])
         ]
 
@@ -1739,6 +1940,7 @@ class VideoCompose(BaseTool):
                 "audio_spotcheck": audio_spotcheck,
                 "promise_preservation": promise_preservation,
                 "subtitle_check": subtitle_check,
+                "transcript_comparison": transcript_comparison,
             },
             "issues_found": issues,
             "recommended_action": recommended_action,