Lecture to Notes

Turn a YouTube, Bilibili, or X/Twitter lecture video into a complete, compilable .tex note set and a rendered PDF.

Dependencies

Check before starting (use which). Prompt the user to install any missing tools.

Additional Python dependencies: Pillow (pip install Pillow) only for the optional /ABSOLUTE/PATH/TO/lecture-to-notes/assets/smart_crop.py experiment.

LaTeX package check (do NOT skip — which xelatex alone is insufficient)

which xelatex passing does not mean the required LaTeX packages are installed. On minimal TeX installs (e.g. MacTeX Basic, TeX Live scheme-basic), the binary exists but ctex, tcolorbox, and other packages are missing. This causes silent failures: long Chinese lines overflow \textwidth (no CJK line breaking) or compilation aborts with File '...' not found.

Check required packages before starting:

MISSING=0
for pkg in ctex tcolorbox environ trimspaces listings hyperref booktabs float subcaption etoolbox; do
  if ! kpsewhich "$pkg.sty" >/dev/null 2>&1; then
    echo "❌ Missing LaTeX package: $pkg"
    MISSING=1
  fi
done
if [ "$MISSING" -ne 0 ]; then
  echo "Install missing packages:"
  echo "  tlmgr install ctex tcolorbox environ trimspaces etoolbox"
  echo "Or install full TeX distribution:"
  echo "  macOS:  https://www.tug.org/mactex/  (~4 GB, includes everything)"
  echo "  Linux:  sudo apt install texlive-full"
fi

If ctex cannot be installed (e.g. due to l3kernel version conflicts on minimal installs), the notes-template.tex includes a fallback that uses XeTeX's built-in CJK line breaking (\XeTeXlinebreaklocale "zh") with system fonts — no ctex or xeCJK needed.

YouTube Cookie Notice

YouTube may require authentication to avoid bot detection. When yt-dlp fails with "Sign in to confirm you're not a bot", add --cookies-from-browser chrome (or safari/firefox/edge) to all yt-dlp commands.

Goal

Produce a professional Chinese lecture note from a YouTube, Bilibili, or X/Twitter video URL. The output must:

• use the video's actual teaching content, not just subtitle transcription
• place the video's original cover image on the front page
• include selected full-frame slide figures chosen by contact-sheet review
• achieve high information density — every figure, box, and paragraph earns its space
• be structurally organized with \section{} / \subsection{}
• end with a synthesis section combining speaker's conclusions and your own distillation
• be a complete .tex from \documentclass to \end{document}
• compile successfully to PDF

Non-negotiable quality bar (Codex / production mode)

STOP and do not claim completion unless every item below is true. Skeleton outlines, image-heavy PDFs with thin prose, or missing intermediate artifacts are automatic failures.

Mandatory workdir artifacts (must exist on disk before saying "done")

Density and structure gates (hard)

For a lecture of duration $T$ minutes (from metadata):

Gate vs target (do not confuse): the numbers below are hard gates (fail = no delivery). A high-quality Codex-class note often lands ~25–40% above the CJK gate; that is a target, not a second hard floor. Raising the hard floor to the measured gold value encourages padding, not more teaching atoms. (Validated on CS336 L3: $T\approx 89$ → gate CJK 6246 / figs 25; gold ~8035 CJK / 27 figs / 31 pages.)

1. Chinese character count in notes.tex body (CJK unified ideographs only) must be at least $\max(5000,\ \mathrm{round}(70\times T))$.
   Example: 86 min → ≥ 6020 CJK chars. Do not count English jargon, LaTeX commands, or captions alone.

2. Figures: at least $\max(20,\ \mathrm{round}(T/3.5))$ distinct teaching figures (86 min → ≥ 25). Prefer one figure per teaching atom, not one per half-hour.

3. Sections: major \section count ≥ 8 for $T\ge 60$; every major section ends with \subsection{本章小结} that restates mechanisms (not a single vague sentence).

4. Judgment boxes: for $T\ge 60$, at least 12 of importantbox / knowledgebox / warningbox / practicebox combined. Thin notes often pass CJK by repeating captions; boxes force explicit claims, pitfalls, and checklists.

5. Teaching atom template (required for each non-trivial concept):

   • judgment / why it matters (2–5 Chinese sentences);
   • mechanism with display math when needed;
   • symbol list (itemize explaining every symbol) immediately after each display formula;
   • verified figure + same-page time footnote;
   • importantbox / knowledgebox / warningbox / optional practice checklist as needed.

6. No outline-only sections. If a 5-minute span of the lecture introduces a distinct mechanism, number, or design choice, it must appear as its own subsection or a clearly labeled paragraph with evidence — not a bullet in a summary list.

7. Coverage audit before compile (timeline): walk the timeline in 3–5 minute steps using the SRT; list any gap without corresponding prose or figure; fill gaps before xelatex.

8. Teaching-atom checklist (topic coverage, hard for technical lectures): Before delivery, extract a lecture-specific atom list from SRT + slides (15–40 atoms). Each atom must map to a subsection, a labeled paragraph, or a figure+caption in notes.tex. Missing atoms → expand prose; do not “pass” on CJK alone. Example atoms for an LLM-architecture lecture: Pre/Post-Norm, LN vs RMSNorm, FLOPs≠runtime, SwiGLU, serial vs parallel block, RoPE, FFN ratio, head dim, width/depth, vocab size, dropout vs weight decay, z-loss, QK-Norm, Prefill vs Decode, MQA/GQA, sliding window / interleaved / hybrid attention. Persist the checklist as teaching_atoms.tsv (atom\tstatus\tevidence) when practical.

9. Mechanism density (hard — L2 gold comparison 2026-07-12): CJK/fig/box gates alone are not enough. CS336 L2 first Grok pass: 5463 CJK / 8 formulas / 0 code (gate PASS, gold incomplete); after claim-driven补全: ~6450 CJK / 31 formulas / 3 code / 33 pages (matches Codex). Also require:

   • Display math: at least $\max(10,\ \mathrm{round}(T/4))$ blocks (\[ ... \] or equation), each with an immediate symbol itemize when ≥2 symbols appear.
   • Numerical claims file numerical_claims.tsv (header claim\tvalue\tsource_time\tin_notes): before writing, extract every concrete number/formula from SRT+selected frames (e.g. 6PT, 12 bytes/param, I_*=295, 144 days, 53.3B, peak TFLOP/s, bandwidth). After writing, every row must be in_notes=yes. Topic-only atoms without numbers still FAIL if the lecture stated a number.
   • Code: if slides/SRT show ≥1 non-trivial code fragment (einops, timing, AMP, …), notes must include ≥1 lstlisting (or equivalent verbatim) with the same mechanism, not a prose paraphrase only.
   • Comparison tables: if the lecture contrasts ≥3 formats/ops/modes (e.g. FP32/FP16/BF16/FP8), include a compact tabular — do not leave it only as prose.
   • Per-section floor (for $T\ge 60$): each major \section except title/appendix must have ≥ $\max(300,\ \mathrm{round}(0.7\times \mathrm{CJK}/N_{\mathrm{sec}}))$ Chinese chars and at least one of: display formula, table, code block, or ≥2 judgment boxes. A section that is only “术语澄清 + 小结” is FAIL.
   • Fine-grained subsections: for each 5–8 min teaching span that introduces a distinct mechanism, prefer a dedicated \subsection (Codex L2: 42 subs vs thin notes ~35). Merging is OK only if the merged subsection still contains all numbers and derivations from both spans.

10. Write-from-claims, not write-from-outline (workflow): Order of work after frames/manifest: (a) build numerical_claims.tsv + topic teaching_atoms.tsv; (b) draft section outline keyed to claims; (c) write prose that discharges every claim; (d) run density + formula + claims gates; (e) only then compile. Skipping (a) is the main root cause of “gate PASS, gold incomplete”.

Forbidden shortcuts

• Stopping after a “skeleton PDF” that only titles topics and pastes slides.
• Writing captions from section titles without reading the full-resolution frame.
• Claiming high density without measuring CJK character count against the gate above.
• Omitting figure_manifest.tsv / figure_verification.txt “because figures look fine”.
• Merging unrelated teaching points to reduce page count.
• Padding CJK with repeated slogans or synonym paraphrases that add no mechanism, formula, or decision rule.
• Treating the target (~Codex measured density) as a second hard gate and stuffing filler to hit it.
• Summary-only mechanism sections: describing Roofline / MFU / $6BP$ / checkpointing in words while omitting the lecture’s actual formulas, critical constants, and worked numerical examples.
• Marking a teaching atom ok because a topic word appears, when the lecture’s number or derivation is absent.

Platform Detection

Detect the platform from the URL:

Adapt the acquisition workflow accordingly (see below).

For X/Twitter, preserve the exact input URL throughout acquisition. Do not shorten, canonicalize, or remove an optional /video/<n> suffix.

Workflow

Working Directory Convention

Resolve the absolute assets directory from the loaded SKILL.md before running helpers. Then substitute that literal directory for /ABSOLUTE/PATH/TO/lecture-to-notes/assets everywhere below before executing a command. Each fenced command may run in a fresh shell, so never rely on a path variable defined by an earlier command.

for helper in \
  "/ABSOLUTE/PATH/TO/lecture-to-notes/assets/video_source.py" \
  "/ABSOLUTE/PATH/TO/lecture-to-notes/assets/check_srt_health.py" \
  "/ABSOLUTE/PATH/TO/lecture-to-notes/assets/clean_subs.py" \
  "/ABSOLUTE/PATH/TO/lecture-to-notes/assets/correct_srt.py" \
  "/ABSOLUTE/PATH/TO/lecture-to-notes/assets/llm_correct_srt.py" \
  "/ABSOLUTE/PATH/TO/lecture-to-notes/assets/verify_figures.py" \
  "/ABSOLUTE/PATH/TO/lecture-to-notes/assets/prepare_cover.sh" \
  "/ABSOLUTE/PATH/TO/lecture-to-notes/assets/notes-template.tex" \
  "/ABSOLUTE/PATH/TO/lecture-to-notes/assets/whisper_prompts/nju_os.txt" \
  "/ABSOLUTE/PATH/TO/lecture-to-notes/assets/whisper_prompts/glossary_nju_os.json"; do
  test -e "$helper" || { echo "Missing installed helper: $helper" >&2; exit 1; }
done

CRITICAL: Always use absolute paths for background commands (Whisper, video download). Claude Code's shell resets the working directory between commands. Background tasks that use relative paths will write output to the wrong location.

Recommended naming: <course_id>_<lecture_number>_<short_title>/

• Example: nju_os_01_intro/, nju_os_02_app_view/, tamu_biegler_nlp/

Phase 1: Source Acquisition

1a. Offline Platform Detection and Bilibili Part Selection

python3 "/ABSOLUTE/PATH/TO/lecture-to-notes/assets/video_source.py" detect "<URL>"

This detection is offline. If it prints bilibili, enumerate the parts before any metadata, subtitle, audio, thumbnail, or video acquisition. This discovery command must not use --no-playlist, because the playlist is the information being inspected:

yt-dlp --flat-playlist --print "%(playlist_index)s\t%(title)s" "<URL>"

STOP and ask the user which part(s) to process. Do not continue until the selection is known. For each selected part, use a part-specific Bilibili URL such as https://www.bilibili.com/video/<BV_ID>?p=<n>, create a separate working directory and run the entire workflow there, and replace <URL> below with that part-specific URL before the metadata probe. Process multiple selected parts as separate runs.

1b. Metadata Inspection

python3 "/ABSOLUTE/PATH/TO/lecture-to-notes/assets/video_source.py" probe "<URL>" > metadata.json

Extract: platform, title, uploader, duration, thumbnail availability, and subtitle languages. For X/Twitter, pass the full original <URL> including optional /video/<n>; /ABSOLUTE/PATH/TO/lecture-to-notes/assets/video_source.py retains that URL and probes it with --no-playlist.

1c. Subtitle Acquisition (Four-Stage Fallback: Manual CC → Automatic Captions → Whisper → Visual-Only)

Stage 1 — Manual CC subtitles:

# YouTube
yt-dlp --no-playlist --write-subs --sub-langs "zh.*,en.*" --convert-subs srt --skip-download "<URL>"

# Bilibili
yt-dlp --no-playlist --write-subs --sub-langs "zh-Hans,zh-CN,zh,ai-zh" --convert-subs srt --skip-download "<URL>"

# X/Twitter manual caption tracks (keep the full input URL)
yt-dlp --no-playlist --write-subs --sub-langs "all,-live_chat" --convert-subs srt --skip-download -o "x_caption.%(id)s.%(ext)s" "<URL>"

Stage 2 — Automatic captions (when no manual CC):

# YouTube
yt-dlp --no-playlist --write-auto-subs --sub-langs "en" --convert-subs srt --skip-download "<URL>"
# IMPORTANT: Clean duplicates — YouTube auto-subs repeat every line 2-3x
python3 "/ABSOLUTE/PATH/TO/lecture-to-notes/assets/clean_subs.py" subs.en.srt --stats

# X/Twitter automatic caption tracks (keep the full input URL)
yt-dlp --no-playlist --write-auto-subs --sub-langs "all,-live_chat" --convert-subs srt --skip-download -o "x_caption.%(id)s.%(ext)s" "<URL>"

X/Twitter caption acceptance gate (mandatory):

After conversion, the deterministic template produces language-tagged candidates such as x_caption.<id>.<lang>.srt. Enumerate every candidate and run the structural health check with the duration extracted from metadata.json:

# X_CAPTION_HEALTH_BLOCK
DURATION="$(python3 -c 'import json,sys; print(json.load(sys.stdin)["duration"])' < metadata.json)"
find . -maxdepth 1 -type f -name 'x_caption.*.srt' -print > x_caption_candidates.txt
if [ ! -s x_caption_candidates.txt ]; then
  echo "No X caption candidates; continue with Whisper fallback."
else
  while IFS= read -r srt; do
    if python3 "/ABSOLUTE/PATH/TO/lecture-to-notes/assets/check_srt_health.py" "$srt" --duration "$DURATION"; then
      echo "Structurally healthy candidate: $srt"
    else
      echo "Rejected structurally unhealthy candidate: $srt"
    fi
  done < x_caption_candidates.txt
fi

For every candidate reported as structurally healthy, sample that specific track against both the audio and visible teaching content at 10%, 50%, and 90% of the runtime. Record the three results per candidate. Select exactly one track only after all three checks align, and persist the explicit choice:

printf '%s\n' "<one explicitly accepted x_caption path>" > selected_x_caption.txt

Do not select a merely structurally healthy track. If there are no candidates, or no candidate passes all three semantic samples, use X audio → Whisper → the existing dictionary and LLM SRT correction passes below.

External official captions may be used only when all of the following are documented:

• identity proof that the caption source and X post contain the same lecture and video variant
• constant-offset alignment using one fixed time shift, never independent per-segment shifts
• three-point audio/visual validation at 10%, 50%, and 90% after applying that offset
• provenance disclosure naming the external caption URL/provider and the applied offset

Put an external official track under the deterministic x_caption.<id>.<lang>.srt naming scheme and apply the same structural and semantic gates; provenance never bypasses validation.

Stage 3 — Whisper speech-to-text (when captions are absent or rejected):

yt-dlp --no-playlist -x --audio-format wav -o "audio.%(ext)s" "<URL>"
# IMPORTANT: Use absolute paths for Whisper to avoid working directory issues
# when running in background. The shell may reset cwd between commands.
# IMPORTANT: ffmpeg must be on PATH — Whisper uses it internally to read audio.
WORKDIR="$(pwd)"
whisper "$WORKDIR/audio.wav" --model small --language zh \
  --output_format srt --output_dir "$WORKDIR" --fp16 False \
  --initial_prompt "$(cat "/ABSOLUTE/PATH/TO/lecture-to-notes/assets/whisper_prompts/nju_os.txt")"  # Optional: domain glossary

Whisper initial_prompt (strongly recommended for technical lectures): Point --initial_prompt at a plain-text file enumerating domain terms (syscalls, APIs, speaker names, course-specific jargon). See /ABSOLUTE/PATH/TO/lecture-to-notes/assets/whisper_prompts/nju_os.txt for a working example. This dramatically reduces same-sound errors like "PASSNAME" instead of "pathname" or "SAM" instead of "sum".

Post-Whisper SRT correction passes:

# Stage A — fast dictionary-level fix (wrong → right pairs)
python3 "/ABSOLUTE/PATH/TO/lecture-to-notes/assets/correct_srt.py" audio.srt \
    -g "/ABSOLUTE/PATH/TO/lecture-to-notes/assets/whisper_prompts/glossary_nju_os.json" --stats

# Stage B — slow LLM + multimodal fix (uses Claude Code CLI, no API key needed)
python3 "/ABSOLUTE/PATH/TO/lecture-to-notes/assets/llm_correct_srt.py" \
    --srt audio.srt --frames frames/ --out corrected.srt \
    --context "南京大学操作系统原理，讲师 jyy"

Stage A is essentially free and catches 80% of wrong characters. Stage B is expensive (one Claude call per ~90s of audio) and only worth running for notes you plan to publish.

Stage 4 — Visual-only mode (when audio quality is unusable): Skip subtitles. Use dense frame sampling (fps=1) and rely entirely on visual content.

1d. Video and Cover Download

# Cover image (may be webp/png/jpg depending on platform)
yt-dlp --no-playlist --write-thumbnail --skip-download -o "cover" "<URL>"
# Convert to jpg for xelatex compatibility
bash "/ABSOLUTE/PATH/TO/lecture-to-notes/assets/prepare_cover.sh" .

# Video (for frame extraction)
yt-dlp --no-playlist -f "bestvideo+bestaudio/best" --merge-output-format mp4 -o "video.mp4" "<URL>"

# Bilibili 1080P+ (if user has logged in):
# yt-dlp --no-playlist --cookies-from-browser chrome -f "bestvideo+bestaudio/best" -o "video.mp4" "<URL>"

For every X/Twitter thumbnail, audio, and video command above, <URL> must be the unchanged input URL, including an optional /video/<n>, and --no-playlist must remain present. The same rule applies to X metadata probing as described in Phase 1b.

Phase 2: Frame Extraction and Full-Frame Selection

Use dense extraction, contact-sheet review, and full-frame verification:

Stage 1: Dense frame extraction by chapter

mkdir -p frames
# Extract 1 frame every 15 seconds per chapter
ffmpeg -ss <start> -to <end> -i video.mp4 -vf "fps=1/15" frames/ch<N>_%03d.png

Stage 2: Frame selection (no automatic cropping)

Use the original full frames directly. Do NOT apply automatic cropping — heuristic-based cropping is unreliable (misidentifies blackboard content as "low information" regions).

Future direction: Use multimodal LLM (e.g., Claude Vision API) to classify frames and decide cropping per-frame. For now, use full frames and select the best ones manually via contact sheet review.

Stage 3: Contact sheet review

# Generate contact sheets per chapter (keeps montage size manageable)
# For chapters: montage per chapter directory
magick montage frames/ch<N>_*.png -tile 5x -geometry 384x216+2+2 contact_ch<N>.png

# For unchaptered videos with many frames (>100):
# Split into batches of 50 to avoid montage failures
for i in $(seq 1 50 $(ls frames/*.png | wc -l)); do
  ls frames/*.png | tail -n +$i | head -50 | xargs magick montage \
    -tile 5x -geometry 384x216+2+2 contact_batch_${i}.png
done

Review contact sheets to select the best frames. Criteria:

• Pick the final fully-populated state of progressive reveals
• Prefer the frame with the most complete and readable information
• Drop repetitive or low-information frames
• Keep every frame that teaches something distinct

Stage 4: Figure Verification (CRITICAL — prevents figure-text mismatch)

Problem: ~40% of figures in early versions had mismatched timestamps, captions, or surrounding text. Root cause: timestamps were estimated from frame numbers, captions were written from "what should be here" rather than "what IS here", and content was inferred from section structure rather than verified against the actual frame.

Before writing ANY figure into LaTeX, perform this three-way verification:

For each candidate figure:

1. Compute exact timestamp: chapter_start_seconds + (frame_number - 1) × 15

2. Cross-reference with subtitle: Find the Whisper/CC subtitle entry at that timestamp. Read 2-3 subtitle lines before and after. This tells you what the speaker is ACTUALLY saying at the moment of that frame.

   # Quick lookup: what was being said at timestamp T?
   import re
   target_sec = 285  # example: 4:45
   for entry in srt_entries:
       if abs(entry.start - target_sec) < 15:
           print(f"{entry.start}s: {entry.text}")
   

   Copy

3. Read the frame at full resolution: Use the Read tool to view the actual frame image. Identify the ACTUAL text/diagram/code shown on screen — not what you think should be there.

4. Three-way match check: Verify that ALL THREE align:

   • ✅ Frame visual content (what the slide/screen actually shows)
   • ✅ Subtitle content (what the speaker is saying at that moment)
   • ✅ Your caption + surrounding text (what you plan to write)

   If any mismatch: either pick a different frame, adjust the timestamp, or rewrite the caption.

5. Persist audit artifacts (mandatory):

   • Append every accepted figure to figure_manifest.tsv with columns figure, frame, start, end, topic (topic in Chinese, concrete).
   • Run verify_figures.py on all start times and save full stdout to figure_verification.txt. Do not delete these files after compile.

Common failure modes to watch for:

Phase 3: Writing

Teaching Content Rules

Include: title, chapters, on-screen diagrams/formulas/tables/code, subtitle explanations, speaker emphasis.

Exclude: greetings, small talk, sponsorship, channel logistics, 一键三连, 关注投币, closing pleasantries.

Preserve: speaker's closing discussion when it carries teaching value (synthesis, limitations, advice, open questions).

Writing Rules

1. Chinese by default unless user requests otherwise.

2. Organize with \section{} / \subsection{}. Reconstruct the teaching flow — don't mirror subtitle order.

3. Start from /ABSOLUTE/PATH/TO/lecture-to-notes/assets/notes-template.tex. Fill metadata and replace the body block.

4. Front page cover: video's original cover image, visually distinct from in-body figures.

5. Figures: use full frames. Use as many figures as needed for teaching clarity — do not optimize for a low count. Every figure MUST pass the Stage 4 three-way verification before being written into LaTeX. Never write a caption from section context alone — always read the actual frame first.

6. No figures inside boxes. importantbox, knowledgebox, warningbox must not contain \includegraphics.

7. Math: display math $$...$$ followed immediately by a symbol explanation list.

8. Code: wrap in lstlisting with descriptive caption.

9. Box strategy — no quota, use as many as the teaching signal demands:

   • importantbox: core concepts, definitions, key mechanisms, theorem-like statements
   • knowledgebox: background, history, design tradeoffs, terminology, analogies
   • warningbox: common mistakes, hidden assumptions, pitfalls, causal confusions

10. Every major \section ends with \subsection{本章小结}. Add \subsection{拓展阅读} when worthwhile.

11. Final section \section{总结与延伸}:

    • Speaker's substantive closing (no sign-off fluff)
    • Your structured distillation of core claims and mechanisms
    • Cross-section synthesis, conceptual compression
    • Concrete takeaways, open questions, next steps

12. No [cite] placeholders.

Figure Time Provenance

Every figure from a video frame must have a same-page footnote with the source time interval:

\begin{figure}[H]
\centering
\includegraphics[width=\textwidth]{figures/fig_xxx.png}
\caption{描述\protect\footnotemark}
\end{figure}
\footnotetext{视频画面时间区间：00:12:31--00:12:46。}

• Time intervals come from subtitle alignment, not chapter-level guesses.
• Use [H] or stable placement to keep figure and footnote on the same page.

Visualization

For concepts that screenshots and prose can't explain clearly, add visualizations:

• LaTeX-native: TikZ / PGFPlots
• Pre-generated: Python matplotlib scripts

Use for: process flows, architecture layouts, scaling-law plots, comparison charts. No decorative graphics.

Phase 4: Compilation, Density Gate, and Delivery

xelatex -interaction=nonstopmode notes.tex && xelatex -interaction=nonstopmode notes.tex

Pre-delivery density check (run and report numbers)

python3 - <<'PY'
from pathlib import Path
import re, json
tex = Path("notes.tex").read_text(encoding="utf-8")
cn = sum(1 for c in tex if "\u4e00" <= c <= "\u9fff")
figs = len(list(Path("figures").glob("*")))
secs = len(re.findall(r"\\section\{", tex))
boxes = len(re.findall(r"\\begin\{(important|knowledge|warning|practice)box\}", tex))
maths = len(re.findall(r"\\\[|\\begin\{equation", tex))
codes = len(re.findall(r"\\begin\{lstlisting\}", tex))
tables = len(re.findall(r"\\begin\{tabular", tex))
print(f"CJK_chars={cn} figures={figs} sections={secs} boxes={boxes} display_math={maths} code={codes} tables={tables}")
meta = json.loads(Path("metadata.json").read_text()) if Path("metadata.json").exists() else {}
T = float(meta.get("duration") or 0) / 60.0
need_cn = max(5000, round(70 * T)) if T else 5000
need_fig = max(20, round(T / 3.5)) if T else 20
need_box = 12 if T >= 60 else 6
need_math = max(10, round(T / 4)) if T else 10
print(f"duration_min={T:.1f} need_CJK>={need_cn} need_figures>={need_fig} need_boxes>={need_box} need_math>={need_math}")
ok = cn >= need_cn and figs >= need_fig and boxes >= need_box and maths >= need_math
print("PASS" if ok else "FAIL — expand mechanisms / formulas / figures / boxes before delivery")
atoms = Path("teaching_atoms.tsv")
if atoms.exists():
    rows = [r for r in atoms.read_text().splitlines()[1:] if r.strip()]
    missing = [r for r in rows if len(r.split("\t")) < 2 or r.split("\t")[1] != "ok"]
    print(f"teaching_atoms total={len(rows)} missing={len(missing)}")
    if missing:
        print("FAIL — teaching atom gaps remain"); ok = False
claims = Path("numerical_claims.tsv")
if claims.exists():
    rows = [r for r in claims.read_text().splitlines()[1:] if r.strip()]
    miss = [r for r in rows if "yes" not in r.split("\t")[-1].lower()]
    print(f"numerical_claims total={len(rows)} missing_in_notes={len(miss)}")
    if miss:
        print("FAIL — numerical claims not discharged:"); ok = False
        for r in miss[:12]:
            print(" ", r)
elif T >= 45:
    print("FAIL — numerical_claims.tsv missing (required for T>=45)"); ok = False
print("OVERALL", "PASS" if ok else "FAIL")
PY
test -s figure_manifest.tsv && test -s figure_verification.txt && test -s audio.srt

If the script prints FAIL, keep writing (add teaching atoms, formulas, numerical claims, and verified figures). Do not hand the user a PDF that fails this gate.

Delivery Checklist

• notes.tex + two-pass notes.pdf
• cover.jpg
• figures/ with semantic names; count passes density gate
• figure_manifest.tsv and figure_verification.txt present and non-empty
• audio.srt (and audio_corrected.srt when correction was applied or as a copy of the final track)
• CJK character count reported and ≥ gate for lecture duration
• Judgment-box count ≥ 12 when $T\ge 60$
• Display-math count ≥ $\max(10,\mathrm{round}(T/4))$ with symbol lists
• numerical_claims.tsv complete (in_notes=yes for every row) when $T\ge 45$
• Code/table present when lecture showed code or multi-way format comparisons
• Timeline coverage audit done (no multi-minute teaching gaps without prose)
• Teaching-atom checklist reviewed (teaching_atoms.tsv; atoms require numbers when lecture had numbers)
• Whisper-generated SRT retained if speech-to-text was used
• X/Twitter SRT health result and 10% / 50% / 90% semantic samples (if X captions were used)
• Absolute paths of PDF and workdir listed for the user

Assets

• /ABSOLUTE/PATH/TO/lecture-to-notes/assets/notes-template.tex: LaTeX template
• /ABSOLUTE/PATH/TO/lecture-to-notes/assets/video_source.py: YouTube / Bilibili / X/Twitter URL detection and metadata probe
• /ABSOLUTE/PATH/TO/lecture-to-notes/assets/check_srt_health.py: Structural health gate for downloaded X/Twitter SRT tracks
• /ABSOLUTE/PATH/TO/lecture-to-notes/assets/clean_subs.py: YouTube auto-subtitle deduplication
• /ABSOLUTE/PATH/TO/lecture-to-notes/assets/correct_srt.py: Whisper SRT dictionary-level fix (fast, data-driven)
• /ABSOLUTE/PATH/TO/lecture-to-notes/assets/llm_correct_srt.py: Whisper SRT LLM + multimodal segment-level fix (slow, uses Claude Code CLI — no API key needed)
• /ABSOLUTE/PATH/TO/lecture-to-notes/assets/verify_figures.py: Three-way figure verification (timestamp × subtitle × frame)
• /ABSOLUTE/PATH/TO/lecture-to-notes/assets/prepare_cover.sh: Cover image format conversion (webp/png → jpg)
• /ABSOLUTE/PATH/TO/lecture-to-notes/assets/smart_crop.py: Slide-region detector; optional and experimental, while production uses full frames
• /ABSOLUTE/PATH/TO/lecture-to-notes/assets/whisper_prompts/nju_os.txt: Whisper --initial_prompt glossary example
• /ABSOLUTE/PATH/TO/lecture-to-notes/assets/whisper_prompts/glossary_nju_os.json: Dictionary of wrong → right pairs for /ABSOLUTE/PATH/TO/lecture-to-notes/assets/correct_srt.py