NUAA VOD Summarizer

Use this skill for NUAA Feitian Cloud Classroom pages. Both URL formats are supported:

https://ft.nuaa.edu.cn/jy-application-vod-he-ui/#/video-detail?id=118467
https://ft.nuaa.edu.cn/jy-application-resourcemanage-ui/#/play-center?teclId=118690

The API base (/jy-application-vod-he or /jy-application-resourcemanage) is auto-detected from the URL.

Assume commands run from this skill root. Otherwise use the absolute path to scripts/.

Core Boundary

• Let scripts handle browser login reuse, API discovery, replay metadata extraction, subtitle export, and artifact writes.
• Let the agent handle transcript diagnosis, terminology correction, semantic reconstruction, and final Markdown prose.
• Deterministic seed notes must not masquerade as finished course notes.
• Do not call a note final when subtitles/transcripts are missing, empty, or obviously partial.

Main Commands

Probe one lesson (quick test)

python scripts\extract_nuaa_vod.py "<video-detail-url>" --output-dir "<output-dir>" --browser-runtime-auth --manual-wait-seconds 60

Extract ALL transferable lessons (batch)

python scripts\extract_nuaa_vod.py "<video-detail-url>" --output-dir "<output-dir>" --browser-runtime-auth --manual-wait-seconds 60 --batch --skip-existing

Extract a specific lesson by ID

python scripts\extract_nuaa_vod.py "<video-detail-url>" --output-dir "<output-dir>" --subtitle-course-id 1547370 --manual-wait-seconds 10

Output Structure

<output-dir>/
  course.json                          # course-level metadata
  replay_inventory.json                # full lesson list with transfer flags
  raw/
    page.json                          # parsed URL info
    captured_responses.json            # all captured API responses
    storage_state.json                 # browser storage state
    responses/                         # individual response files
  lessons/
    <lesson_id>/
      metadata.json                    # lesson metadata
      raw/
        subtitle.json                  # raw subtitle API response
        transcript.txt                 # cleaned transcript with timestamps
      semantic_rebuild/
        semantic_rebuild_input.json    # packet for semantic rebuild
        semantic_rebuild_prompt.md     # prompt template
      note.md                          # generated Markdown note (after rebuild)

Supported Platforms

Two UI/API pairs are supported, auto-detected from the URL:

Known API families (both platforms share similar paths under their API base):

• /v1/subject_vod_list — lesson list (vod-he)
• /v1/group_subject_vod_list — lesson list (resourcemanage)
• /v1/course_vod_videoinfos — lesson video metadata
• /v1/course_vod_urls — playback URLs
• /v1/course_vod_subtitle — AI subtitles (requires courTransferFlag=true)
• /v1/courseware/upload/list — courseware list
• /v1/courseware/downLoad — courseware download
• /v1/course/verify — course access check

Workflow

1. Open the supplied URL (video-detail or play-center) in a persistent Chromium context.
2. If needed, complete NUAA login in the browser window.
3. Capture NUAA VOD API responses while the page loads.
4. Auto-detect API base from URL markers.
5. Enumerate lessons and identify which have subtitles (courTransferFlag).
6. For each target lesson, fetch <api_base>/v1/course_vod_subtitle.
7. Save subtitle JSON and cleaned transcript per lesson.
8. Prepare semantic_rebuild/ packet only when transcript material exists.
9. Generate Markdown notes from transcripts via semantic rebuild.

Required Replay Diagnosis

Before building any note, classify each lesson into exactly one of:

1. waiting_transcript — subtitles not yet available (no courTransferFlag, or fetch returned empty)
2. partial_transcript — subtitle exists but is clearly incomplete, garbled beyond repair, or covers only a fraction of the lesson
3. transcript_only — has complete transcript but no note written yet

Downstream note logic must consume this classification instead of recomputing route decisions.

Semantic Rebuild Rules

• Perform course-alignment check before treating a note as final.
• Correct obvious ASR term errors when course context makes the intended term clear.
• Keep the lesson time axis visible. Every major section should reference a time range or coarse MM:SS-MM:SS marker.
• Keep math as $...$ or $$...$$ only (do not wrap formulas in backticks).
• Treat the course transcript as the only primary source for section boundaries, lesson mainline, and completion checks.
• Only mark a lesson final when transcript coverage and summary coverage both pass.
• Reconstruct course-specific substance. Do not substitute generic learning advice for missing semantic understanding.
• If transcript.txt is missing, empty, or near-empty, treat the replay as waiting for transcript material. Do not create a formal note from metadata, schedule, title, or courseware alone.

Authoring Contract

When writing the final student-facing Markdown note:

• You are writing the finished note, not a seed note, diagnostic note, or instruction to a future organizer.
• Read the full transcript.txt before writing. Use semantic_rebuild_input.json only as metadata, time anchors, and artifact index.
• Do not expose evidence snippets, candidate phrases, OCR fragments, raw ASR lines, or internal workflow notes.
• Every major time block should explain what teaching move happened: definition, model, argument, proof, example, comparison, case discussion, policy explanation, teacher comment, assignment, exam arrangement, or class logistics.
• Capture high-value classroom signals: exams, homework, deadlines, submission format, grading weight, reading requirements, teacher-emphasized key points, repeatedly stressed phrases, formulas, theorems, definitions, examples, and common mistakes.
• If the teacher explicitly says something is important, likely to be tested, easy to confuse, often wrong, or needs review after class — preserve it in the note.
• If transcript evidence is weak, write the item under "待核对" instead of turning it into a confident conclusion.
• The final note must face the student reader directly. Avoid phrases like "整理时应...", "后续重写...", "这一段主要在...", or other process commentary.

Final Note Quality Gate

A user-facing final note must be a semantic reconstruction, not a decorated transcript segment list. Before accepting as final, reject if it contains:

• Raw ASR/OCR snippets presented as "代表性表达" or representative expressions
• Headings like "课堂讲解与主题推进 1"
• Boilerplate like "整理时建议不要把这一段只当作..."
• Repeated generic advice across sections instead of course-specific content
• Transcript noise (misrecognized symbols) copied without correction
• Low-quality diagnostics marked as formal notes

If a note fails this gate, keep only extraction artifacts and semantic packet, then mark the lesson as needing semantic rebuild. Do not call it final.

Course-Domain Reconstruction Guidance

• Math and statistics: reconstruct objects, definitions, assumptions, equations, theorems, proof ideas, examples, counterexamples, symbol meanings, and links between results.
• Engineering and computer science: reconstruct system components, algorithms, design constraints, implementation steps, experiment setup, failure cases, trade-offs, and how formulas/code relate to the design.
• Humanities and social sciences: reconstruct concepts, arguments, historical/institutional background, author positions, evidence, comparisons, cases, and the teacher's evaluative emphasis.
• Ideological and political courses: reconstruct policy concepts, theoretical claims, historical context, named documents/events, value judgments, exam-oriented formulations, and examples used to explain abstract claims.
• Language, writing, and communication courses: reconstruct vocabulary, rhetorical patterns, text structure, examples, correction points, practice requirements, and teacher feedback.
• Lab, design, or project courses: reconstruct task goals, deliverables, tools, operation steps, data requirements, safety/format constraints, grading criteria, and troubleshooting advice.

Transcript-Only Rule

When a lesson is classified as transcript_only:

• Do NOT emit fake content templates (e.g., "课程定位 / 基础概念 / 方法流程").
• Let scripts provide only time segments from the transcript, representative transcript lines, and transcript overview.
• Let the agent infer the real lesson structure from the transcript plus course context.
• Do not ask scripts to pre-confirm concepts from transcript-only material.

PPT / Courseware Rule

• Prefer teacher audio/subtitle stream by default.
• Treat PPT and courseware as auxiliary only, even when available.
• PPT may help with term spelling, page/book titles, formula symbols, and logistics screenshots.
• PPT must not decide section boundaries, lesson mainline, concept generation, or completion state.

Failure Rules

• If authentication fails or no VOD API responses are captured, keep raw/page.json and explain that a logged-in browser session is required.
• If subtitle data is absent for a lesson, mark it as no-subtitle and skip.
• If subtitle text is empty or near-empty, do not produce a final note.
• If transcript coverage is clearly partial, keep only a diagnostic draft rather than a final note.
• Treat courseware/PPT as auxiliary only; it must not replace transcript evidence.
• If session reuse fails, rerun with --browser-runtime-auth.

On Windows, prefer PowerShell commands. For inline Python, use:

@'
print("hello")
'@ | python -