physics-lesson-capture

name: physics-lesson-capture description: Capture lesson content from images/PDF/DOCX, run OCR (DeepSeek-OCR), extract example questions, summarize classroom discussion, and prepare lesson-first post-class diagnostics. Use when teachers provide lesson materials or discuss classroom content.

Physics Lesson Capture

Overview

Use this skill when the teacher provides lesson materials (images/PDF/DOCX) or describes classroom content. The goal is to OCR/parse the materials, extract example questions, and produce a structured lesson summary and discussion notes that can drive post-class diagnostics and homework.

Required Inputs

• lesson_id (e.g., L2403_2026-02-04)
• lesson topic (string)
• class (optional)
• sources: images, PDF, or DOCX files
• optional teacher notes: classroom misconceptions, emphasis KPs, homework focus

Workflow (Lesson-First)

1. Ingest sources

   • Copy sources into data/lessons/<lesson_id>/sources/.
   • Record a manifest file with filenames and types (see references/lesson_io.md).

2. Extract text

   • PDF: if text-based, use pdfplumber; if scanned, render pages to images and OCR.
   • DOCX: extract text via python-docx; OCR embedded images.
   • Images: OCR directly.
   • Use DeepSeek-OCR via SiliconFlow for OCR (see references/ocr_pipeline.md).
   • Script: scripts/lesson_capture.py handles OCR + extraction end-to-end.
   • If OCR quality is low, ask the teacher for a clearer scan or export to PDF.

3. Build artifacts

   • Save raw OCR output under ocr/ and cleaned text under text/.
   • Keep page references for traceability.

4. Extract example questions

   • Split by numbering and option patterns (A/B/C/D).
   • Create examples.csv with example_id, stem_text, options, source_ref, page.
   • See references/extract_rules.md for heuristics.

5. Summarize classroom content

   • Parse teacher notes (or request confirmation) into a structured class_discussion.md.
   • Use template in references/discussion_template.md.

6. Knowledge point candidates

   • Propose KP candidates based on examples.
   • Require teacher confirmation before updating data/knowledge/knowledge_point_map.csv.

7. Lesson-first post-class diagnostic

   • If requested, call generate_postclass_diagnostic.py with --discussion-notes and --student-notes.
   • Only merge exam data when explicitly requested.

8. Write-back rules

   • Never store raw student scores here.
   • Only write confirmed summaries to mem0 using the teacher memory template.

Output Templates

• class_discussion.md (structured classroom summary)
• lesson_summary.md (lesson digest + key misconceptions)
• examples.csv (example questions index)
• lesson_review.md (visual review report for OCR + examples)

References

• references/lesson_io.md
• references/ocr_pipeline.md
• references/extract_rules.md
• references/discussion_template.md
• (Related) skills/physics-core-examples/SKILL.md

CLI (Quick Start)

python3 skills/physics-lesson-capture/scripts/lesson_capture.py \\
  --lesson-id L2403_2026-02-04 \\
  --topic \"静电场综合\" \\
  --sources /path/to/lesson.pdf /path/to/example.png \\
  --discussion-notes /path/to/class_discussion.md

Optional:

• --lesson-plan to include teacher plan (PDF/DOCX/MD)
• --force-ocr to OCR even text-based PDFs
• --ocr-mode FREE_OCR|GROUNDING|OCR_IMAGE

Review report:

python3 skills/physics-lesson-capture/scripts/lesson_review_report.py \\
  --lesson-id L2403_2026-02-04 \\
  --render-pdf

Confirm KP candidates from the review:

python3 skills/physics-lesson-capture/scripts/confirm_kp_candidates.py \\
  --lesson-id L2403_2026-02-04

Apply confirmed KP candidates to knowledge_point_map.csv:

python3 skills/physics-lesson-capture/scripts/apply_kp_to_question_map.py \\
  --lesson-id L2403_2026-02-04