name: assignment-solver description: Solve one assignment problem file using a single endpoint with two logical tracks: (A) Math/Physics PDF problem solving (lecture slides/lecture notes as ground truth) and (B) code artifact generation. Save a final answer with detailed explanation next to the problem file.

Assignment Solver

Goal

Produce a high-quality solution artifact for exactly one assignment problem file, using a single endpoint with track-specific instructions:

• Track A (Math/Physics PDF Solver): strict ground-truth-based solving with lecture slides/lecture notes.

• Track B (Code Results Generator): generate code artifacts and companion explanations.

• Required outcome: final answer + detailed explanation

• Default output: Markdown file

• Optional output: a non-Markdown format only when the user explicitly requests it

• If the requested output is code (or a folder containing code), also generate a companion Markdown document summarizing assignment overview, evidence, concepts used, and sources from lecture slides/assignment materials

Inputs

• Problem file (required): one file containing the assignment question
• Reference paths (optional): zero or more files/folders (lecture slides, textbook notes, course folders, etc.)
• Output format (optional): file extension such as md, py, js, java, txt

If output format is not explicitly requested, use md.

Track Selection (Single Endpoint)

After extracting the problem text, the skill must choose exactly one track:

1. Use Track A (Math/Physics PDF Solver) when:
   • The problem file is a .pdf, AND
   • The extracted problem content indicates a math/physics question or concept (e.g., equations, variables, units, derivations, “Problem/Exercise” style statement), AND
   • The user intent is to solve/explain/derive (e.g., “풀이”, “해설”, “계산”, “유도”).
2. Use Track B (Code Results Generator) when:
   • The user explicitly requests code/scripts (e.g., “write code”, “implement”, “generate script”, or the requested output format is a code file extension), OR
   • The extracted problem is primarily about software/tooling/code output rather than a math/physics derivation.
3. Ambiguity handling:
   • Prefer Track A when the extracted problem text contains clear equations/units/derivation structure.
   • Otherwise prefer Track B.

Procedure

1. Validate inputs.

• Ensure exactly one problem file is selected.
• Ensure the problem path exists and is a file.

1. Extract the problem file text and gather context from local references when provided.

• Extract the problem file text.
  • If the problem file is .pdf, .pptx, or .ppt, use the bundled extractor at scripts/extract_reference_text.py resolved relative to this SKILL.md.
  • For other problem file types, read the text directly.
• Read directly mentioned reference files.
• For referenced folders, recursively scan and prioritize relevant text-like files first.
• When a lecture or reference file is .pdf, .pptx, or .ppt, use the bundled extractor at scripts/extract_reference_text.py resolved relative to this SKILL.md.
• The bundled extractor already includes the required runtime modules under vendor/; do not install packages in the assignment/project folder at execution time.
• Format routing is fixed:
  • .pdf -> bundled pypdf
  • .pptx -> bundled extractor's standard-library ZIP/XML parser
  • .ppt -> bundled olefile + binary PowerPoint text-record parser
• Include PDF/PPTX/PPT sources when extractable.
• Prefer relevance over volume: select files by filename/path keywords and problem-topic matching.

1. Decide track and fill knowledge gaps (track-specific).

• First, apply the track decision rules in ## Track Selection (Single Endpoint).
• Track A (Math/Physics PDF Solver):
  • Ground truth priority: treat the project files’ lecture slides and lecture notes/transcripts as the primary ground truth for any concept/formula/definition used in the answer.
  • In ## 상세 풀이, always explain the most relevant chapter's core concepts and formulas first, then substitute/apply them to the specific given problem, with a detailed step-by-step 풀이.
  • Professional terms/concepts mentioned in the lecture slides/assignment must be kept in English exactly as written; explain them only in Korean.
  • If the needed ground-truth chapter/material is not found locally, clearly state what is missing and either ask for additional reference files or use web research only as a clearly labeled fallback with explicit uncertainty.
  • If web research is used, reflect it in ## 출처 링크 and clearly separate it from local ground truth in ## 참고 근거 (로컬 파일/섹션).
• Track B (Code Results Generator):
  • Use local references first; if absent or insufficient, web research is allowed.
  • Combine gathered evidence with generally known domain knowledge and explicit reasoning.

Bundled Lecture Material Extraction

Use the following command pattern for bundled lecture material extraction:

python3 "<skill-dir>/scripts/extract_reference_text.py" "<reference-file>"

Rules:

• Resolve <skill-dir> as the directory containing this SKILL.md.
• Supported bundled formats are only .pdf, .pptx, and .ppt.
• Treat .ppt extraction as text-focused best effort. If the extracted text is sparse or noisy, say so in the final artifact and compensate with other references or web research.
• If a file in one of these formats fails extraction, report the failure explicitly instead of silently skipping it.

Optional PDF page OCR (agent judgment)

When bundled text extraction (scripts/extract_reference_text.py) is insufficient for a .pdf, the agent may run page-level OCR using the separate tool below. Do not run OCR by default; use it only when justified.

When to consider OCR

• Extraction raises no extractable text found in PDF, or
• Per-page extracted text is clearly too sparse for the visible content (e.g. scan/image-only slides, figures with critical labels), or
• The problem statement clearly depends on content that is unlikely to be text-layered.

Command pattern

python3 "<skill-dir>/scripts/extract_pdf_page_ocr.py" --page N "<path-to.pdf>"
# or multiple pages / ranges (1-based):
python3 "<skill-dir>/scripts/extract_pdf_page_ocr.py" --pages "1,3-5" "<path-to.pdf>"
# optional: tesseract language(s), default is eng+kor
python3 "<skill-dir>/scripts/extract_pdf_page_ocr.py" --page N --lang eng "<path-to.pdf>"

Rules

• Resolve <skill-dir> as the directory containing this SKILL.md.
• OCR output is less reliable than native PDF text; treat it as approximate.
• In the solution artifact, label OCR-derived content explicitly (e.g. in ## 참고 근거 (로컬 파일/섹션) or ## 가정 및 불확실성): note OCR, page number(s), and uncertainty (misread symbols, equations, diagrams).
• If OCR cannot run (missing tesseract, renderer, or languages), report the error message to the user and fall back to asking for alternate materials or other allowed strategies—do not invent PDF content.

Runtime prerequisites (not bundled; see vendor/README.md): tesseract on PATH; plus either Poppler (pdftoppm) or an importable PyMuPDF (fitz) in the Python environment used to run the script.

Non-Bundled Format Extraction (delegate to md-convert)

The bundled extractor handles .pdf / .pptx / .ppt only. For any other reference format — .docx, .xlsx, .html, .epub, images, audio, ZIP, YouTube URL, etc. — delegate to the sibling md-convert skill instead of hand-rolling an extractor:

python3 agent-skills/skills/md-convert/scripts/convert.py "<reference>" --stdout

Use the returned Markdown as a reference source under ## 참고 근거 (로컬 파일/섹션). Do not use md-convert for .pdf / .pptx / .ppt — the bundled extractor is primary for those to avoid pip-install requirements at execution time. If md-convert fails (see agent-skills/skills/md-convert/references/gotchas.md for setup issues), report the failure and continue with other available references.

1. Build the solution content.

• Detect and follow the language of the problem file.
• Provide the final answer first, then step-by-step explanation.
• If information is incomplete, continue with explicit assumptions and uncertainty notes.
• Track A (Math/Physics PDF Solver) writing rules:
  • In ## 상세 풀이, use the explicit flow: 핵심 개념 -> 핵심 공식/Equations -> 대입/적용 -> detailed step-by-step 풀이.
  • When writing equations in Track A, use LaTeX math blocks in the requested formats: $...$ (single-line/inline) or $$ + newline + equation + newline + $$ (multi-line block).
  • Keep lecture-assigned professional terms/concepts in English exactly as written; explain them only in Korean.
  • Prefer local evidence for each concept/formula; when showing a formula, briefly indicate the source ground-truth evidence in ## 참고 근거 (로컬 파일/섹션) (no fabricated citations).
• Track B (Code Results Generator) writing rules:
  • If the output is code, include the code blocks and a brief run/use note when applicable.

1. Decide output filename and save.

• Let stem be the problem file basename without extension.
• Let ext be requested format; if missing or invalid, fall back to md.
• Base filename: <stem> sol.<ext> in the same folder as the problem file.
• Collision handling: if file exists, create <stem> sol (2).<ext>, then (3), etc.

1. Enforce output structure.

• For md, use the required Markdown structure below.
• For non-md, generate the requested format artifact.
• If the requested format is code or a code-containing folder, also generate a companion Markdown file next to the output (base name: <stem> sol notes.md, with the same collision handling rule).
• The companion Markdown must organize references and sources specifically from lecture slides and assignment materials.

Required Markdown Structure

When output format is md, include sections in this order:

1. ## 과제 개요
2. ## 최종 답안
3. ## 상세 풀이
4. ## 참고 근거 (로컬 파일/섹션)
5. ## 출처 링크 (only if web research was used)
6. ## 가정 및 불확실성

Track A (Math/Physics PDF Solver) additional requirement:

• In ## 상세 풀이, start with the relevant chapter's key concepts and formulas first, then apply/substitute them to the specific given problem, and include detailed step-by-step derivation. Professional terms/concepts must keep their English wording exactly as in the lecture slides.

Companion Markdown for Code/Folder Outputs

When the output is code (for example py, js, java, etc.) or a folder containing code, create an additional Markdown file with these sections in order:

1. ## 과제 개요
2. ## 참고 근거 (강의 슬라이드/과제 자료)
3. ## 사용된 개념 설명
4. ## 출처 (강의 슬라이드/과제 자료)

Rules:

• Prefer local lecture slides and assignment handouts/materials as primary evidence.
• If a referenced source was not used directly, do not list it.
• Keep mappings explicit: concept -> which slide/material supports it.

Output Contract

• Summary: what was solved and which strategy was used (local references, web, or mixed)
• Artifact: exact saved file path(s) (include companion Markdown path when created)
• Validation: note whether filename collision handling was applied
• Follow-up: suggest additional checks only when materially helpful

Guardrails

• Process exactly one problem file per invocation.
• Do not silently skip uncertainty; state assumptions and confidence limits.
• Do not fabricate local citations; only cite files/sections actually used.
• Do not change output format unless explicitly requested by the user.