| name | homework-grader |
| description | Use this skill whenever a user wants to grade student homework from handwritten images — whether scanned, photographed, or taken on an iPad. Triggers include: "grade these homeworks", "mark student submissions", "check student answers against the solution key", "parse handwritten homework", "score student assignments", or any request involving images of handwritten student work that should be compared to a solution key. Also trigger when the user provides multiple student image sets alongside a PDF or image solution key and wants a graded spreadsheet output. Subject areas include geophysics, earth science, math, and equations. Always use this skill when student work is handwritten and needs structured, scored grading output — even if the user just says "can you grade these?" with images attached.
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Homework Grader Skill
Grades multi-page handwritten student homework submissions against a solution key. Outputs a
consolidated Excel spreadsheet with per-question scores, partial credit, mismatch flags, and
a total score per student.
Overview of the Workflow
- Ingest the solution key (PDF or image) → parse and number all questions
- Ingest student submissions (multiple images per student) → parse, stitch pages, detect questions
- Match student answers to solution key questions (auto-detect first, then anchor to key)
- Grade each matched answer with partial credit scoring
- Flag mismatches — missing questions, unmatched answers, ambiguous numbering
- Write the output spreadsheet using the xlsx skill
Read references/parsing-strategy.md for detailed image parsing and OCR guidance.
Read references/matching-and-grading.md for question matching logic and scoring rubrics.
Read references/spreadsheet-schema.md for the exact output spreadsheet format.
Step 1 — Collect Inputs
Ask the user for the following if not already provided:
- Solution key: a PDF (typed) or image. This is the ground truth.
- Student submissions: one or more images per student, grouped by student. Ask the user how the students are identified (file name convention, folder, or manually labelled).
- Point values: Does each question have equal weight, or does the user want to specify per-question points? Default: equal weight, total = 100.
- Partial credit policy: Default policy is described in
references/matching-and-grading.md. Ask only if the user wants to override.
If the user uploads files directly in the chat, accept them. If files are on disk, they will be at /mnt/user-data/uploads/.
Step 2 — Parse the Solution Key
Follow the detailed parsing guidance in references/parsing-strategy.md, Section 1.
Quick summary:
- If PDF: extract text directly using
pdfplumber (preferred) or pymupdf. Fall back to vision if extraction yields garbled text (scanned PDF).
- If image: send directly to Claude vision with the structured extraction prompt in
references/parsing-strategy.md.
- Output: a numbered list of questions with their canonical answers, e.g.:
Q1: Define P-wave velocity. Answer: Speed of compressional waves through a medium, ~6 km/s in continental crust.
Q2: Calculate depth given two-way travel time of 1.2s and v=3000 m/s. Answer: depth = (v × t) / 2 = 1800 m
...
Store this as solution_key: list[dict] with fields q_num, q_text, answer_text, points.
Step 3 — Parse Student Submissions
For each student, process their image set in order. Follow references/parsing-strategy.md, Section 2.
Key rules:
- Treat multiple images as ordered pages of a single document. Sort by filename if numeric suffix is present (e.g.,
student1_p1.jpg, student1_p2.jpg); otherwise ask the user for page order.
- Use Claude's vision to extract all handwritten content from each image.
- Concatenate extracted text across pages before attempting question detection.
- Extract student identifier from filename or ask the user.
Output per student: raw_text: str, detected_answers: list[dict] with fields detected_q_label (what the student wrote, e.g. "Q3", "3.", "iii", or None if no label), answer_text, page_number.
Step 4 — Match Student Answers to Solution Key
Follow references/matching-and-grading.md, Section 1 for full logic.
Two-pass matching strategy:
Pass 1 — Auto-detect: Try to map detected_q_label to q_num in the solution key.
- Normalise labels: "Q3", "3.", "3)", "iii", "(3)" all →
3
- If all detected labels map cleanly and cover ≥ 70% of solution key questions → use auto-detect mapping
Pass 2 — Content anchor fallback: If auto-detect fails (missing labels, ambiguous numbering, <70% coverage):
- Use semantic similarity: compare
answer_text content to answer_text in solution key
- For math/equations: check if numerical values, variables, and formula structure match
- For geoscience: check for domain-specific keywords (velocity, impedance, reflection coefficient, etc.)
- Assign best content-match if similarity score > threshold (see
references/matching-and-grading.md)
Flag the following as mismatches:
MISSING: A solution key question has no matched student answerUNMATCHED: A student answer has no plausible match in solution keyAMBIGUOUS: Two or more student answers could match the same solution key questionREORDERED: Answer matched via content (not label) — note the original label vs. matched label
Step 5 — Grade Each Matched Answer
Follow references/matching-and-grading.md, Section 2 for rubric details.
Score each matched answer on a 0–4 scale (then scale to point value):
| Score | Meaning |
|---|
| 4 | Fully correct — key facts, values, units all present and correct |
| 3 | Mostly correct — minor error, missing unit, small numeric rounding |
| 2 | Partially correct — correct approach, significant error or missing step |
| 1 | Minimal credit — relevant attempt, mostly wrong |
| 0 | Incorrect, blank, or unmatched |
For math/equations:
- Check formula structure first, then numerical result
- Award partial credit for correct setup with arithmetic error
For geoscience/written answers:
- Check for required key concepts (listed in solution key)
- Each missing key concept deducts proportionally
MISSING questions → score 0 automatically.
UNMATCHED answers → score 0, flag for instructor review.
Step 6 — Build the Output Spreadsheet
Read /mnt/skills/public/xlsx/SKILL.md before writing the spreadsheet.
Follow the schema in references/spreadsheet-schema.md exactly.
Sheet 1: Grade Summary
- One row per student
- Columns: Student ID, Q1 score, Q2 score, … Qn score, Total Score, Total %, Flags (count of mismatches)
Sheet 2: Mismatch Flags
- One row per flagged item
- Columns: Student ID, Flag Type (MISSING / UNMATCHED / AMBIGUOUS / REORDERED), Solution Key Q#, Student Label, Notes
Sheet 3: Parsed Answers (Detail)
- One row per student × question
- Columns: Student ID, Q#, Student's Answer (transcribed), Expected Answer, Score (0–4), Points Awarded, Max Points, Grader Notes
Apply conditional formatting:
- Red fill for score = 0 in Grade Summary
- Yellow fill for REORDERED or AMBIGUOUS flags
- Orange fill for MISSING flags
Save to /mnt/user-data/outputs/homework_grades.xlsx and present to user.
Step 7 — Present Results
After writing the file, give the user a brief summary:
- Total students graded
- Class average score (%)
- Number of students with at least one mismatch flag
- Most commonly missed question (if any)
Call present_files with the xlsx path.
Error Handling
- Image too blurry to parse: Note in Parsed Answers sheet as
ILLEGIBLE, score 0, flag for manual review.
- Student wrote in a language other than expected: Flag as
LANGUAGE_MISMATCH, attempt translation, note uncertainty.
- Solution key has sub-questions (1a, 1b): Treat each sub-question as an independent question row.
- Duplicate student IDs: Warn the user and append
_dup to the second occurrence.
