Homework Machine

Overview

The homework-machine skill provides an end-to-end pipeline for completing university assignments and coursework. It automates the entire workflow from requirement analysis through final deliverable assembly, including:

• Requirement Analysis — Parse assignment specifications and extract all deliverables
• Research Phase — Conduct literature search for background and methods
• Code Implementation — Write production-quality code with comprehensive tests
• Report Writing — Generate academic reports with proper citations and formatting
• Paraphrasing — Automatic anti-plagiarism via translation round-trip (macOS only)
• Deliverable Assembly — Collect all outputs with submission checklist

The workflow produces publication-ready reports, tested code, and all supporting materials needed for submission.

Arguments

• <assignment> (required) — Path to assignment PDF or plain text file containing assignment requirements
• --format (optional, default: latex) — Output report format: latex or docx
• --language (optional, default: English) — Report writing language for the academic report

Example usage:

homework-machine /path/to/assignment.pdf --format latex --language English
homework-machine "assignment_text.txt" --format docx

Setup

Before running this skill, ensure all dependencies are installed:

pip install -r "BASE_DIR/scripts/requirements.txt"

If using .docx output format, also install the Word document library:

pip install python-docx

Verify that the following helper scripts exist in BASE_DIR/scripts/:

• paper_search.py — Academic paper search and retrieval
• bibtex_utils.py — BibTeX citation management
• translate_roundtrip.py — Translation-based paraphrasing (macOS only)

Workflow

The homework-machine executes 6 phases in sequence. After Phase 1, the user must explicitly confirm the plan before proceeding.

Phase 1 — Analyze Assignment

1. Read the Assignment

   • If the input is a PDF, read it using appropriate tools
   • Paginate large PDFs (>20 pages) to avoid context overflow
   • If the input is a text file, read the entire content

2. Extract Deliverables

   • List all required outputs: academic report, source code, datasets, written answers, specific questions to address
   • Identify the format and structure expected for each deliverable

3. Identify Constraints and Grading Criteria

   • Page limits for the report
   • Specific format requirements (LaTeX template, Word template, etc.)
   • Required methods, algorithms, or frameworks
   • Expected citation count and style
   • Code submission format (single file, project structure, etc.)
   • Grading rubric if provided

4. Present the Plan to User

   • Summarize the assignment scope, deliverables, and constraints
   • Display estimated effort for each phase
   • Present the execution plan
   • Wait for explicit user confirmation before proceeding to Phase 2
   • Ask the user: "Are there any additional requirements or constraints not mentioned in the assignment that I should know about?"
   • Allow the user to add custom constraints, provide reference materials, or modify the scope

Phase 2 — Research

1. Conduct Literature Search

   • Run the research command with the assignment topic:

     python "BASE_DIR/scripts/paper_search.py" --query "<assignment-topic>" --max-results 20 --sort citations
     

   • If the assignment mentions specific papers, methods, or techniques, run additional targeted searches

2. Collect Findings

   • Extract key concepts, mathematical formulations, and algorithms from top-cited papers
   • Identify foundational methods and state-of-the-art approaches relevant to the assignment
   • Note empirical results and benchmarks that will validate the implementation
   • Document all sources for later citation

3. Organize Research Results

   • Group findings by topic area (background, method, optimization, benchmarks, etc.)
   • Prioritize papers by relevance and citation count
   • Prepare to reference these in the report's Related Work section

Phase 3 — Code Implementation

1. Write Implementation Code

   • Use Python as the default language unless the assignment specifies otherwise
   • Implement all required algorithms, data structures, and functionality per the assignment spec
   • Follow the specific requirements for methods, libraries, or frameworks mentioned in the assignment
   • Use clean, readable code with meaningful variable names
   • Add inline comments explaining the approach, key decisions, and non-obvious logic

2. Write Test Cases

   • Write comprehensive test cases that verify correctness
   • Test against expected outputs provided in the assignment
   • Test edge cases and boundary conditions
   • Ensure tests are isolated and reproducible
   • Aim for 80%+ code coverage

3. Run and Verify

   • Execute all tests and confirm they pass
   • Verify the implementation produces correct results
   • Check for runtime performance issues or memory leaks
   • Document any known limitations

4. Save Code

   • Save all source code files to the code/ directory in the output
   • Include a requirements.txt if external packages are used
   • Include a README.md with instructions to run the code and tests

Phase 4 — Report Writing

1. Draft Academic Report

   • Follow standard academic structure:
     • Title & Abstract — Concise summary of the problem and solution
     • Introduction — Problem statement, motivation, scope, and contributions
     • Related Work / Background — Review of relevant literature and methods (with citations)
     • Method / Approach — Detailed technical description of the solution with equations and pseudo-code where needed
     • Experiments / Results — Present findings, include tables, figures, and quantitative analysis
     • Discussion — Interpret results, discuss limitations, and suggest future work
     • Conclusion — Summarize key contributions and findings
     • References — Complete bibliography

2. For LaTeX Format (--format latex)

   • Use the LaTeX template provided in the Templates section below
   • Use article document class with standard packages
   • Include proper mathematical notation using amsmath
   • Ensure all figures and tables are properly referenced
   • Configure bibliography for natbib with numbered citations

3. For DOCX Format (--format docx)

   • Use the python-docx library to generate a Word document:

     from docx import Document
     from docx.shared import Pt, RGBColor
     from docx.enum.text import WD_ALIGN_PARAGRAPH
     
     doc = Document()
     doc.add_heading('Your Title', 0)
     doc.add_paragraph('Your introduction text...')
     # ... add more content ...
     doc.save('report.docx')
     

   • Use proper heading hierarchy (Heading 1 for sections, Heading 2 for subsections)
   • Format tables with borders and shading for readability
   • Insert code blocks as formatted text or images
   • Use numbered and bulleted lists appropriately

4. Fetch and Format Citations

   • For each cited paper, retrieve the BibTeX entry:

     python "BASE_DIR/scripts/bibtex_utils.py" fetch --title "<paper-title>"
     

   • Collect all BibTeX entries into references.bib
   • Ensure author names, publication years, and venues are accurate
   • Use consistent citation style (numbered for LaTeX, author-date for Word)

5. Accuracy and Depth

   • Tailor report depth to assignment level (undergrad reports are typically 8-15 pages, graduate reports 15-30 pages)
   • Match technical depth to course level
   • Ensure all claims are supported by citations or experimental results
   • Verify mathematical correctness of equations

Phase 5 — Paraphrase (macOS Only)

This phase uses Apple's built-in translation API to automatically rephrase content while preserving technical accuracy. It is only available on macOS systems with AppleScript support.

1. Generate Technical Terms File

   • Extract domain-specific terms from the assignment and report that should NOT be paraphrased:
     • Algorithm names (e.g., "QuickSort", "LSTM")
     • Method names from the assignment or code
     • Mathematical terms and notation
     • Proper nouns (author names, institution names)
     • Variable names and code identifiers
   • Save these as terms.txt in the output directory (one term per line)

2. Paraphrase Substantial Paragraphs

   • For each major paragraph in the report (excluding abstract, references, and code):

     python "BASE_DIR/scripts/translate_roundtrip.py" --input <paragraph_file> --terms-file terms.txt --diff
     

   • The script performs a round-trip translation (e.g., English → French → English) to generate varied phrasing
   • Technical terms are preserved by the terms file

3. Review and Approve

   • Display the diff for each paraphrased paragraph showing before/after versions
   • Verify that the paraphrased version:
     • Preserves technical accuracy
     • Maintains the original meaning
     • Varies sentence structure while keeping technical terms unchanged
     • Does not introduce errors in grammar or logic
   • Allow the user to accept or reject each paraphrase

4. Error Handling

   • If the script detects a non-macOS system, warn the user: "Paraphrasing requires macOS with AppleScript support. This feature will be skipped. Your report is complete but without automated paraphrasing."
   • If Apple Translate is unavailable or fails, skip this phase and continue to Phase 6
   • Save a copy of the original report before applying changes for recovery

5. Apply Approved Changes

   • Update the report with approved paraphrases
   • Save a paraphrase_diff.md showing all accepted before/after changes for reference

Phase 6 — Assemble Deliverables

1. Collect All Outputs

   • Verify the output directory structure exists: ./output/homework-machine/YYYY-MM-DD-HHMMSS/
   • Ensure all required deliverables from Phase 1 are present

2. Directory Structure

   ./output/homework-machine/2026-03-03-143025/
   ├── report.tex              (or report.docx if --format docx)
   ├── references.bib          (LaTeX) or bibliography data (docx)
   ├── code/
   │   ├── main.py
   │   ├── tests/
   │   │   └── test_main.py
   │   ├── requirements.txt
   │   └── README.md
   ├── terms.txt               (if paraphrasing ran)
   ├── paraphrase_diff.md      (if paraphrasing ran)
   └── checklist.md            (submission checklist)
   

3. Generate Submission Checklist

   • Create checklist.md with all required deliverables from the assignment
   • Mark each item as present/verified
   • Include file sizes and line counts for code files
   • Add notes on completeness and quality

4. Final Verification

   • Confirm all files are readable and properly formatted
   • Generate a summary report showing what was completed
   • Display the path to the output directory
   • Provide instructions for the user to review and submit

LaTeX Template

Use this template as the foundation for LaTeX reports. If the assignment provides a specific LaTeX template, use that instead.

\documentclass[12pt]{article}
\usepackage[margin=1in]{geometry}
\usepackage{amsmath, amssymb}
\usepackage{graphicx}
\usepackage{hyperref}
\usepackage[numbers]{natbib}
\usepackage{booktabs}

\title{Your Assignment Title}
\author{Your Name}
\date{\today}

\begin{document}

\maketitle

\begin{abstract}
Provide a concise summary of the problem, approach, and key results (150-250 words).
\end{abstract}

\section{Introduction}
\label{sec:intro}

Introduce the problem, explain its significance, and outline the scope of the work.
State the main contributions clearly.

\section{Related Work}
\label{sec:related}

Review relevant prior work and methods. Cite key papers using \cite{key}.

\section{Method / Approach}
\label{sec:method}

Describe the solution approach in detail. Include algorithms, pseudocode, and
mathematical formulations as needed.

\subsection{Algorithm Name}

Provide pseudocode or detailed steps here.

\section{Experiments and Results}
\label{sec:results}

Present experimental findings, results, and analysis.

\begin{table}[h]
\centering
\caption{Example Results Table}
\label{tab:results}
\begin{tabular}{lcc}
\toprule
Method & Accuracy & Time \\
\midrule
Baseline & 85\% & 0.5s \\
Proposed & 92\% & 0.6s \\
\bottomrule
\end{tabular}
\end{table}

\section{Discussion}
\label{sec:discussion}

Interpret the results, discuss limitations, and suggest directions for future work.

\section{Conclusion}
\label{sec:conclusion}

Summarize the key findings and contributions.

\bibliographystyle{plainnat}
\bibliography{references}

\end{document}

Output Directory

All deliverables are saved to: ./output/homework-machine/YYYY-MM-DD-HHMMSS/

The timestamp ensures each run creates a unique output directory. Contents include:

• report.tex or report.docx — The complete academic report ready for submission
• references.bib — BibTeX bibliography file (LaTeX) or embedded references (DOCX)
• code/ — Directory containing all source code:
  • main.py or language-appropriate entry point
  • tests/ subdirectory with test cases
  • requirements.txt listing dependencies
  • README.md with build and execution instructions
• terms.txt — Technical terms that were excluded from paraphrasing (if Phase 5 ran)
• paraphrase_diff.md — Before/after comparison of paraphrased sections (if Phase 5 ran)
• checklist.md — Final submission checklist verifying all deliverables

Tips

1. Always Confirm Before Heavy Work — After Phase 1 analysis, present the plan to the user and wait for explicit confirmation. This prevents wasted effort if the user's understanding of the assignment differs.

2. Match Report Depth to Assignment Level — Undergraduate assignments typically expect 8-15 page reports with 15-25 citations, while graduate assignments often require 20-40 pages with 40+ citations. Adjust accordingly.

3. Use Provided Templates — If the assignment includes a specific LaTeX template or Word template, use it instead of the default template provided here. Adapt the structure to match instructor expectations.

4. Test Code Before Writing About It — Always run the implementation code and verify tests pass before writing the report. This ensures the reported results match actual behavior and prevents embarrassing discrepancies.

5. Verify Citations — Double-check BibTeX entries for accuracy, especially author names, publication years, and venues. Incorrect citations reflect poorly on academic work.

6. Handle Paraphrasing Failures Gracefully — If the paraphrasing script fails (non-macOS system, missing dependencies, API unavailability), the report is still complete and usable. Warn the user but continue to Phase 6.

7. Request Clarification When Ambiguous — If the assignment is unclear or contradictory, ask the user for clarification in Phase 1 before proceeding.

8. Keep Code Separate from Report — Maintain clean separation between implementation code in the code/ directory and the report. Reference code in the report but don't embed large code blocks in the main text (use appendix or external files if needed).

9. Document All Assumptions — If you make assumptions about missing specifications (e.g., dataset sources, parameter choices), document them in the report's Method section or in the code README.

10. Archive Outputs — The timestamped output directory structure allows running the skill multiple times without overwriting previous work, enabling iterative refinement if needed.