vect-G

vect-G / lecture-to-hw

Public

A Codex skill that turns course lectures, homework files, classroom code, and previous solution style into concise Markdown submissions.

ai-agent codex codex-skill multi-agent student-tools
42
3
69% credibility
Found May 09, 2026 at 42 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
AI Summary

A skill for an AI assistant that automates generating concise, student-style Markdown homework solutions from course materials like lectures, assignments, and past work.

How It Works

1
🔍 Discover lecture-to-hw

You hear about a handy AI skill that turns messy class notes and homework files into ready-to-submit answers.

2
➕ Add it to your AI helper

You simply place the skill folder into your AI assistant's collection so it's always ready when you need it.

3
📂 Open your course folder

You go to the folder holding your homework PDFs, lecture slides, code examples, and past assignments.

4
💬 Give the command

You tell your AI friend, 'Use lecture-to-hw to finish this homework as a neat Markdown file!' and it springs into action.

5
✨ Watch it work its magic

Your AI scans all the files, matches problems to class lessons, solves step by step, checks for mistakes like a teacher, and builds a student-like answer.

6
👀 Review the result

You get a clean Markdown file with a confidence score, suggested checks, and notes on what it did.

✅ Homework done, time saved

You quickly tweak if needed, submit your polished work, and enjoy extra time for coffee or studying what matters.

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Star Growth

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AI-Generated Review

What is lecture-to-hw?

lecture-to-hw is a Codex skill that automates turning messy classroom folders—full of lecture slides, homework PDFs, DOCX files, notebooks, images, and past solutions—into clean, submission-ready Markdown homework. Drop it into your codex skills directory as a codex skill installer, navigate to a course folder, and trigger it with a simple command like "use lecture-to-hw to finish this homework." It handles multi-format inputs and spits out concise answers styled like a real student's work, complete with confidence scores and review flags.

Why is it gaining traction?

It stands out in the codex skills library by chaining lectures to solutions via multi-agent workflows: a controller agent decomposes tasks, spawns up to four parallel subagents for independent problems or code runs, and runs a TA-style review to catch errors, AI smells, or lecture mismatches. Users love how it learns your past homework style for headers, detail levels, and brevity, plus flags uncertainties in formulas or images—instead of hallucinating. As a codex skills example on GitHub, it hooks devs building codex GitHub integrations or exploring codex skills GitHub for classroom automation.

Who should use this?

College CS students grinding low-stakes homework in data science, algorithms, or ML courses, especially those with cluttered dirs of slides and code demos. Devs tinkering with codex GitHub apps, codex skills folder setups, or codex GitHub code review flows for teaching assistants automating grading. Anyone evaluating codex skills list for multi-agent classroom tools.

Verdict

Worth cloning into your codex skills GitHub setup if you're in the ecosystem—solid docs, bilingual READMEs, and MIT license make it an easy codex skills.md experiment, despite 42 stars signaling early maturity. Low 0.699999988079071% credibility score means test on toy assignments first; pair with high-reasoning models for best results.

(198 words)

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