> Source URL: /unit-3/project-paths/makayla-c/makayla-c-2026-04-14.guide
# Makayla's Project Guide

**Project:** CLP Curator
**Category:** Web App (Flask) + Data Science
**Last updated:** April 14

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> Note: This guide is created based on the latest state of your project repository + any notes from our discussion. It may not always reflect the most up-to-date information.

## Where You Are

You have a strong project idea: a web app that helps Furman students find CLP events that match their interests. Your spec is in good shape -- you have a clear description, concrete MVP features, and realistic stretch goals. Your journal has a solid first entry too.

What's missing right now is the scaffolding: your project doesn't have `pyproject.toml`, dependencies, or a running app yet. That's the priority before Thursday.

## What We Talked About

We refined your MVP flow:

1. **Data pipeline** -- Scrape or collect CLP event data from the [CLP calendar](https://www.furman.edu/academics/cultural-life-program/upcoming-clp-events/), then use a script with an LLM to categorize events by topic. Export to a clean `.csv` that your web app can load.
2. **UX flow** -- Pick topics → Show list → "Add this" button
3. **Flask app** -- Show topic options, let the user pick, display a top picks list based on their choices.

Stretch: a browseable directory with links to speakers, topics, etc. that's more inspiring than the current listing.

## Next Steps (Before Thursday)

These are the things to focus on to be ready for Checkpoint 1:

1. **Scaffold your project.** Use this prompt with your agent:

   ```text
   Read my project.spec.md and the Flask setup guide at
   https://csc-121.path.app/unit-3/resources/flask-setup.guide.llm.md
   Set up my project: initialize uv, install Flask and Pandas,
   create the basic file structure, and build a minimal starting
   point I can run.
   ```

2. **Get one tiny slice working.** You don't need the full recommendation engine yet. A good first slice: a Flask page that loads your `.csv` and displays a list of CLP event titles. That proves your data-to-web pipeline works.

3. **Start your data file.** Even a manually created `.csv` with 5-10 CLP events (title, date, description, topic) is enough for now. You can automate the scraping later.

4. **Update your journal.** Write what you've done and what's next in the Checkpoint 1 section of `project.journal.md`.

5. **Commit and push.** Make sure everything is on GitHub before Thursday.

## Checkpoint 1 Readiness

By Thursday April 16th, you need:

- [x] Completed `project.spec.md`
- [ ] Project initialized with `uv` and dependencies installed
- [ ] A basic scaffolding that runs
- [ ] One tiny working feature (e.g. load and display event data)
- [ ] First journal entry in `project.journal.md`
- [ ] Everything committed and pushed to GitHub

## Helpful Resources

- [Flask Setup Guide](../../resources/flask-setup.guide.md) -- follow this to scaffold your project
- [Prompt Engineering Guide](../../resources/prompt-engineering.guide.md) -- useful when you get to the LLM categorization step
- [Checkpoint 1 Instructions](../../projects/final-project-checkpoint-1.project.md) -- the full checkpoint requirements


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## Backlinks

The following sources link to this document:

- [April 14 -- Checkpoint 1 prep](/unit-3/project-paths/makayla-c/makayla-c.path.llm.md)