Find the truth.
Trace controls, permissions, business rules, and outcomes. Separate working behavior from plans and assumptions.
A LEARNING SKILL FOR CODEX v2.0.1
Turn your repository
into clear learning.
Neckbeard is a Codex skill that creates onboarding,
task tutorials, and technical guides from your code.
For end users, administrators, and developers.
FROM IMPLEMENTATION TO INDEPENDENCE01 — THE FIRST USEFUL TASK
Repository evidence
↳ interface / actions
↳ permissions / rules
↳ states / outcomes
Inspect what actually exists.
A path someone can follow
Your first successful task.
- 01 Know where to begin.
- 02 Take the next clear action.
- 03 Recognize the result.
Understand what changed. Know what to do next.
ILLUSTRATIVE LEARNING STRUCTURE Evidence in. Understanding out.
01 / THE APPROACH
Teach a real task.
Show a clear result.
Neckbeard reads the implementation, traces a real task, and designs the learning around it. The goal is a person who can do something useful—and recover when something goes wrong.
Make a path.
Start with a meaningful goal. Put prerequisites, explanations, and practice in the order people need them.
Check the steps.
Verify workflows where the environment allows. Make the remaining uncertainty visible to maintainers.
02 / A PATH FOR EACH PERSON
Same product.
Different starting points.
Choose a perspective to see how the learning changes.
THE END-USER PATH
“Help me get something done.”
A first-task walkthrough with exact controls, expected feedback, and a way back when you make a mistake.
EXAMPLE REQUEST
Use $neckbeard to teach a new participant how to submit their first proposal.
Orientation → Walkthrough → Practice
03 / BUILT AROUND YOUR NEEDS
Start small.
Add what helps.
Start with end-user onboarding and practice, or choose another combination. Core is always included. Your selection changes the setup command; nothing is installed from this page.
Core
Repository orientation, evidence checks, and task tutorials.
Choose any combination. No selection gives you core only.
04 / MAKE IT YOURS
Your edition.
Your next steps.
Requires Git, Python 3.9+, and Codex configured
to load your chosen skills directory.
Not available to clone yet. This repository has not been published to GitHub. You can explore your edition here; the clone step will work after publication.
TERMINAL
# 1. Get the repository once published
git clone https://github.com/Seth-arc/neckbeard.git
cd neckbeardpython3 skills/neckbeard/scripts/install.py --target ~/.codex/skills --components end-user,practiceCore + 2 components
What happens next?
- After publication, download the repository using the clone command.
- Run the installer in your terminal. It copies your edition into your Codex skills directory.
- Open Codex with access to the product repository, then ask Neckbeard for a learning guide.
What access and tools does it need?
Neckbeard inspects files you make available to Codex. A running application helps verify interface workflows. Voice generation, screen recording, and editing require separate compatible tools; those services may have their own costs and permissions.
What if I already have Neckbeard installed?
Run the new distribution’s installer with --action upgrade. It backs up the previous installation, preserves the selected edition and organization configuration, and refuses changes to other managed files. Use --action uninstall for a reversible removal.
Can I use it without audio or video tools?
Yes. Written guides, tutorial scripts, and storyboards do not require media production tools. Finished recordings depend on the tools available to Codex.
Already installed? Use --action upgrade from the new distribution. Your previous installation is backed up.
A NOTE ON TRUST
Clarity includes
knowing the limits.
Code inspection is evidence. Running the workflow is stronger evidence. Neckbeard keeps the difference explicit.
Media production needs compatible recording, voice, and editing tools. The media component provides direction and templates; it does not bundle those tools. Without them, it prepares the script and production package.
No claim of learning effectiveness without evidence from learners.