# start

Walks a newcomer through picking a language and track, scoring their level, and running a first RAG pipeline in the same session.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · ★ 24
- Author: TakaGoto
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/TakaGoto/rag-learning-academy/tree/HEAD/.claude/skills/start/SKILL.md
- Updated: 2026-04-03
- HTML: https://affpapa.org/en/nevibecode/skills/takagoto-start

## What it does

The `start` skill opens a RAG course inside your coding agent. It asks which language you want to work in — Python, TypeScript, Go or Rust — then offers three tracks: Beginner, Intermediate and Advanced. Unsure which fits? It runs five questions covering programming, ML, LLM use, embeddings and search, adds up a score from 0 to 10, and names the track for you.

It then writes `progress/learner-profile.md` and `progress/module-tracker.md` so your progress survives between sessions. The last step is a scaffold: `sandbox/data.txt` with sample text, `sandbox/pipeline.[ext]` in your chosen language, and `sandbox/README.md` with run instructions. That pipeline splits the data into 200-token chunks, embeds them with all-MiniLM-L6-v2, stores vectors in an in-memory ChromaDB, and pulls the top-3 chunks for a test query with similarity scores.

The local Ollama route wants 8-16GB of free RAM. On an 8GB machine you will be closing browsers, Docker and IDEs before loading models, or you get slowdowns and crashes. Embedding models stay light; local LLMs are the demanding part, so the skill suggests mixing the two — embeddings on your machine, generation in the cloud.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add TakaGoto/rag-learning-academy --skill start -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/start/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/start/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add TakaGoto/rag-learning-academy --skill start -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/start/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add TakaGoto/rag-learning-academy --skill start -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/start/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install TakaGoto/rag-learning-academy start
```

2. **Manually: put the files in place**
- File: `.github/skills/start/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add TakaGoto/rag-learning-academy --skill start -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/TakaGoto/rag-learning-academy --path .claude/skills/start
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/start/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add TakaGoto/rag-learning-academy --skill start -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/TakaGoto/rag-learning-academy/tree/HEAD/.claude/skills/start/SKILL.md

## When to use it
- settle on a language and RAG track without second-guessing
- score your own level with five questions and get a starting module
- keep a learner profile and module progress in files
- run a working RAG pipeline in your first session
- judge whether your hardware can handle local models

## Example prompts
- Run the start skill and help me pick a RAG track
- I want to learn in TypeScript, start from the beginning
- Not sure where I fit — assess my level

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 8e0200b6e85971cd.

BEGIN THIRD-PARTY FILE 8e0200b6e85971cd
```text
---
name: start
description: "Begin your RAG learning journey — assess your level and pick a learning path"
---

# Start: Begin Your RAG Learning Journey

## Step 0: Welcome & Context

Before anything else, give a clear, casual picture of what they're about to learn. Follow the project's voice & tone (see CLAUDE.md) — conversational, direct, like a smart friend explaining something cool:

> Here's the short version: LLMs are smart but they don't know anything about *your* data. RAG fixes that. You grab relevant documents, stuff them into the prompt, and suddenly the model can answer questions about your PDFs, your codebase, your product docs — accurately, with sources.
>
> By the end of this, you'll be able to build that system yourself. Not just follow a tutorial — actually understand every piece well enough to debug it when things go wrong (and they will).

Briefly explain the learning structure: 9 modules, 3 guided projects, 17 slash commands. They'll learn by building, not just reading.

Mention the two paths: **API path** (OpenAI + Anthropic keys, computation in the cloud) or **local/free path** (Ollama, no cost but needs hardware). If they plan to use local models, add this note:

> **Heads up:** Running LLMs locally with Ollama needs 8-16GB of free RAM. If your machine has 8GB total, close heavy apps (browsers, Docker, IDEs) before running models. If you hit slowdowns or crashes, switch to the API path — local embedding models are lightweight but local LLMs are demanding. You can always mix: use local embeddings with a cloud LLM.

## Step 1: Choose Your Programming Language

Present the language options. Wait for the learner to choose before continuing.

> **What language do you want to code in?**
>
> 1. **Python** (default) — Best RAG ecosystem. LangChain, LlamaIndex, ChromaDB, RAGAS, sentence-transformers all native. Every exercise works out of the box.
>
> 2. **TypeScript** — Solid ecosystem. LangChain.js, ChromaDB client, OpenAI SDK. Most exercises translate directly; a few evaluation tools (RAGAS) need a Python fallback or manual implementation.
>
> 3. **Go** — Limited RAG tooling. No LangChain equivalent — you'll build more from scratch, which means you'll learn the internals deeply. Some exercises need adaptation.
>
> 4. **Rust** — Very limited RAG tooling. qdrant-client exists, but most RAG libraries are Python/TS. Recommended only if you're already fluent in Rust and want the challenge.
>
> Not sure? Pick Python — you can always switch later by editing your profile.

Default to Python if they skip or say "whatever."

## Step 2: Choose Your Track
```
END THIRD-PARTY FILE 8e0200b6e85971cd

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