Borrowing it
Nothing to install: this file belongs to TakaGoto/rag-learning-academy. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/TakaGoto/rag-learning-academy/main/.claude/skills/start/SKILL.mdgit clone --depth 1 https://github.com/TakaGoto/rag-learning-academyWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/takagoto/rag-learning-academy/start)<a href="https://agentmods.dev/skills/takagoto/rag-learning-academy/start"><img src="https://agentmods.dev/badge/skills/takagoto/rag-learning-academy/start.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00017 | $0.01895 |
| Opus 5 | $0.00009 | $0.00948 |
| Sonnet 5 | $0.00003 | $0.00379 |
| Haiku 4.5 | $0.00002 | $0.00189 |
Grade A, and why
start scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 162 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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?
Python (default) — Best RAG ecosystem. LangChain, LlamaIndex, ChromaDB, RAGAS, sentence-transformers all native. Every exercise works out of the box.
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.
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.
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.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 162 lines · 17 tokens per session scan A ae0bad3e7ab6
start is a skill published in the GitHub repository TakaGoto/rag-learning-academy (18 stars, last pushed 5mo ago), licensed MIT. It adds 17 tokens to every session and 1,895 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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