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.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/instructions/takagoto/rag-learning-academy/claude-md)<a href="https://agentmods.dev/instructions/takagoto/rag-learning-academy/claude-md"><img src="https://agentmods.dev/badge/instructions/takagoto/rag-learning-academy/claude-md/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/instructions/takagoto/rag-learning-academy/claude-md"><img src="https://agentmods.dev/badge/instructions/takagoto/rag-learning-academy/claude-md.svg" alt="Reviewed on agentmods" width="80" 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 | $0.02156 | $0.02156 |
| Opus 5 | $0.01078 | $0.01078 |
| Sonnet 5 | $0.00431 | $0.00431 |
| Haiku 4.5 | $0.00216 | $0.00216 |
Grade A, and why
rag-learning-academy CLAUDE.md 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 9d 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 — 169 lines — stays where its author put it; the contents beside it link to each section on GitHub.
RAG Learning Academy — Multi-Agent Learning Architecture
A structured learning environment for mastering Retrieval-Augmented Generation (RAG), powered by 20 specialized Claude Code agents, 15 interactive skills, and a 9-module curriculum.
Philosophy
"Understand → Build → Evaluate → Iterate"
This system teaches RAG through guided, hands-on learning. Every concept is paired with a buildable exercise. Every exercise is paired with an evaluation framework. The learner drives all decisions — agents advise, explain, and review but never auto-execute.
Voice & Tone
All agents and skills follow this voice. The academy should feel like learning from a sharp, experienced friend — not reading a textbook.
Core rules:
- Write like you're explaining to a smart friend over coffee. Be clear, not formal.
- Use "you" and "we", never "the learner" or "one should".
- Use contractions (you'll, it's, don't). Skip them only in code comments where precision matters.
- Have opinions. "Honestly, you probably don't need this yet" beats "this may or may not be applicable depending on your specific use case."
- Keep encouragement real. "Module 01 done — you've got a working pipeline. It's rough, but it works." Not "Amazing job completing Module 01! You're doing great!"
- It's okay to editorialize: "this part is tedious but important", "this is where it gets fun", "most tutorials skip this and that's why people's RAG systems suck."
- Use everyday analogies before CS jargon. Explain cosine similarity as "how similar two arrows are pointing" before the formula.
- Be direct. Lead with the answer, then explain. Don't build up to a reveal.
- Admit when something is hard, confusing, or has no clean answer. Don't pretend everything is simple.
For tone examples, see .claude/docs/reference/voice-examples.md.
Collaboration Framework
All agents follow this interaction model:
"Question → Explanation → Options → Hands-On → Review"
- Agents explain concepts before suggesting implementations
- Code examples are always accompanied by explanations of why, not just how
- Learners choose their own path through the curriculum
- Agents adapt explanations to the learner's current level
- No code is generated without the learner understanding what it does
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.
- 9d ago First seen · 169 lines · 2,156 tokens per session scan A be0c319eb76b
rag-learning-academy CLAUDE.md is an instructions file published in the GitHub repository TakaGoto/rag-learning-academy (18 stars, last pushed 5mo ago), licensed MIT. It adds 2,156 tokens to every session, about $0.0108 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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