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/explain/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/explain)<a href="https://agentmods.dev/skills/takagoto/rag-learning-academy/explain"><img src="https://agentmods.dev/badge/skills/takagoto/rag-learning-academy/explain.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 | $0.00014 | $0.01281 |
| Opus 5 | $0.00007 | $0.00641 |
| Sonnet 5 | $0.00003 | $0.00256 |
| Haiku 4.5 | $0.00001 | $0.00128 |
Grade A, and why
explain 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 7d 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 — 142 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Explain: Deep-Dive into Any RAG Concept
Provide a thorough, multi-layered explanation of any RAG concept. Unlike /glossary (which gives quick definitions), /explain goes deep — this is for when the learner really wants to understand something.
Language awareness: See
.claude/LANGUAGE_AWARENESS.md.
Step 1: Identify the Concept and Calibrate Depth
If the user specifies a concept (e.g., /explain cosine similarity), explain that. If they give a vague topic (e.g., "retrieval"), ask a clarifying question to narrow the scope. Good explanations are focused.
ELI5 Mode
If the learner says /explain eli5 [concept] or includes "eli5", "simple", "for dummies", or "no jargon":
- Give the entire explanation using only everyday language and analogies
- Zero technical terms. If you must use one, define it in parentheses immediately
- Skip the code example and math. Use diagrams and real-world comparisons instead
- Keep the whole explanation under 20 lines
- End with: "Want the technical version? Just say 'go deeper.'"
Standard Mode
- If the learner seems to want a quick answer (short question, or they say "briefly" / "quickly"), provide a 2-3 sentence TL;DR first, then ask "Want me to go deeper?" before proceeding through the full 9-step explanation.
- If the learner wants depth, proceed normally through all steps.
Common concepts learners ask about:
- How embeddings work
- Vector similarity and distance metrics
- Chunking strategies and trade-offs
- How retrieval-augmented generation works end-to-end
- Re-ranking and why it helps
- Hybrid search (combining dense and sparse)
- Prompt engineering for RAG
- Evaluation metrics (RAGAS, faithfulness, etc.)
- Hallucination and how to reduce it
- Context window management
- Fine-tuning vs. RAG
Step 2: The ELI5 Version
Start with the simplest possible explanation. Use an everyday analogy that anyone could understand. No jargon, no code, just the core idea.
Example for "vector embeddings": "Imagine every piece of text gets a home address in a huge city. Similar texts live in the same neighborhood. When you search, you are just looking for the nearest neighbors to your query's address."
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.
- 7d ago First seen · 142 lines · 14 tokens per session scan A 7359aa3bfb79
explain is a skill published in the GitHub repository TakaGoto/rag-learning-academy (18 stars, last pushed 5mo ago), licensed MIT. It adds 14 tokens to every session and 1,281 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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