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/glossary/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/glossary)<a href="https://agentmods.dev/skills/takagoto/rag-learning-academy/glossary"><img src="https://agentmods.dev/badge/skills/takagoto/rag-learning-academy/glossary/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/skills/takagoto/rag-learning-academy/glossary"><img src="https://agentmods.dev/badge/skills/takagoto/rag-learning-academy/glossary.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 | $0.00011 | $0.00918 |
| Opus 5 | $0.00005 | $0.00459 |
| Sonnet 5 | $0.00002 | $0.00184 |
| Haiku 4.5 | $0.00001 | $0.00092 |
Grade A, and why
glossary 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 10d 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 — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Glossary: RAG Terminology and Concepts
Scope: This skill is a quick-reference dictionary — short, scannable definitions with analogies. For deep, multi-layered explorations of how a concept works and why, use
/explain.
An interactive glossary that provides clear, multi-layered definitions of RAG terminology. Designed to be a quick reference that also teaches.
Language awareness: See
.claude/LANGUAGE_AWARENESS.md.
Step 1: Determine What to Look Up
- If the user provides a term (e.g.,
/glossary embeddings), define that term. - If the user provides a module number (e.g.,
/glossary module 3), list all key terms for that module. - If no argument is given, present the full glossary organized by category.
Step 2: Present the Definition
For each term, provide a structured definition with these layers:
One-Line Definition
A concise, precise definition in one sentence. Example: "An embedding is a dense numerical vector that represents the semantic meaning of a piece of text."
Analogy
An everyday analogy that makes the concept intuitive. Example: "Think of embeddings like GPS coordinates for meaning — texts with similar meanings have coordinates that are close together on the map of all possible meanings."
Technical Detail
A deeper technical explanation for learners who want to understand the mechanism. Include relevant details like dimensions, algorithms, or mathematical concepts — but keep it accessible.
Code Example
A minimal code snippet in the learner's chosen language that demonstrates the concept in action:
Generate a minimal, runnable example in the learner's language. For instance, if explaining embeddings, show how to create one using the appropriate library.
Common Misconceptions
One or two things people often get wrong about this concept. Example: "Embeddings are not word-for-word encodings — the same word in different contexts will have different embeddings in contextual models."
Related Terms
Links to other glossary terms that are closely related, so the learner can explore connected concepts.
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.
- 10d ago First seen · 117 lines · 11 tokens per session scan A 27dac5732090
glossary is a skill published in the GitHub repository TakaGoto/rag-learning-academy (19 stars, last pushed 5mo ago), licensed MIT. It adds 11 tokens to every session and 918 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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