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/architecture/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/architecture)<a href="https://agentmods.dev/skills/takagoto/rag-learning-academy/architecture"><img src="https://agentmods.dev/badge/skills/takagoto/rag-learning-academy/architecture/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/architecture"><img src="https://agentmods.dev/badge/skills/takagoto/rag-learning-academy/architecture.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 | $0.00012 | $0.01028 |
| Opus 5 | $0.00006 | $0.00514 |
| Sonnet 5 | $0.00002 | $0.00206 |
| Haiku 4.5 | $0.00001 | $0.00103 |
Grade A, and why
architecture 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 — 135 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Architecture: Design a RAG System for Your Use Case
Guide the learner through the process of designing a complete RAG architecture tailored to a specific use case. This skill builds system design thinking.
Step 1: Understand the Use Case
Ask the learner to describe their use case. If they do not have one, offer example scenarios:
- Customer support chatbot over product documentation
- Legal document Q&A for a law firm
- Internal knowledge base for a software team
- Research paper search and synthesis
- Multilingual FAQ system for an e-commerce platform
- Code documentation assistant
For their use case, gather the following requirements:
- Document types: What kinds of documents? (PDFs, HTML, code, structured data)
- Corpus size: How many documents? Total size?
- Query patterns: What kinds of questions will users ask?
- Accuracy requirements: How critical is correctness? (casual chatbot vs. medical advice)
- Latency requirements: What response time is acceptable?
- Scale: How many queries per day? How often does the corpus update?
- Budget constraints: Open-source only, or can you use paid APIs?
Step 2: Walk Through Architecture Decisions
For each component, explain the options and help the learner choose:
Document Processing
- File format handling and extraction
- Cleaning and preprocessing steps
- Metadata extraction strategy
Chunking Strategy
- Fixed-size, semantic, document-structure, or hybrid
- Chunk size and overlap recommendations for their document types
- Metadata to attach to each chunk
Embedding Model
- Open-source vs. API-based
- Model size vs. quality trade-off
- Domain-specific considerations (e.g., code embeddings, multilingual)
Vector Database
- In-memory (FAISS, Chroma) vs. hosted (Pinecone, Weaviate) vs. self-hosted (Qdrant, Milvus)
- Filtering and metadata requirements
- Scale and cost considerations
Retrieval Strategy
- Dense, sparse, or hybrid search
- Top-k selection and re-ranking
- Multi-stage retrieval if needed
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 · 135 lines · 12 tokens per session scan A d4badb7fb015
architecture is a skill published in the GitHub repository TakaGoto/rag-learning-academy (18 stars, last pushed 5mo ago), licensed MIT. It adds 12 tokens to every session and 1,028 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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