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/compare/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/compare)<a href="https://agentmods.dev/skills/takagoto/rag-learning-academy/compare"><img src="https://agentmods.dev/badge/skills/takagoto/rag-learning-academy/compare.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 | $0.00010 | $0.01100 |
| Opus 5 | $0.00005 | $0.00550 |
| Sonnet 5 | $0.00002 | $0.00220 |
| Haiku 4.5 | $0.00001 | $0.00110 |
Grade A, and why
compare 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 — 129 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Compare: Side-by-Side RAG Approach Analysis
Provide structured, balanced comparisons of RAG approaches so learners can make informed decisions for their pipelines.
Step 1: Identify What to Compare
If the user specifies two approaches (e.g., /compare fixed vs semantic chunking), use those. Otherwise, present common comparison topics:
Chunking Strategies
- Fixed-size vs. semantic chunking
- Sentence-based vs. paragraph-based
- Recursive character vs. document-structure aware
Vector Databases
- Chroma vs. Pinecone vs. Weaviate vs. Qdrant
- In-memory vs. hosted vs. self-hosted
- FAISS vs. purpose-built vector DBs
Search Methods
- Dense (vector) vs. sparse (BM25) vs. hybrid
- Single-stage vs. two-stage (retrieve + re-rank)
- Keyword vs. semantic vs. hybrid search
Embedding Models
- OpenAI embeddings vs. open-source (sentence-transformers)
- Small vs. large embedding models
- Domain-specific vs. general-purpose embeddings
Architecture Patterns
- Naive RAG vs. advanced RAG vs. modular RAG
- Single-hop vs. multi-hop retrieval
- RAG vs. fine-tuning vs. long-context models
Ask the learner to pick one or suggest their own comparison.
Step 2: Present the Structured Comparison
For each approach, cover these dimensions in a clear side-by-side format:
Concept Overview
Explain each approach in 2-3 sentences. What is it and how does it work?
How It Works (Technical Detail)
Describe the mechanism. Include a short code snippet or pseudocode for each.
Pros and Cons Table
| Dimension | Approach A | Approach B |
|------------------|-------------------|-------------------|
| Ease of setup | ... | ... |
| Performance | ... | ... |
| Scalability | ... | ... |
| Cost | ... | ... |
| Flexibility | ... | ... |
| Maintenance | ... | ... |
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 · 129 lines · 10 tokens per session scan A fb8f1293d067
compare is a skill published in the GitHub repository TakaGoto/rag-learning-academy (18 stars, last pushed 5mo ago), licensed MIT. It adds 10 tokens to every session and 1,100 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.
Other skills, from other repositories
agent-platform-rag-engine-management
Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK. Use when listing RAG corpora or files, inspecting a corpus, retrieving contexts, or generating content grounded in a RAG corpus. Do not use for standard database queries (use SQL/Spanner skills), Google…
llm-app-patterns
Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications, implementing RAG, building agents, or setting up LLM observability.
9router-embeddings
Generate vector embeddings via 9Router /v1/embeddings using OpenAI / Gemini / Mistral / Voyage / Nvidia / GitHub embedding models for RAG, semantic search, similarity. Use when the user wants embeddings, vectors, RAG, semantic search, or to embed text.
azure-search-documents-dotnet
Azure AI Search SDK for .NET (Azure.Search.Documents). Use for building search applications with full-text, vector, semantic, and hybrid search. Covers SearchClient (queries, document CRUD), SearchIndexClient (index management), and SearchIndexerClient (indexers, skillsets). Triggers: "Azure Search .NET"…
similarity-search-patterns
Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.
embedding-strategies
Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embedding quality for specific domains.