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/paper-review/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/paper-review)<a href="https://agentmods.dev/skills/takagoto/rag-learning-academy/paper-review"><img src="https://agentmods.dev/badge/skills/takagoto/rag-learning-academy/paper-review/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/paper-review"><img src="https://agentmods.dev/badge/skills/takagoto/rag-learning-academy/paper-review.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 | $0.00999 |
| Opus 5 | $0.00005 | $0.00500 |
| Sonnet 5 | $0.00002 | $0.00200 |
| Haiku 4.5 | $0.00001 | $0.00100 |
Grade A, and why
paper-review 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 11d 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 — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Paper Review: Understand RAG Research Papers
Break down RAG research papers into understandable pieces, connecting academic innovations to practical applications the learner can use in their pipeline.
Step 1: Identify the Paper
If the user provides a paper title or URL (e.g., /paper-review "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks"), use that. Otherwise, suggest papers from a curated list organized by topic:
Foundational Papers
- "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks" (Lewis et al., 2020)
- "Dense Passage Retrieval for Open-Domain Question Answering" (Karpukhin et al., 2020)
- "REALM: Retrieval-Augmented Language Model Pre-Training" (Guu et al., 2020)
Chunking and Retrieval
- "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks" (Reimers & Gurevych, 2019)
- "ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction" (Khattab & Zaharia, 2020)
Advanced RAG
- "Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection" (Asai et al., 2023)
- "RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval" (Sarthi et al., 2024)
Evaluation
- "RAGAS: Automated Evaluation of Retrieval Augmented Generation" (Es et al., 2023)
Ask the learner which paper interests them or recommend one based on their current module.
Step 2: Set Context
Before diving in, give the learner context:
- What problem was this paper trying to solve?
- When was it published and why did it matter at that time?
- What should they pay attention to while reading?
- How does it connect to what they have already learned?
Step 3: Section-by-Section Walkthrough
Break the paper down into digestible sections:
Abstract and Introduction
- Summarize the core claim in one sentence
- What gap in existing work does this address?
- What is the proposed solution at a high level?
Background and Related Work
- What prior work does this build on?
- What are the key concepts the reader needs to understand?
- Fill in any knowledge gaps the learner might have
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.
- 11d ago First seen · 105 lines · 11 tokens per session scan A 90a1c46002e8
paper-review 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 999 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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