Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add commands/ai-learning-gems/ai-learning-gems.github.io/paper-background-researchgit clone --depth 1 https://github.com/AI-Learning-Gems/AI-Learning-Gems.github.ioWrote 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/commands/ai-learning-gems/ai-learning-gems.github.io/paper-background-research)<a href="https://agentmods.dev/commands/ai-learning-gems/ai-learning-gems.github.io/paper-background-research"><img src="https://agentmods.dev/badge/commands/ai-learning-gems/ai-learning-gems.github.io/paper-background-research.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 | $0.00000 | $0.00225 |
| Opus 5 | $0.00000 | $0.00112 |
| Sonnet 5 | $0.00000 | $0.00045 |
| Haiku 4.5 | $0.00000 | $0.00022 |
Grade A, and why
paper-background-research 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 6d 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.
What it actually says
Research and explain the background for the given academic paper:
Paper Understanding
- Summarize the paper's key contributions in 2-3 sentences
- Identify the core problem being solved and why it matters
- Extract the main equations, algorithms, or methods
- Note any assumptions or limitations the authors mention
Prerequisites & Context
- List the prerequisite knowledge needed to understand the paper
- Explain unfamiliar terminology or notation
- Connect to foundational concepts
Related Work
- Identify the paper's intellectual lineage (what work it builds on)
- Note contemporary or subsequent papers that extend this work
- Highlight key differences from competing approaches
Critical Analysis
- Identify strengths and weaknesses of the proposed approach
- Note any gaps between theory and experiments
- Suggest potential extensions or open questions
Practical Takeaways
- Summarize what a practitioner should take away
- Note any implementation details or tricks mentioned
- Highlight hyperparameters or design choices that matter
Citation Format
- Use standard academic citation format
- Include arXiv links where available
- Note publication venue and year
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.
- 6d ago First seen · 33 lines · 0 tokens per session scan A dcd80b012a82
paper-background-research is a command published in the GitHub repository AI-Learning-Gems/AI-Learning-Gems.github.io (22 stars, last pushed 3mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 225 tokens. 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 commands, from other repositories
review
Cold re-quiz on code that already shipped — your own session commits, not the change in front of you.
learn-story-flow
Learn story-flow concepts with interactive guidance for junior developers.
annex-a-deep-dive
Deep dive analysis of ISO 27001 Annex A control domains with implementation guidance.
start-10-1
Command "start-10-1" from minicoohei/ai-agent-camp, covering 🎓 lesson 10-1: clasp基本・gasプロジェクト管理, 📍 このセッションでやること, 🎯 準備チェック, 🚀 step 1: claspのインストールと apps script api の確認 and 🚀 step 2: google認証.
start-13-4.en
Welcome to Lesson 13-4: Landing Page Implementation!
start-0-8
AIが自動実行: uv run python tools/setupprogress.py show を実行して現在のセットアップ進捗を表示する。.