How do I install the Agentic R Learning To Retrieve For Agentic Search skill?

Run npx skillmds@latest add adu2021/agentic-r-learning-to-retrieve-for-agentic-search in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Agentic R Learning To Retrieve For Agentic Search safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Agentic R Learning To Retrieve For Agentic Search?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Agentic R Learning To Retrieve For Agentic Search free to use?

Yes. Installing skills from SkillMD is free. This skill is licensed under MIT.

Who published Agentic R Learning To Retrieve For Agentic Search?

adu2021 (@adu2021) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Agentic R Learning To Retrieve For Agentic Search by adu2021

Name: Agentic R Learning To Retrieve For Agentic Search
Author: adu2021

Agentic R Learning To Retrieve For Agentic Search

Agentic search has recently emerged as a powerful paradigm, where an agent interleaves multi-step reasoning with on-demand retrieval to solve complex questions. Despite its success, how to design a retriever for agentic search remains largely underexplored. Existing search agents typically rely on similarity-based retrievers, while similar passages are not always useful for final answer generation. In this paper, we propose a novel retriever training framework tailored for agentic search. Unlike...

adu2021 Updated Sep 17, 2026

File contents

Problem

Agentic-R addresses key challenges in autonomous agent development. This paper provides solutions for evaluating, building, or improving agent systems.

Key Approach

The paper introduces a novel framework, methodology, or benchmark for agentic-r. The core contributions include:

Systematic framework or benchmark for agent evaluation and development
Empirical findings on agent performance, efficiency, or capabilities
Generalizable principles applicable across domains

When to Use

Use this skill when you need to:

Evaluate or benchmark autonomous agent systems
Understand best practices in agent design and evaluation
Learn empirical results on agent performance
Improve agent efficiency, reasoning, or capabilities

When NOT to Use

For non-agent-related tasks
When seeking quick implementation code (see the paper for details)
For general knowledge unrelated to autonomous agents

Resources

ArXiv Abstract: https://arxiv.org/abs/2601.11888
Full PDF: https://arxiv.org/pdf/2601.11888
HTML Version: https://arxiv.org/html/2601.11888

See the paper for comprehensive methodology, experimental protocols, benchmarks, and implementation details.

Frequently asked questions