iterative-retrieval

Name: iterative-retrieval
Availability: InStock
Author: yosefhayim

Iteratively discover relevant code files through a four-phase retrieval loop.

Updated Aug 27, 2026

Search Tags:#context #codebase #RAG #multi-agent #agents #retrieval

Ready to run

npx skills add https://github.com/YosefHayim/Template --skill iterative-retrieval-yosefhayim

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: iterative-retrieval
Source: https://github.com/YosefHayim/Template/tree/main/.cursor/skills/iterative-retrieval
Command: npx skills add https://github.com/YosefHayim/Template --skill iterative-retrieval-yosefhayim

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill addresses the challenge of providing subagents with the precise codebase context they need, especially when that context is not known upfront.

Core Features & Use Cases

Progressive Context Refinement: Iteratively searches and evaluates files to build a relevant context set.
Handles Unpredictable Needs: Ideal for scenarios where subagents must discover necessary information as they work.
Use Case: When debugging a complex issue, this Skill can help an agent progressively find the most relevant files related to authentication and session management without being given an exhaustive list initially.

Quick Start

Use the iterative-retrieval skill to find files related to 'user authentication' and 'session management'.

Frequently Asked Questions about iterative-retrieval

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I provide relevant codebase context to subagents when the required files are unknown initially?▼

Iterative retrieval dynamically discovers relevant code files for subagents by using a four-phase loop: Dispatch, Evaluate, Refine, and Loop. This progressive context refinement pattern evaluates files against evolving task requirements to build a precise context set.

What is the best way to refine context for complex multi-agent workflows?▼

The best way to refine multi-agent context is using a four-phase loop: Dispatch, Evaluate, Refine, and Loop. This pattern manages context size and relevance by progressively searching and evaluating files against evolving task requirements during code exploration.

How does iterative retrieval work for RAG-like pipelines in code exploration?▼

Iterative retrieval works in RAG-like pipelines by progressively searching and evaluating code files to build a relevant context set. It dynamically discovers necessary information based on evolving task requirements rather than relying on a static initial list.

Can I use iterative retrieval to find files for debugging complex issues like session management?▼

Yes, you can use iterative retrieval to find files for debugging complex issues like session management. It progressively searches the codebase to find relevant files related to specific domains without requiring an exhaustive initial list.

When do I need progressive context refinement for agents?▼

You need progressive context refinement when subagents must discover necessary codebase information as they work. It is ideal for scenarios where the required context is unpredictable and not known upfront, such as debugging complex issues.