iterative-retrieval

Name: iterative-retrieval
Availability: InStock
Author: Walter

Iteratively retrieves and evaluates code files to refine subagent context.

1|Updated Oct 14, 2023

Search Tags:#refinement #context #codebase #agent #multi-agent #retrieval

Ready to run

npx skills add https://github.com/walterfan/lazy-ai-primer --skill iterative-retrieval-walterfan

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: iterative-retrieval
Source: https://github.com/walterfan/lazy-ai-primer/tree/main/assets/skills/iterative-retrieval
Command: npx skills add https://github.com/walterfan/lazy-ai-primer --skill iterative-retrieval-walterfan

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill addresses the challenge where subagents lack sufficient context to perform their tasks effectively, by progressively refining the information provided to them.

Core Features & Use Cases

Progressive Context Refinement: Iteratively searches and evaluates files to gather the most relevant information.
Subagent Context Problem Solution: Solves the issue of subagents not knowing what context they need upfront.
Use Case: When debugging a complex authentication issue, this Skill can intelligently find and provide only the relevant code files related to tokens, sessions, and user management, avoiding overwhelming the agent with the entire codebase.

Quick Start

Use the iterative-retrieval skill to find files related to 'user authentication' in the 'src' directory.

Frequently Asked Questions about iterative-retrieval

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

FAQPage Schema

How do I refine context for subagents in a multi-agent workflow?▼

To refine context for multi-agent workflows, use an iterative retrieval pattern that dynamically identifies and evaluates relevant code files based on evolving task requirements. This progressively gathers necessary codebase patterns, terminology, and focus areas for subagents.

What is the best way to retrieve relevant code files for agents without overwhelming them?▼

The best way to retrieve relevant code files without overwhelming agents is progressive context refinement. By iteratively searching and evaluating files, this approach intelligently provides only the targeted information needed, avoiding the injection of the entire codebase.

How does iterative retrieval work to solve the subagent context problem?▼

Iterative retrieval solves the subagent context problem by employing a four-phase loop: Dispatch, Evaluate, Refine, and Loop. This mechanism dynamically identifies relevant code files and evaluates them against evolving task requirements.

Can I use iterative retrieval to find specific codebase patterns for debugging complex issues?▼

Yes, you can use iterative retrieval to find specific codebase patterns for debugging. For example, when debugging an authentication issue, it intelligently finds and provides only the relevant code files related to tokens, sessions, and user management.

Why do my subagents lack sufficient context to perform their tasks effectively?▼

Subagents lack sufficient context because they do not know what information they need upfront. This issue is solved by progressively refining the information provided to them through an iterative retrieval loop that evaluates files based on evolving task requirements.

When do I need progressive context refinement for my agent workflows?▼

You need progressive context refinement when your subagents lack sufficient context to perform tasks effectively. It is specifically required when agents cannot determine the necessary codebase patterns, terminology, and focus areas upfront in multi-agent workflows.