iterative-retrieval

Refine sub-agent queries through a structured 4-phase protocol with follow-up questions.

102|10|Updated Mar 4, 2026
One-click install
npx skills add https://github.com/closedloop-ai/claude-plugins --skill iterative-retrieval-closedloop-ai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: iterative-retrieval
Source: https://github.com/closedloop-ai/claude-plugins/tree/main/plugins/code/skills/iterative-retrieval
Command: npx skills add https://github.com/closedloop-ai/claude-plugins --skill iterative-retrieval-closedloop-ai

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill addresses the challenge where AI sub-agents may return incomplete information because they lack the broader semantic context that the orchestrator possesses, leading to insufficient summaries or missed details.

Core Features & Use Cases

  • Iterative Query Refinement: Enables an orchestrator to ask follow-up questions to a sub-agent, guiding it to gather more specific and relevant context.
  • Sufficiency Evaluation: Provides a checklist for the orchestrator to objectively assess if the sub-agent's response meets the required context level.
  • Use Case: When asking an AI to summarize a complex technical document, it might miss crucial nuances. This skill allows the orchestrator to ask targeted follow-up questions about specific sections or related concepts until a comprehensive understanding is achieved.

Quick Start

Use the iterative retrieval skill to ask follow-up questions to an AI agent until you have all the necessary context for your task.

Frequently Asked Questions about iterative-retrieval

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

FAQPage Schema
How do I get an AI agent to gather complete context instead of returning incomplete summaries?▼

To ensure complete context, use iterative query refinement to ask targeted follow-up questions that bridge the semantic gap between the orchestrator and sub-agents. This structured protocol guides the sub-agent to gather specific details until the orchestrator's required context level is achieved.

What is iterative retrieval in AI prompt engineering?▼

Iterative retrieval is a prompt engineering technique where an orchestrator evaluates a sub-agent's response and issues targeted follow-up questions to resolve semantic gaps. This structured refinement cycle ensures comprehensive context gathering before accepting the final output.

How do I evaluate if my AI sub-agent has gathered sufficient context?▼

Evaluate sufficient context by using a structured checklist to objectively assess the sub-agent's response against your requirements. If semantic gaps remain, initiate up to three targeted refinement cycles to prompt the agent for missing details.

Can I resume an AI context gathering workflow after stopping a query refinement cycle?▼

Yes, you can resume a context gathering workflow because the structured refinement protocol outputs the cycle count, gathered context, and a specific agent ID. This output format allows you to restart the session and continue iterative query refinement seamlessly.

What are the limitations of using iterative query refinement for AI agents?▼

The main limitation of iterative query refinement is the recommended maximum of three refinement cycles per sub-agent interaction. Exceeding this threshold is not advised, and complex semantic gaps may require manual orchestrator intervention rather than further automated follow-up questions.