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iterative-retrieval

Refine context retrieval through a 4-phase iterative loop for subagents.

Updated Mar 19, 2026

Search Tags:#subagents #multi-agent #workflow-automation #context-management #token-efficiency #code-search #iterative-retrieval

Ready to run

npx skills add https://github.com/RUSHYOP/imperium-cli --skill iterative-retrieval-rushyop

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: iterative-retrieval
Source: https://github.com/RUSHYOP/imperium-cli/tree/main/content/skills/iterative-retrieval
Command: npx skills add https://github.com/RUSHYOP/imperium-cli --skill iterative-retrieval-rushyop

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Pattern for progressively refining context retrieval to solve the subagent context problem

Core Features & Use Cases

4-phase iterative loop (DISPATCH, EVALUATE, REFINE, LOOP) to narrow context for subagents.
Demonstrates how to seed, evaluate, and refine search across code files to improve context.
Applies to multi-agent workflows where subagents need progressively refined context to complete tasks efficiently.

Quick Start

Run an iterative retrieval task to gather high-relevance context files for a given objective.

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 retrieval for subagents in a multi-agent workflow?▼

Context retrieval for subagents is refined through a 4-phase iterative loop that progressively narrows context payloads to high-relevance files. This staged discovery prevents subagents from receiving excessive upfront context while ensuring they retrieve the exact code files needed for their specific tasks.

What is the subagent context problem in multi-agent workflows?▼

The subagent context problem occurs when subagents in multi-agent workflows lack upfront context and require staged discovery to avoid excessive context payloads. Without progressive refinement, subagents either operate with insufficient context or consume too many tokens processing irrelevant code files.

How does the iterative retrieval loop evaluate and refine code search results?▼

The iterative retrieval loop evaluates and refines code search results through explicit evaluation scoring and refinement rules applied across four phases. The EVALUATE phase scores retrieved context for relevance, while the REFINE phase adjusts search parameters based on those scores to produce higher-relevance files.

When do I need progressive context refinement for multi-agent code search?▼

Progressive context refinement is needed when subagents in multi-agent workflows must discover task-relevant code files without receiving excessive upfront context payloads. It applies specifically to scenarios where subagents require staged discovery and iterative evaluation to efficiently complete their assigned tasks.

What are the limitations of using iterative retrieval for token efficiency in subagents?▼

Iterative retrieval improves token efficiency by narrowing context payloads to high-relevance files, but it requires multiple loop iterations to achieve optimal results. The 4-phase loop introduces processing overhead through explicit evaluation and refinement phases, which may not benefit simple tasks with minimal context requirements.