iterative-retrieval

Name: iterative-retrieval
Availability: InStock
Author: jakezp

Refine retrieved materials through a four-phase iterative loop with gap detection.

17|1|Updated Jan 30, 2026

Search Tags:#planning #context #subagents #multi-agent #retrieval #iterative

Ready to run

npx skills add https://github.com/jakezp/everything-opencode --skill iterative-retrieval-jakezp

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: iterative-retrieval
Source: https://github.com/jakezp/everything-opencode/tree/main/skills/iterative-retrieval
Command: npx skills add https://github.com/jakezp/everything-opencode --skill iterative-retrieval-jakezp

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Subagents often start with limited context, leading to inefficient or incorrect task execution. This skill provides a disciplined, iterative retrieval pattern that progressively refines the context to align with the task at hand.

Core Features & Use Cases

Structured 4-phase loop: DISPATCH, EVALUATE, REFINE, LOOP to gradually improve context relevance.
Context-aware pruning: Dynamically exclude irrelevant files and identify missing context to guide refinements.
Deterministic cycles: Enforces a max of three refinement cycles to balance depth and latency.

Quick Start

Initiate iterative retrieval by issuing a broad query, then iteratively refine based on evaluated relevance up to three cycles.

Frequently Asked Questions about iterative-retrieval

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

FAQPage Schema

How do I improve subagent context in a multi-agent workflow?▼

You can improve subagent context by applying an iterative retrieval pattern with a four-phase loop: DISPATCH, EVALUATE, REFINE, and LOOP. This structured process progressively narrows retrieved materials to align with the subagent's specific task.

What is the best way to refine retrieved context for subagents?▼

The best way to refine retrieved context is through iterative retrieval that dynamically prunes irrelevant files and detects context gaps. It enforces a deterministic cycle, evaluating relevance and refining search criteria up to three times to balance depth and latency.

How do I start iterative retrieval for multi-agent planning?▼

Start iterative retrieval by issuing a broad initial query to the subagent. The system then evaluates the relevance of the retrieved materials, prunes irrelevant files, and refines search criteria through a maximum of three deterministic cycles.

How many refinement cycles are needed for subagent context retrieval?▼

A maximum of three refinement cycles is enforced for subagent context retrieval. This deterministic limit ensures a balance between achieving deep context alignment and maintaining acceptable execution latency in multi-agent workflows.

How do multi-agent workflows handle missing context during task execution?▼

Multi-agent workflows handle missing context by using context-aware pruning to dynamically exclude irrelevant files and identify gaps. The system then refines search criteria in an iterative loop, progressively guiding subagents toward relevant files and concepts.

When should I avoid using an iterative retrieval pattern for subagents?▼

You should avoid using an iterative retrieval pattern when the subagent already possesses sufficient context, or when the latency introduced by up to three refinement cycles outweighs the benefits of dynamically pruning irrelevant files and detecting context gaps.