iterative-retrieval

Name: iterative-retrieval
Availability: InStock
Author: Aayushsoam

Iteratively retrieve and prune codebase files for subagent context refinement.

1|Updated Mar 31, 2026

Search Tags:#codebase #subagents #multi-agent #context-management #iterative-retrieval #retrieval-pipeline

Ready to run

npx skills add https://github.com/aayushsoam/clawbot-plus --skill iterative-retrieval-aayushsoam

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Subagents often start with limited context and must be guided to the right files and concepts; this pattern progressively narrows and refines the information load to the subagents.

Core Features & Use Cases

Iterative retrieval loop: A four-phase cycle (DISPATCH, EVALUATE, REFINE, LOOP) to progressively surface relevant files.
Context-aware refinement: Scores relevance, identifies missing context, and updates search criteria to close gaps.
Use Case: In large codebases, spawn subagents that need codebase context they cannot predict upfront and reformulate queries until sufficient context is assembled.

Quick Start

Use a task to trigger iterative retrieval that assembles the most relevant files.

Frequently Asked Questions about iterative-retrieval

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

FAQPage Schema

How do I progressively refine context for subagents in a large codebase?▼

To progressively refine subagent context in a large codebase, use an iterative retrieval loop with four phases: DISPATCH, EVALUATE, REFINE, and LOOP. This cycle scores relevance and dynamically updates search criteria to assemble the necessary files for subagents.

What is the best way to assemble relevant files for multi-agent code exploration?▼

The best way to assemble relevant files for multi-agent code exploration is using iterative retrieval. It scores context relevance, identifies missing information, and reformulates queries until sufficient context is assembled for the subagents.

When do I need iterative retrieval for subagent context management?▼

You need iterative retrieval for subagent context management when spawning multiple subagents for bug fixes or feature development in large projects where they cannot predict the required codebase context upfront.

How does the four-phase retrieval loop work for codebase information pruning?▼

The four-phase retrieval loop works by dispatching subagents, evaluating relevance scores, refining search criteria to close context gaps, and looping until the correct codebase information is pruned and assembled.

Can I adjust the retrieval cycles when spawning subagents for feature development?▼

Yes, you can adjust the retrieval cycles. The iterative retrieval loop features adjustable cycles, allowing you to control how many times the context is dynamically refined and queried before assembling the final relevant files.

Why does my subagent start with limited context during codebase exploration?▼

Subagents start with limited context during codebase exploration because they cannot predict the necessary files upfront. Iterative retrieval solves this by progressively narrowing and refining the information load through dynamic query refinement.