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iterative-retrieval

Refine codebase context retrieval through a four-phase iterative loop.

1|Updated Jan 30, 2021
Search Tags:#context#codebase#rag#multi-agent#agents#retrieval#iteration
One-click install
npx skills add https://github.com/fideguch/my_dotfiles --skill iterative-retrieval-fideguch
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: iterative-retrieval
Source: https://github.com/fideguch/my_dotfiles/tree/main/claude/skills/iterative-retrieval
Command: npx skills add https://github.com/fideguch/my_dotfiles --skill iterative-retrieval-fideguch

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Solves the context problem in multi-agent workflows by progressively refining the information subagents need to perform tasks within a codebase.

Core Features & Use Cases

  • Four-phase retrieval loop (DISPATCH, EVALUATE, REFINE, LOOP) that iteratively identifies relevant files and context.
  • Cycles with a maximum of three iterations to balance completeness and efficiency.
  • Applies to codebase exploration, debugging, and feature development where context visibility emerges during task execution.
  • Supports building retrieval pipelines similar to RAG for code exploration and context-driven agent orchestration.

Quick Start

Start with a broad query and run the 4-phase loop to gather high-relevance files.

Frequently Asked Questions about iterative-retrieval

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

FAQPage Schema
How do I fix missing context when spawning subagents for codebase tasks?▼

Solve the subagent context problem by iteratively retrieving codebase context through a four-phase loop that progressively evaluates and refines file relevance to ensure agents have necessary information.

How does iterative retrieval work for multi-agent codebase exploration?▼

Iterative retrieval works through a four-phase loop: DISPATCH collects candidate files, EVALUATE scores relevance, REFINE adjusts queries, and LOOP repeats up to three cycles to balance completeness with efficiency.

What is the best way to handle context-too-large failures in agent workflows?▼

Handle context-too-large failures by applying iterative retrieval to progressively refine context, scoring file relevance and cycling queries up to three times to extract only high-relevance context for agents.

Can I use iterative retrieval to build RAG pipelines for code exploration?▼

Yes, you can use iterative retrieval to build RAG-like pipelines for code exploration by dispatching broad queries, evaluating file relevance, and refining searches to orchestrate context-driven multi-agent workflows.

What are the limitations of using a three-cycle retrieval loop for subagent context?▼

The limitation of a three-cycle retrieval loop is a maximum of three iterations, which bounds retrieval completeness; if high-relevance files are not found within three cycles, the context may remain incomplete.

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