Does it support project-specific terminology?

Yes, the loop dynamically extracts and adds new keywords and patterns discovered during the evaluation phase to improve the accuracy of subsequent searches.

How does it solve the 'context problem'?

It avoids the extremes of sending too much irrelevant data or too little critical information by evaluating initial results and refining search queries based on discovered terminology.

What is the Iterative Retrieval pattern?

It is a systematic 4-phase loop that progressively narrows down the most relevant files in a codebase to provide subagents with precise context without exceeding token limits.

How many cycles does the retrieval loop perform?

The pattern typically runs for a maximum of three cycles or until a sufficient amount of high-relevance context (files scoring 0.7 or higher) is gathered.

Can this skill handle large codebases?

Yes, it is specifically designed for large repositories where terminology and file locations might not be immediately obvious to an AI agent in a single pass.

Iterative Context Retrieval

Name: Iterative Context Retrieval
Author: arabicapp

byarabicapp

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Productivity & Workflow

Refines codebase context through a multi-phase loop to ensure subagents have the precise information needed for complex tasks.

The Iterative Retrieval skill implements a sophisticated 4-phase pattern—Dispatch, Evaluate, Refine, and Loop—designed to solve the 'context problem' in complex AI agent workflows. Instead of overwhelming the context window or guessing required files, it progressively identifies relevant code patterns and project-specific terminology over a maximum of three cycles. This ensures that subagents operate with high-precision context, leading to more accurate bug fixes, feature implementations, and architectural analysis in large-scale repositories where naming conventions and file structures may be non-obvious.

Key Features

01Configurable cycle limits to prevent infinite loops and optimize performance

024-phase retrieval loop (Dispatch, Evaluate, Refine, Loop) for deep context discovery

03Intelligent gap identification to drive subsequent search cycles

04Automatic discovery of project-specific terminology and naming conventions

051 GitHub stars

06Quantitative relevance scoring (0.0 to 1.0) to filter high-impact code files

Use Cases

01Providing precise context for subagents in complex multi-agent orchestration workflows

02Locating relevant files for bug fixes in unfamiliar or massive codebases

03Identifying architectural patterns and middleware when implementing new features

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add arabicapp/everything-claude-code iterative-retrieval

For use in Claude.ai and ChatGPT

Download Skill