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iterative-retrieval

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About iterative-retrieval

I

iterative-retrieval

Pattern for progressively refining context retrieval

iterative-retrieval is an agent skill from the ECC collection by affaan-m, listed on skills.sh. It describes a pattern for solving the context problem in multi-agent work: a subagent is started with limited context and does not know which files, patterns or terms the project uses until it begins work. Instead of guessing the context up front, the pattern lets the retrieval be refined step by step, with each pass narrowing down what the agent needs. The skill applies when subagents need codebase context that cannot be predicted in advance, when a multi-agent workflow refines context progressively, when tasks fail with context too large or missing context errors, when designing retrieval-augmented pipelines for code exploration, and when token use in agent orchestration needs to come down. The pattern is a way of structuring what a subagent asks for, not a tool. A skill is a SKILL.md file of instructions that an AI agent loads when a task calls for it. It is free and open source, and the skills.sh listing shows all three security audits passing.

View iterative-retrievalAll AI Skills apps
Price
Free
Made by
affaan-m
Category
AI Skills
Website
github.com/affaan-m/ecc/iterative-retrieval