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Iterative Retrieval

affaan-m/ecc

Solves the problem of subagents not knowing what context they need until they actually start working on a task. Instead of dumping your entire codebase or guessing what's relevant, this runs a 3-cycle loop: dispatch a broad search, evaluate what you got, refine based on what's missing, repeat. Each cycle scores files 0 to 1 and learns your codebase's actual terminology (like discovering your project calls it "throttle" not "rate limit"). Stops when you have 3+ high-relevance files or hit the cycle limit. The pattern is from ECC and targets multi-agent workflows where token limits make "send everything" impossible but "send nothing" leaves agents blind.

skills.sh installs
5.5K
GitHub stars
300.3K
last updated
Aug 21, 2026
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Install

$ npx -y skills add affaan-m/ecc --skill iterative-retrieval --agent claude-code

Installs into .claude/skills of the current project.

Files

Iterative Retrieval Pattern

Solves the "context problem" in multi-agent workflows where subagents don't know what context they need until they start working.

When to Activate

  • Spawning subagents that need codebase context they cannot predict upfront
  • Building multi-agent workflows where context is progressively refined
  • Encountering "context too large" or "missing context" failures in agent tasks
  • Designing RAG-like retrieval pipelines for code exploration
  • Optimizing token usage in agent orchestration

The Problem

Subagents are spawned with limited context. They don't know:

  • Which files contain relevant code
  • What patterns exist in the codebase
  • What terminology the project uses

Standard approaches fail:

  • Send everything: Exceeds context limits
  • Send nothing: Agent lacks critical information
  • Guess what's needed: Often wrong

The Solution: Iterative Retrieval

A 4-phase loop that progressively refines context:

SKILL.md on GitHub

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Details

Repository
github.com/affaan-m/ecc
Category
AI & Agent Building
First seen
Jul 14, 2026

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