Agent skill

Iterative Retrieval

by affaan-m in affaan-m/ECC

Pattern for progressively refining context retrieval to solve the subagent context problem.

MITAuto-check passedAgent Workflows

Install Iterative Retrieval

skills CLI
$ npx skills add affaan-m/ECC --skill iterative-retrieval -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install affaan-m/ECC iterative-retrieval --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/affaan-m/ECC.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/iterative-retrieval .claude/skills/iterative-retrieval && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
iterative-retrieval
GitHub stars
277k
Used in
7 other repos
Token cost
~1.6k tokens
SKILL.md length
277 words
Files
1
Skills in repo
683
Repo updated
First seen
Licence
MIT

At a glance

Pattern for progressively refining context retrieval to solve the subagent context problem.

  • Works in 4 steps: DISPATCH → EVALUATE → REFINE → …
  • A subagent lacks the context it needs and retrieval must be refined across passes
  • SKILL.md covers When to Activate, The Problem, The Solution: Iterative… and Practical Examples, plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Iterative Retrieval is an agent skill from affaan-m/ECC. Pattern for progressively refining context retrieval to solve the subagent context problem. Use when a subagent lacks the context it needs and retrieval must be refined across passes.

Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Agent Workflows, covering Subagents. The repository describes itself as: The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond. The licence is MIT.

When your agent uses it

  • A subagent lacks the context it needs and retrieval must be refined across passes
  • Tasks that involve Subagents

Example prompts

  • “/iterative-retrieval”

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. DISPATCH
  2. EVALUATE
  3. REFINE
  4. LOOP

What it can do on your machine

Read from SKILL.md and the folder at commit 2d515e4. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are javascript and markdown).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • x.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Iterative Retrieval loads about 1.6k tokens when it runs. Until then it costs about 51 tokens; SKILL.md has 277 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~51
When it runs · the whole SKILL.md, loaded when a task matches
~1.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from affaan-m/ECC at commit 2d515e4, republished under its MIT licence (© affaan-m). 277 words, ~1,580 tokens.

Download SKILL.mdSave it as .claude/skills/iterative-retrieval/SKILL.md (or your agent's skills folder).
name
iterative-retrieval
description
Pattern for progressively refining context retrieval to solve the subagent context problem. Use when a subagent lacks the context it needs and retrieval must be refined across passes.
metadata.origin
ECC

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:

┌─────────────────────────────────────────────┐
│                                             │
│   ┌──────────┐      ┌──────────┐            │
│   │ DISPATCH │─────│ EVALUATE │            │
│   └──────────┘      └──────────┘            │
│        ▲                  │                 │
│        │                  ▼                 │
│   ┌──────────┐      ┌──────────┐            │
│   │   LOOP   │─────│  REFINE  │            │
│   └──────────┘      └──────────┘            │
│                                             │
│        Max 3 cycles, then proceed           │
└─────────────────────────────────────────────┘
Phase 1: DISPATCH

Initial broad query to gather candidate files:

javascript
// Start with high-level intent
const initialQuery = {
  patterns: ['src/**/*.ts', 'lib/**/*.ts'],
  keywords: ['authentication', 'user', 'session'],
  excludes: ['*.test.ts', '*.spec.ts']
};

// Dispatch to retrieval agent
const candidates = await retrieveFiles(initialQuery);
Phase 2: EVALUATE

Assess retrieved content for relevance:

javascript
function evaluateRelevance(files, task) {
  return files.map(file => ({
    path: file.path,
    relevance: scoreRelevance(file.content, task),
    reason: explainRelevance(file.content, task),
    missingContext: identifyGaps(file.content, task)
  }));
}

Scoring criteria:

  • High (0.8-1.0): Directly implements target functionality
  • Medium (0.5-0.7): Contains related patterns or types
  • Low (0.2-0.4): Tangentially related
  • None (0-0.2): Not relevant, exclude
Phase 3: REFINE

Update search criteria based on evaluation:

javascript
function refineQuery(evaluation, previousQuery) {
  return {
    // Add new patterns discovered in high-relevance files
    patterns: [...previousQuery.patterns, ...extractPatterns(evaluation)],

    // Add terminology found in codebase
    keywords: [...previousQuery.keywords, ...extractKeywords(evaluation)],

    // Exclude confirmed irrelevant paths
    excludes: [...previousQuery.excludes, ...evaluation
      .filter(e => e.relevance < 0.2)
      .map(e => e.path)
    ],

    // Target specific gaps
    focusAreas: evaluation
      .flatMap(e => e.missingContext)
      .filter(unique)
  };
}
Phase 4: LOOP

Repeat with refined criteria (max 3 cycles):

javascript
async function iterativeRetrieve(task, maxCycles = 3) {
  let query = createInitialQuery(task);
  let bestContext = [];

  for (let cycle = 0; cycle < maxCycles; cycle++) {
    const candidates = await retrieveFiles(query);
    const evaluation = evaluateRelevance(candidates, task);

    // Check if we have sufficient context
    const highRelevance = evaluation.filter(e => e.relevance >= 0.7);
    if (highRelevance.length >= 3 && !hasCriticalGaps(evaluation)) {
      return highRelevance;
    }

    // Refine and continue
    query = refineQuery(evaluation, query);
    bestContext = mergeContext(bestContext, highRelevance);
  }

  return bestContext;
}

Practical Examples

Example 1: Bug Fix Context
Task: "Fix the authentication token expiry bug"

Cycle 1:
  DISPATCH: Search for "token", "auth", "expiry" in src/**
  EVALUATE: Found auth.ts (0.9), tokens.ts (0.8), user.ts (0.3)
  REFINE: Add "refresh", "jwt" keywords; exclude user.ts

Cycle 2:
  DISPATCH: Search refined terms
  EVALUATE: Found session-manager.ts (0.95), jwt-utils.ts (0.85)
  REFINE: Sufficient context (2 high-relevance files)

Result: auth.ts, tokens.ts, session-manager.ts, jwt-utils.ts
Example 2: Feature Implementation
Task: "Add rate limiting to API endpoints"

Cycle 1:
  DISPATCH: Search "rate", "limit", "api" in routes/**
  EVALUATE: No matches - codebase uses "throttle" terminology
  REFINE: Add "throttle", "middleware" keywords

Cycle 2:
  DISPATCH: Search refined terms
  EVALUATE: Found throttle.ts (0.9), middleware/index.ts (0.7)
  REFINE: Need router patterns

Cycle 3:
  DISPATCH: Search "router", "express" patterns
  EVALUATE: Found router-setup.ts (0.8)
  REFINE: Sufficient context

Result: throttle.ts, middleware/index.ts, router-setup.ts

Integration with Agents

Use in agent prompts:

markdown
When retrieving context for this task:
1. Start with broad keyword search
2. Evaluate each file's relevance (0-1 scale)
3. Identify what context is still missing
4. Refine search criteria and repeat (max 3 cycles)
5. Return files with relevance >= 0.7

Best Practices

  1. Start broad, narrow progressively - Don't over-specify initial queries
  2. Learn codebase terminology - First cycle often reveals naming conventions
  3. Track what's missing - Explicit gap identification drives refinement
  4. Stop at "good enough" - 3 high-relevance files beats 10 mediocre ones
  5. Exclude confidently - Low-relevance files won't become relevant
  • The Longform Guide - Subagent orchestration section
  • continuous-learning skill - For patterns that improve over time
  • Agent definitions bundled with ECC (manual install path: agents/)

© affaan-m, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/iterative-retrieval of affaan-m/ECC.

Open the folder on GitHubat commit 2d515e4

Used in 7 other repositories

We found 12 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 7 other GitHub owners. This page covers the copy in affaan-m/ECC, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Iterative Retrieval next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Iterative Retrieval compared with similar skills
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Iterative Retrieval this skillaffaan-m/ECC277k7 repos~1.6kAutomated safety check: PassMIT
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Subagent Driven DevelopmentAsvarox/allkaraoke26138 repos~1.2kAutomated safety check: PassNone
Dispatching Parallel Agentsultralisp/ultralisp25841 repos~1.5kAutomated safety check: PassNone
Reflect on Session Learningscursor/plugins11k5 repos~1.2kAutomated safety check: PassNone
Paseo Advisor Second Opiniongetpaseo/paseo20k1 repos~756Automated safety check: PassCustom licence

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Categories

Questions about Iterative Retrieval

What does Iterative Retrieval do?

Pattern for progressively refining context retrieval to solve the subagent context problem. Iterative Retrieval is an agent skill from affaan-m/ECC. Pattern for progressively refining context retrieval to solve the subagent context problem.

When should I use Iterative Retrieval?

Iterative Retrieval fits situations like: A subagent lacks the context it needs and retrieval must be refined across passes; tasks that involve Subagents.

How do I install Iterative Retrieval in Claude Code?

Run `npx skills add affaan-m/ECC --skill iterative-retrieval -a claude-code`. Or copy the skill folder (skills/iterative-retrieval in affaan-m/ECC) into .claude/skills/iterative-retrieval in your project. Claude Code loads it when a task matches its description.

How do I install Iterative Retrieval in Codex?

Run `npx skills add affaan-m/ECC --skill iterative-retrieval -a codex`. Or copy the skill folder (skills/iterative-retrieval in affaan-m/ECC) into .agents/skills/iterative-retrieval in your project. Codex loads it when a task matches its description.

Can I use Iterative Retrieval in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add affaan-m/ECC --skill iterative-retrieval -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/iterative-retrieval, .gemini/skills/iterative-retrieval, .github/skills/iterative-retrieval and .opencode/skills/iterative-retrieval in your project.

What does Iterative Retrieval need to run?

SKILL.md names no scripts, command-line tools or credentials: Iterative Retrieval is instructions for the agent only.

Does Iterative Retrieval access the network?

SKILL.md names 1 domain. As links in the text: x.com. This is read from the text; nothing was executed.

Is Iterative Retrieval safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Iterative Retrieval use?

Iterative Retrieval is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Iterative Retrieval use?

About 1.6k tokens (SKILL.md is roughly 6.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Iterative Retrieval?

Skills that share tags, products or a category with Iterative Retrieval: Claude Code Agent Development (anthropics/claude-plugins-official, 38k stars), Subagent Driven Development (Asvarox/allkaraoke, 261 stars), Dispatching Parallel Agents (ultralisp/ultralisp, 258 stars) and Reflect on Session Learnings (cursor/plugins, 11k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Iterative Retrieval?

affaan-m (a GitHub user) maintains it in affaan-m/ECC, which has 276,673 GitHub stars. The repository holds 683 skills in this directory. The repository was last updated on October 11, 2026.

Source: affaan-m/ECC on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.