How to use
- Copy the installation command and review the source package before installing it in your own agent environment.
- Check the source document for required tools, credentials and supported hosts.
- Describe your input and desired deliverable; review the output against the source material.
npx skills add https://github.com/affaan-m/ecc --skill iterative-retrievalBefore you start
- Installation does not include a model subscription, API allowance or permission to access your files.
- Repository stars and installation counts describe activity, not an independent quality or security test.
Original skill document
Rendered from the source repository. Headings, examples, code, tables, links, and referenced images are preserved.
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.tsExample 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.tsIntegration 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 >= 0.7Best Practices
- Start broad, narrow progressively - Don't over-specify initial queries
- Learn codebase terminology - First cycle often reveals naming conventions
- Track what's missing - Explicit gap identification drives refinement
- Stop at "good enough" - 3 high-relevance files beats 10 mediocre ones
- Exclude confidently - Low-relevance files won't become relevant
Related
- The Longform Guide - Subagent orchestration section
continuous-learningskill - For patterns that improve over time- Agent definitions bundled with ECC (manual install path:
agents/)
