Agents & OrchestrationSûr100100/100
Iterative Retrieval
Pattern for progressively refining context retrieval to solve the subagent context problem
ou envoie-le directement à ton agent.
Installer dans ton projet
$ npx arboris-cli@latest install iterative-retrievalaffaan-m
affaan-m/ECC
Contenu à copier
---
name: iterative-retrieval
description: Pattern for progressively refining context retrieval to solve the subagent context problem
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
## Related
- [The Longform Guide](https://x.com/affaanmustafa/status/2014040193557471352) - Subagent orchestration section
- `continuous-learning` skill - For patterns that improve over time
- Agent definitions bundled with ECC (manual install path: `agents/`)Colle ce Markdown dans ton agent ou utilise les boutons ci-dessus pour l’écrire dans ton projet.
Ce que fait Iterative Retrieval
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
Comment utiliser Iterative Retrieval
1
Copie le prompt
Un clic copie le prompt packagé (ou l'envoie à ton agent).
2
L'agent installe le skill
Il ajoute le SKILL.md et ses ressources à ton projet.
3
Activation automatique
Le skill s'active dès que le contexte correspond.
Déclencheurs pour Iterative Retrieval
Dis simplement à ton agent quelque chose comme :
Applique le skill iterative-retrieval à cette tâche
Utilise iterative-retrieval pour améliorer cette implémentation
Passe en revue ce sujet avec iterative-retrieval
Skills liés à Iterative Retrieval
Acceleration (Microsoft)
Compare synthetic acceleration options and clearly separate scenario value from forecast commitments.Account Overview (Microsoft)
Create a synthetic Acme account overview and identify evidence that merits seller attention.Acroform Writer (Microsoft)
Use this skill whenever the user wants values written INTO an existing PDF form that lives in the connected SharePoint knowledge source — filling out, completing, populating, or submitting a fillable PDF (application, intake sheet, contract, government form) from data they supply, a spreadsheet row, or the conversation. Triggers include "fill out the job application form", "complete the intake form for [person]", "populate our standard NDA template", and "make this ready to send / non-editable" (flatten). The source form should be found via knowledge search; only ask the user to upload it if knowledge search turns up nothing plausible. different task), and do NOT use it on scanned/image-only PDFs that have no real fillable fields — those need OCR or manual overlay instead, which this skill explicitly detects and reports rather than silently failing.