home / skills / affaan-m / everything-claude-code / iterative-retrieval
This skill guides iterative retrieval to progressively refine codebase context for multi-agent tasks, reducing missing context while honoring token limits.
npx playbooks add skill affaan-m/everything-claude-code --skill iterative-retrievalReview the files below or copy the command above to add this skill to your agents.
---
name: iterative-retrieval
description: Pattern for progressively refining context retrieval to solve the subagent context problem
---
# 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 in `~/.claude/agents/`
This skill implements an iterative retrieval pattern for progressively refining the context supplied to subagents. It prevents sending too much or too little code by looping through dispatch, evaluation, refine, and loop phases. The approach targets multi-agent workflows where the right context isn’t known upfront and optimizes token use and relevance.
Start with a broad file-and-keyword query to gather candidate files (DISPATCH). Score and explain each candidate for relevance and identify missing gaps (EVALUATE). Use those signals to expand patterns and keywords, exclude irrelevant paths, and focus on specific gaps (REFINE). Repeat up to three cycles, returning high-relevance files and the minimal context needed for the subagent.
How many cycles should I run?
Limit to three cycles in most cases; stop early when you have multiple high-relevance files and no critical gaps.
What relevance threshold should I use?
Use 0.7 as a practical cut-off for high relevance; merge multiple high-relevance files to form the working context.