ワンクリックで
iterative-retrieval
Pattern for progressively refining context retrieval to solve the subagent context problem
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
メニュー
Pattern for progressively refining context retrieval to solve the subagent context problem
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
Use when about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before assertions always. Upgraded with AC-first workflow, pre-flight fast-fail, and structured Judge verdicts (inspired by opslane/verify).
Guide for creating Claude Code skills following Anthropic's official best practices. Use when user wants to create a new skill, build a skill, write SKILL.md, or needs skill creation guidelines. Provides structure, naming conventions, description writing, and quality checklist.
Fast iteration with two-stage review (spec compliance, then code quality)
Multi-agent coordination SOP derived from Anthropic's internal coordinator architecture. Use when orchestrating parallel workers, dispatching subagents, planning multi-step implementations, or when tasks require research→synthesis→implementation→verification workflow. Triggers: 'coordinate', 'dispatch workers', 'parallel agents', 'multi-agent', 'fan out', 'orchestrate'.
Use to run multiple subagents concurrently on independent tasks
Cross-platform content distribution and growth strategy for DYA app across 10+ platforms (Xiaohongshu, Douyin, TikTok, Instagram Reels, X/Twitter, Reddit, Weibo, Bilibili, WeChat Video). Use when planning social media posts, distributing video content, optimizing for platform algorithms, writing platform-specific copy, scheduling content releases, analyzing engagement data, deciding posting strategy, or maximizing organic reach. Also use when creating video ads that need multi-platform distribution, or when discussing growth/marketing/推广/发帖/推流/算法/涨粉 topics.
| name | iterative-retrieval |
| description | Pattern for progressively refining context retrieval to solve the subagent context problem |
Solves the "context problem" in multi-agent workflows where subagents don't know what context they need until they start working.
Subagents are spawned with limited context. They don't know:
Standard approaches fail:
A 4-phase loop that progressively refines context:
┌─────────────────────────────────────────────┐
│ │
│ ┌──────────┐ ┌──────────┐ │
│ │ DISPATCH │─────▶│ EVALUATE │ │
│ └──────────┘ └──────────┘ │
│ ▲ │ │
│ │ ▼ │
│ ┌──────────┐ ┌──────────┐ │
│ │ LOOP │◀─────│ REFINE │ │
│ └──────────┘ └──────────┘ │
│ │
│ Max 3 cycles, then proceed │
└─────────────────────────────────────────────┘
Initial broad query to gather candidate files:
// 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);
Assess retrieved content for relevance:
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:
Update search criteria based on evaluation:
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)
};
}
Repeat with refined criteria (max 3 cycles):
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;
}
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
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
Use in agent prompts:
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
continuous-learning skill - For patterns that improve over time~/.claude/agents/From Anthropic's coordinator architecture: "Workers can't see your conversation. Every prompt must be self-contained."
After iterative retrieval completes, the gathered context must be synthesized into a self-contained spec — not passed through as-is:
❌ WRONG: "I found some relevant files. Based on the retrieval, fix the bug."
✅ RIGHT: "Fix the null pointer in src/auth/validate.ts:42.
The Session type (src/auth/types.ts:15) has optional `user` field.
When sessions expire, user becomes undefined but token stays cached.
Add null check before user.id access. If null, return 401."
The retrieval is for YOUR understanding. The worker gets a synthesized spec, not raw retrieval results.