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逐步优化上下文检索以解决子代理上下文问题的模式
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Basierend auf der SOC-Berufsklassifikation
Use after a complex task, failure, or when reviewing what was learned. Teaches how to write growth logs that extract reusable patterns — not diary entries.
Design a goal-oriented agent loop, and review it for the ways loops go wrong — spinning and burning tokens, Goodhart-gaming the verifier, or running a wrong answer to completion. Two actions: (1) WRITE a loop — gate whether to build it, define a machine-decidable goal, pick the loop type, pick a skeleton; (2) REVIEW a loop — run it past five failure modes plus decidability, boundaries, fallback, judge independence, and keep-judgment-with-the-human red lines. Use when designing an autonomous agent loop, or when you already have one and worry it will spin, cheat, or run a wrong answer to the end. Complements the mechanism-layer loop skills (autonomous-loops, continuous-agent-loop) by covering the judgment layer they don't. 中文触发：写 loop、设计 loop、做一个 loop、检查 loop 对不对、loop 体检、loop 会不会跑飞、可判定目标、五个崩法、plan build judge。English triggers: design an agent loop, write a loop, check a loop, loop review, prevent a runaway loop, goal-oriented loop, decidable goal, plan/build/judge.
Stop hook that blocks Claude from finishing until quality checks pass. Detects rationalization patterns (surface text heuristics), stale learning logs (filesystem mtime), and low disk space. Complements self-audit by mechanically enforcing learning capture habits.
React Native and Expo app patterns — Expo Router navigation, state separation (server/client/route/form), TanStack Query data fetching with Zod, performant lists, NativeWind/StyleSheet styling, native APIs, and secure storage. Use when building or editing React Native / Expo screens, components, navigation, or data layers.
Instinct-based learning system that observes sessions via hooks, creates atomic instincts with confidence scoring, and evolves them into skills/commands/agents. v2.1 adds project-scoped instincts to prevent cross-project contamination.
Use this skill when writing new features, fixing bugs, or refactoring code. Enforces test-driven development with 80%+ coverage including unit, integration, and E2E tests.
| name | iterative-retrieval |
| description | 逐步优化上下文检索以解决子代理上下文问题的模式 |
| origin | ECC |
解决多智能体工作流中的“上下文问题”，即子智能体在开始工作前不知道需要哪些上下文。
子智能体被生成时上下文有限。它们不知道：
标准方法会失败：
一个逐步优化上下文的 4 阶段循环：
┌─────────────────────────────────────────────┐
│ │
│ ┌──────────┐ ┌──────────┐ │
│ │ 调度 │─────│ 评估 │ │
│ └──────────┘ └──────────┘ │
│ ▲ │ │
│ │ ▼ │
│ ┌──────────┐ ┌──────────┐ │
│ │ 循环 │─────│ 优化 │ │
│ └──────────┘ └──────────┘ │
│ │
│ 最多3次循环，然后继续 │
└─────────────────────────────────────────────┘
初始的广泛查询以收集候选文件：
// 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);
评估检索到的内容的相关性：
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)
}));
}
评分标准：
根据评估结果更新搜索条件：
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)
};
}
使用优化后的条件重复（最多 3 个周期）：
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;
}
任务："修复身份验证令牌过期错误"
循环 1:
分发：在 src/** 中搜索 "token"、"auth"、"expiry"
评估：找到 auth.ts (0.9)、tokens.ts (0.8)、user.ts (0.3)
优化：添加 "refresh"、"jwt" 关键词；排除 user.ts
循环 2:
分发：搜索优化后的关键词
评估：找到 session-manager.ts (0.95)、jwt-utils.ts (0.85)
优化：上下文已充分（2 个高相关文件）
结果：auth.ts、tokens.ts、session-manager.ts、jwt-utils.ts
任务："为API端点添加速率限制"
周期 1：
分发：在 routes/** 中搜索 "rate"、"limit"、"api"
评估：无匹配项 - 代码库使用 "throttle" 术语
优化：添加 "throttle"、"middleware" 关键词
周期 2：
分发：搜索优化后的术语
评估：找到 throttle.ts (0.9)、middleware/index.ts (0.7)
优化：需要路由模式
周期 3：
分发：搜索 "router"、"express" 模式
评估：找到 router-setup.ts (0.8)
优化：上下文已足够
结果：throttle.ts、middleware/index.ts、router-setup.ts
在智能体提示中使用：
在为该任务检索上下文时：
1. 从广泛的关键词搜索开始
2. 评估每个文件的相关性（0-1 分制）
3. 识别仍缺失哪些上下文
4. 优化搜索条件并重复（最多 3 个循环）
5. 返回相关性 >= 0.7 的文件
continuous-learning 技能 - 适用于随时间改进的模式agents/）