Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/luohaothu/everything-codex/iterative-retrievalnpx skills add Luohaothu/everything-codex --skill iterative-retrievalgit clone --depth 1 https://github.com/Luohaothu/everything-codexWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00023 | $0.01575 |
| Opus 5 | $0.00012 | $0.00788 |
| Sonnet 5 | $0.00005 | $0.00315 |
| Haiku 4.5 | $0.00002 | $0.00158 |
Grade A, and why
iterative-retrieval scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 207 lines — stays where its author put it; the contents beside it link to each section on GitHub.
迭代检索模式
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。
问题
子智能体被生成时上下文有限。它们不知道:
- 哪些文件包含相关代码
- 代码库中存在哪些模式
- 项目使用什么术语
标准方法会失败:
- 发送所有内容:超出上下文限制
- 不发送任何内容:智能体缺乏关键信息
- 猜测所需内容:经常出错
解决方案:迭代检索
一个逐步优化上下文的 4 阶段循环:
┌─────────────────────────────────────────────┐
│ │
│ ┌──────────┐ ┌──────────┐ │
│ │ DISPATCH │─────▶│ EVALUATE │ │
│ └──────────┘ └──────────┘ │
│ ▲ │ │
│ │ ▼ │
│ ┌──────────┐ ┌──────────┐ │
│ │ LOOP │◀─────│ REFINE │ │
│ └──────────┘ └──────────┘ │
│ │
│ Max 3 cycles, then proceed │
└─────────────────────────────────────────────┘
阶段 1:调度
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);
阶段 2:评估
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:
- 高 (0.8-1.0):直接实现目标功能
- 中 (0.5-0.7):包含相关模式或类型
- 低 (0.2-0.4):略微相关
- 无 (0-0.2):不相关,排除
阶段 3:优化
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)
};
}
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 207 lines · 23 tokens per session scan A be51de91f731
iterative-retrieval is a skill published in the GitHub repository Luohaothu/everything-codex (24 stars, last pushed 21d ago), licensed MIT. It adds 23 tokens to every session and 1,575 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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