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/duolongworld/ai_renaissance/layer4_expected_diffnpx skills add duolongworld/AI_Renaissance --skill layer4_expected_diffgit clone --depth 1 https://github.com/duolongworld/AI_RenaissanceWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/duolongworld/ai_renaissance/layer4_expected_diff)<a href="https://agentmods.dev/skills/duolongworld/ai_renaissance/layer4_expected_diff"><img src="https://agentmods.dev/badge/skills/duolongworld/ai_renaissance/layer4_expected_diff.svg" alt="Measured on agentmods" height="20"></a>What 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 | $0.00024 | $0.00436 |
| Opus 5 | $0.00012 | $0.00218 |
| Sonnet 5 | $0.00005 | $0.00087 |
| Haiku 4.5 | $0.00002 | $0.00044 |
Grade A, and why
layer4_expected_diff 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 4d 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.
What it actually says
Layer 4: 预期差信号引擎
执行方式
- 数值计算
- 智能分析（需要LLM解读预期差含义、交易含义）
- 混合模式
适用范围
计算"实际状态vs市场定价"的偏差，为 Layer 5 资产配置提供信号输入。
信号类型
类型A：高频意外指数
- 中国CESI：正值=中国数据持续超预期
- 美国CESI：正值=美国数据持续超预期
类型B：基本面vs市场定价
| 信号 | 计算 | 正信号含义 |
|---|---|---|
| 增长预期差 | CAI - 利率隐含增长 | 实际增长强于定价 |
| 通胀预期差 | 通胀得分 - 隐含通胀 | 实际通胀强于定价 |
类型C：跨国预期差
| 信号 | 含义 |
|---|---|
| 中美增长预期差 | A股vs美股相对价值 |
| 中美流动性差 | 全球资本流向 |
信号衰减规则
| 类型 | 初始有效期 | 衰减方式 |
|---|---|---|
| A类 | 2-4周 | 每周25% |
| B类 | 1-3个月 | 每月20% |
| C类 | 1-3个月 | 每月15% |
复核规则
- 连续2周反向 → 触发复核
- 连续3周反向 → 强制降级至弱信号
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 4d ago First seen · 54 lines · 24 tokens per session scan A a1149cfaa3ab
layer4_expected_diff is a skill published in the GitHub repository duolongworld/AI_Renaissance (59 stars, last pushed 6d ago), licensed Apache-2.0. It adds 24 tokens to every session and 436 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.
Other skills, from other repositories
convex-add
Add a capability to the CURRENT Convex app — consults the served Convex capability catalog for always-current procedures (billing, crons, auth, agent, search, …); falls back to built-in hosting or @convex-dev component search. TRIGGER when the user runs /add, or asks to add hosting/publishing or any backend capability…
twitter-reader
Read Twitter/X for financial research using opencli (read-only). Use this skill whenever the user wants to read their Twitter feed, search for financial tweets, view bookmarks, look up user profiles, or gather market sentiment from Twitter/X. Triggers include: "check my feed", "search Twitter for", "show my…
chenhao-limit-up
Use when evaluating A-share limit-up (涨停板) setups through Chen Hao's sentiment and momentum lens: market emotion cycles, board strength, follow-through, and short-term aggressive momentum trading.
trading-risk-gate
Unified pre-trade safety gate: Ruin check (Law #1), ergodicity audit, and win-rate dominance validation. Absorbs: ergodicity-check, law-of-ruin, win-rate-dominance.
cwv-optimizer
Diagnose and fix Core Web Vitals issues on AEM Edge Delivery Services pages. Goes deeper than generic CWV advice by understanding EDS-specific performance patterns including the 100KB LCP budget, E-L-D loading phases, block rendering behavior, and third-party script impact. Produces specific fixes for LCP, CLS, and…
multi-expert-analyzer
针对通用问题进行多领域专家联合分析, 综合稿产生前必经 fact-checker 与 red-team 两道独立校验。适用场景: 用户提出跨领域或不确定领域的复杂问题, 需要从多个专家角度分别搜证并相互校验后综合成文, 例如该不该买房、该不该跳槽、是否进入某个赛道等。触发关键词: 多角度分析、专家分析、综合分析、多视角、跨领域分析、从不同角度看、深度分析。问题只属于单一明确领域时, 优先使用该领域的专门 skill, 例如纯财务用 finance-core-analysis、纯技术用 software-architect。输出 (全部 markdown 保存到当前项目 markdown/ 目录): (1) 每位专家的中间分析稿…