Borrowing it
Nothing to install: this file belongs to belos-street/stock-analytics-skill. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/belos-street/stock-analytics-skill/main/.agents/skills/macro-data-interpretation/SKILL.mdgit clone --depth 1 https://github.com/belos-street/stock-analytics-skillWrote 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/belos-street/stock-analytics-skill/macro-data-interpretation)<a href="https://agentmods.dev/skills/belos-street/stock-analytics-skill/macro-data-interpretation"><img src="https://agentmods.dev/badge/skills/belos-street/stock-analytics-skill/macro-data-interpretation/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/belos-street/stock-analytics-skill/macro-data-interpretation"><img src="https://agentmods.dev/badge/skills/belos-street/stock-analytics-skill/macro-data-interpretation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 5.1 | $0.00065 | $0.03229 |
| Opus 5 | $0.00032 | $0.01614 |
| Sonnet 5 | $0.00013 | $0.00646 |
| Haiku 4.5 | $0.00006 | $0.00323 |
Grade A, and why
macro-data-interpretation 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 9d 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 — 515 lines — stays where its author put it; the contents beside it link to each section on GitHub.
宏观数据解读
技能核心定位
核心目标
将宏观经济数据解读为结构清晰、可直接复用的研究周报式分析。输入一个或多个宏观指标（CPI、PPI、PMI、GDP、社融、外贸、失业率、利率等），输出包含结论摘要、核心数据、趋势与结构分析、后续跟踪展望的定性研究内容。适用于买方研究员、宏观策略师、财富管理顾问等需要快速形成数据观点的金融专业人士。
目标用户
- 宏观研究员：快速形成数据观点
- 策略分析师：宏观策略支持
- 财富顾问：客户沟通素材
- 投资经理：投资决策参考
- 学习者：宏观分析方法
技能边界
可提供服务：
- 数据核心结论
- 趋势结构分析
- 市场影响解读
- 政策含义分析
- 后续跟踪展望
不可提供服务：
- 精确预测
- 投资建议
- 内幕信息
指标分类体系
增长类指标
1. GDP（季度）
- 实际GDP增速
- 名义GDP增速
- GDP平减指数
- 三驾马车：消费、投资、出口
2. 工业增加值（月度）
- 规模以上工业增加值
- 采矿业/制造业/公用事业
- 国有企业/民营企业
3. 固定资产投资（月度）
- 基建投资
- 房地产投资
- 制造业投资
- 民间投资
4. 消费（月度）
- 社会消费品零售总额
- 网上零售额
- 限额以上企业零售
通胀类指标
1. CPI（月度）
- 同比
- 环比
- 食品/非食品
- 核心CPI
2. PPI（月度）
- 同比
- 环比
- 生产资料/生活资料
- 行业出厂价格
3. GDP平减指数（季度）
- 名义GDP/实际GDP
货币金融类指标
1. 社融（月度）
- 增量
- 存量增速
- 人民币贷款
- 企业债券
- 政府债券
- 委托贷款/信托贷款
2. 货币供应量（月度）
- M0/M1/M2
- 增速
3. 信贷（季度）
- 新增贷款
- 贷款结构：居民/企业/票据
- 短期/中长期
外贸类指标
1. 进出口（月度）
- 美元计价
- 人民币计价
- 贸易顺差
- 商品结构
2. 外汇储备（月度）
- 余额
- 变化
就业类指标
1. 城镇调查失业率（月度）
- 全国
- 31个大城市
2. 城镇新增就业（年度）
- 目标vs实际
解读框架
一、数据获取与核实
数据来源
官方发布：
- 国家统计局官网
- 央行官网
- 海关总署官网
- 外汇管理局官网
发布时间：
| 指标 | 发布时间 | 频率 |
|------|---------|------|
| CPI/PPI | 每月9日 | 月度 |
| PMI | 每月最后一天 | 月度 |
| GDP | 1/4/7/10月15日 | 季度 |
| 社融 | 每月12-15日 | 月度 |
| 进出口 | 每月13日 | 月度 |
| 失业率 | 每月15日 | 月度 |
二、核心结论提炼
结论结构
一、两句话核心结论
（一句话概括数据表现）
（一句话判断市场影响）
二、数据概览
（关键数据表格）
三、趋势判断
（数据方向：改善/回落/分化）
四、结构特征
（亮点/拖累项分析）
五、市场影响
（对各类资产的影响）
六、后续跟踪
（需要持续关注什么）
三、趋势与结构分析
趋势判断
方向判断：
- 回升：连续2个月以上改善
- 回落：连续2个月以上恶化
- 平稳：窄幅波动
- 分化：部分改善部分恶化
幅度判断：
- 大幅：变化超预期
- 小幅：符合预期
- 低于预期：弱于预期
结构分析
总量vs结构：
- 总量好 + 结构差：质量不高
- 总量差 + 结构好：转型成功
- 总量好 + 结构好：高质量发展
边际变化：
- 新变化是什么
- 变化的原因
- 变化能否持续
四、市场影响解读
债券市场
利率债：
- 利好：经济下行 + 通胀下行
- 利空：经济上行 + 通胀上行
信用债：
- 利好：信用环境改善
- 利空：信用风险上升
可转债：
- 关注：正股上涨机会
股票市场
行业影响：
| 宏观环境 | 受益行业 |
|----------|---------|
| 经济复苏 | 周期/金融/消费 |
| 经济衰退 | 防御/医药/消费 |
| 通胀上行 | 资源/农业/有定价权 |
| 通胀下行 | 中下游/成长 |
风格影响：
- 价值/成长风格切换
- 大盘/小盘风格
- 行业轮动
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.
- 9d ago First seen · 515 lines · 65 tokens per session scan A 57073422b5e8
macro-data-interpretation is a skill published in the GitHub repository belos-street/stock-analytics-skill (48 stars, last pushed 27d ago), licensed MIT. It adds 65 tokens to every session and 3,229 once invoked, about $0.0003 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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