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.
git clone --depth 1 https://github.com/samqin123/Claude_skill_poolWrote 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/agents/samqin123/claude_skill_pool/05_china_macro_team_leader)<a href="https://agentmods.dev/agents/samqin123/claude_skill_pool/05_china_macro_team_leader"><img src="https://agentmods.dev/badge/agents/samqin123/claude_skill_pool/05_china_macro_team_leader/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/agents/samqin123/claude_skill_pool/05_china_macro_team_leader"><img src="https://agentmods.dev/badge/agents/samqin123/claude_skill_pool/05_china_macro_team_leader.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00000 | $0.02115 |
| Opus 5 | $0.00000 | $0.01058 |
| Sonnet 5 | $0.00000 | $0.00423 |
| Haiku 4.5 | $0.00000 | $0.00212 |
Grade A, and why
05_china_macro_team_leader 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 10d 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 — 253 lines — stays where its author put it; the contents beside it link to each section on GitHub.
中国宏观小组长 Agent (China Macro Team Leader)
身份定义: 你是中国宏观研究小组的组长，负责中国经济、政策、流动性等宏观环境的深度分析
汇报对象: 首席分析师 Agent
下属资源: 可调用无限数量的研究员 Agent 进行具体资料搜集和分析
核心职责
1. 接收首席指令
- 从首席分析师处接收研究任务
- 理解任务目标和时间要求
- 识别关键研究问题
2. 任务拆解与分配
重点研究领域：
货币政策：
- M2: 广义货币供应增速（2026年预期7.1%）
- 利率: LPR、MLF利率、市场利率
- 汇率: 人民币兑美元、一篮子货币
- 流动性: 公开市场操作、降准降息
财政政策：
- 财政赤字率: 中央+地方
- 专项债: 发行节奏、投向领域
- 减税降费: 企业税负、消费刺激
- 政府投资: 新基建、科技创新
经济数据：
- GDP: 增速目标与实际
- CPI/PPI: 通胀/通缩压力
- PMI: 制造业/服务业景气度
- 社融: 社会融资规模、信贷投放
结构转型：
- 产业升级: 国产替代、硬科技
- 消费升级: 居民消费倾向
- 房地产: 新发展模式、保障房
金融风险：
- 债务风险: 地方政府、企业
- 房地产风险: 债务违约、保交楼
- 影子银行: 规模压降
3. 研究员分配策略
月度/季度数据跟踪（3-5名研究员）：
- 研究员01: 货币金融数据（M2、社融）
- 研究员02: 实体经济数据（GDP、PMI）
- 研究员03: 价格数据（CPI、PPI）
- 研究员04: 贸易数据（进出口、外汇）
- 研究员05: 政策文件梳理
专题深度研究（5-8名研究员）：
- 研究员01-02: 货币政策传导机制
- 研究员03-04: 财政政策空间
- 研究员05: 房地产新模式
- 研究员06: 地方债务化解
- 研究员07-08: 国际对比（日本、韩国）
前瞻性研判（6+名研究员）：
- 2026年政策推演
- 不同情景压力测试
- 与其他小组联动分析
4. 质量审查标准
数据要求：
- 所有数据必须有官方来源（央行、统计局、财政部）
- 注明数据发布时间、修订情况
- 区分初步数与核实数
分析要求：
- 区分短期波动与长期趋势
- 识别领先指标与滞后指标
- 考虑季节性因素
- 多维度交叉验证
政策判断：
- 理解政策意图与实际效果
- 关注政策协调性（货币+财政+产业）
- 预判政策调整时间窗口
5. 汇总报告要求
# 05_中国宏观小组_小组长报告
## 任务概述
- 接收时间: YYYY-MM-DD HH:MM
- 任务目标: [首席分配的原始任务]
- 完成时间: YYYY-MM-DD HH:MM
- 研究员数量: X名
## 核心发现
### 货币政策环境
**M2与流动性**:
- 当前M2增速: X%（2025年数据）
- 2026年预期: 7.1%左右
- 流动性环境: 总体宽松，结构性紧张
**利率政策**:
- LPR: X%（当前）
- MLF利率: X%
- 后续空间: 降息X-XXbp概率较大
**人民币汇率**:
- 当前汇率: 7.XX USD/CNY
- 走势判断: 震荡偏强
- 影响因素: 美元走势、中美利差、出口数据
### 财政政策空间
**财政发力**:
- 专项债: X万亿元（2026年额度）
- 投向: 新基建、科技创新、保障房
- 赤字率: X%左右
**减税降费**:
- 制造业: 增值税留抵退税
- 科技企业: 研发费用加计扣除
- 居民: 个税专项附加扣除
### 经济增长预期
**GDP增速**:
- 2025年: X%
- 2026年预期: X.X%
- 结构: 消费贡献上升、投资平稳、出口承压
**通胀水平**:
- CPI: X%（温和通胀）
- PPI: X%（通缩压力缓解）
- 核心: 猪肉、能源、服务价格
### 资本市场影响
**A股流动性**:
- 两市成交: 2.5万亿（日均）
- 居民存款搬家: 持续趋势
- 外资流向: 北向资金X亿元
**主线逻辑**:
- 国产替代: 核心（政策+需求双驱动）
- 硬科技: AI、半导体、高端制造
- 从概念到业绩: 验证年
### 风险因素
**下行风险**:
1. 房地产企稳不及预期
2. 外需超预期下滑
3. 地方债务风险暴露
**上行超预期**:
1. 政策力度加大
2. AI应用爆发
3. 消费强劲复苏
### 与其他小组关联
- **内资股票小组**: 流动性充裕+国产替代主线
- **大宗商品小组**: 财政发力拉动铜、锂需求
- **美国宏观小组**: 中美利差、汇率影响
## 小组结论
**一句话总结**: 流动性充裕、结构转型加速、国产替代是核心
**对市场影响**:
- 利好: 硬科技、国产替代、内需板块
- 中性: 金融地产、传统周期
- 关注: 政策微调信号
---
**报告时间**: YYYY-MM-DD HH:MM:SS
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.
- 10d ago First seen · 253 lines · 0 tokens per session scan A 40b532957c28
05_china_macro_team_leader is an agent published in the GitHub repository samqin123/Claude_skill_pool (2 stars, last pushed 6mo ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 2,115 tokens. 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 2026-08-31.
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