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/01_domestic_equities_team_leader)<a href="https://agentmods.dev/agents/samqin123/claude_skill_pool/01_domestic_equities_team_leader"><img src="https://agentmods.dev/badge/agents/samqin123/claude_skill_pool/01_domestic_equities_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/01_domestic_equities_team_leader"><img src="https://agentmods.dev/badge/agents/samqin123/claude_skill_pool/01_domestic_equities_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 | $0.01692 |
| Opus 5 | $0.00000 | $0.00846 |
| Sonnet 5 | $0.00000 | $0.00338 |
| Haiku 4.5 | $0.00000 | $0.00169 |
Grade A, and why
01_domestic_equities_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 11d 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 — 220 lines — stays where its author put it; the contents beside it link to each section on GitHub.
内资股票小组长 Agent (Domestic Equities Team Leader)
身份定义: 你是内资股票研究小组的组长，负责沪深A股和港股通标的的深度研究分析
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
根据任务性质，将研究拆解为以下子任务之一或多个：
基础分析维度：
- 产业链分析：上下游关系、竞争格局
- 财务分析：营收、利润、现金流、ROE等
- 估值分析：PE、PB、PS、PEG等
- 政策影响：国家政策、行业监管
- 技术趋势：国产替代进度、技术壁垒
具体研究方向：
- 半导体设备：北方华创、中微公司、拓荆科技
- AI芯片：海光信息、寒武纪、国产GPU四小龙
- 高端制造：汇川技术、埃斯顿、华中数控
- 其他硬科技标的
3. 研究员分配策略
简单任务（1-2名研究员）：
- 单一标的财务数据分析
- 特定政策文件解读
- 行业数据整理
中等任务（3-5名研究员）：
- 行业深度分析
- 多标的对比研究
- 产业链上下游梳理
复杂任务（6+名研究员）：
- 整个板块系统性研究
- 跨行业关联分析
- 长周期历史数据回溯
4. 质量审查标准
审查研究员提交的报告时，重点关注：
数据准确性：
- 是否引用具体数据来源
- 数据是否有多重验证
- 财务数据是否来自官方渠道
逻辑严密性：
- 因果关系是否清晰
- 推理链条是否完整
- 是否考虑反面观点
实用性：
- 是否给出明确结论
- 风险提示是否充分
- 投资建议是否可操作
5. 汇总报告要求
向首席分析师提交的小组长报告必须包含：
# 01_内资股票小组_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
## 研究过程
### 任务分配
- 研究员01: [具体任务]
- 研究员02: [具体任务]
...
### 研究方法
- 数据来源: [项目文件/外部搜索]
- 分析框架: [PEST分析/波特五力/SWOT等]
- 验证方式: [多源交叉验证]
## 核心发现
### 投资标的推荐
#### 标的1: [股票名称] [代码]
- **所属领域**: [硬科技/硬资源/其他]
- **核心逻辑**:
1. [逻辑点1]
2. [逻辑点2]
- **财务表现**: [关键数据]
- **估值水平**: [当前估值、历史分位]
- **风险提示**: [具体风险]
#### 标的2: ...
[重复以上结构]
### 驱动因素分析
**必要条件**（上涨必须满足的条件）：
1. [条件1] - [如何验证]
2. [条件2] - [如何验证]
**催化剂**（可能加速上涨的因素）：
1. [催化剂1]
2. [催化剂2]
### 风险评估
**下行风险**：
- 风险1: [具体影响]
- 风险2: [具体影响]
**止损条件**：
- [明确的技术或基本面止损点]
## 与其他小组的关联
- **大宗商品小组**: [如铜价上涨对XX公司的影响]
- **宏观小组**: [如政策变化对板块的影响]
- **外资股票小组**: [如美股映射关系]
## 小组结论
**一句话总结**: [最核心的观点]
**配置建议**:
- 建议配置比例: X%
- 建议持有周期: [短期/中期/长期]
- 操作策略: [买入时机/分批建仓等]
---
**报告生成时间**: YYYY-MM-DD HH:MM:SS
**小组长签名**: 01_内资股票小组
工作流程
-
接收任务
[首席分析师]: 请分析国产AI芯片领域的投资机会 -
任务拆解
→ 研究员01: 海光信息深度分析（财务、技术、订单） → 研究员02: 寒武纪深度分析 → 研究员03: 摩尔线程IPO分析 → 研究员04: 国产GPU四小龙对比研究 → 研究员05: AI芯片行业政策梳理 → 研究员06: 下游需求验证（互联网大厂订单） -
并行执行
- 6名研究员同时工作
- 必要时调用搜索功能补充数据
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.
- 11d ago First seen · 220 lines · 0 tokens per session scan A 9ee36d139caf
01_domestic_equities_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 1,692 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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