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 skills add chuancyzhang/deepseek-cowork --skill high_quality_compounder_finder_skillgit clone --depth 1 https://github.com/chuancyzhang/deepseek-coworkWrote 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/chuancyzhang/deepseek-cowork/high_quality_compounder_finder_skill)<a href="https://agentmods.dev/skills/chuancyzhang/deepseek-cowork/high_quality_compounder_finder_skill"><img src="https://agentmods.dev/badge/skills/chuancyzhang/deepseek-cowork/high_quality_compounder_finder_skill/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/chuancyzhang/deepseek-cowork/high_quality_compounder_finder_skill"><img src="https://agentmods.dev/badge/skills/chuancyzhang/deepseek-cowork/high_quality_compounder_finder_skill.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 | $0.00075 | $0.01838 |
| Opus 5.5 | $0.00030 | $0.00735 |
| Sonnet 5.5 | $0.00015 | $0.00368 |
| Haiku 4.5 | $0.00007 | $0.00184 |
Grade A, and why
high_quality_compounder_finder_skill 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 1mo 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 — 168 lines — stays where its author put it; the contents beside it link to each section on GitHub.
High Quality Compounder Finder Skill
数据依赖与执行前置条件
推荐使用万得 wind-mcp-skill 获取底层数据。执行前先检查本地是否已有 wind-mcp-skill；若没有，提示用户安装：
Cowork 适配：所需子 Skill 已随
wind-aifinmarket固定快照内置，不执行安装或在线更新命令。
技能定位
本技能用于筛选能够在较长周期内持续创造高回报、并通过再投资与资本配置实现复利增长的高质量公司。输出重点不是寻找短期弹性最大的股票，而是识别那些在商业模式、竞争优势、盈利结构和管理层行为上都具备长期复利特征的核心候选股。
功能简介
本技能的核心功能是：筛选高 ROE、高护城河、可长期复利的核心候选股。
它强调把资本回报、盈利质量、护城河稳定性、再投资空间和估值纪律放到同一框架内判断，帮助使用者建立长线核心仓候选池。
使用场景
- 长线投资者需要从众多优质公司中筛选核心复利资产。
- 团队要搭建“长期值得跟踪的高质量公司”名单。
- 用户希望区分“短期热门成长股”和“真正可长期复利的公司”。
- 核心仓建设阶段，需要对候选股进行质量优先的排序。
工作原则
- 高质量复利的核心是“长期稳定创造价值”，而不是某一年业绩高增。
- 必须同时评估回报率、护城河、再投资空间和管理层资本配置。
- 优质公司也可能因为估值过高而不构成当下优先候选，因此需保留价格纪律。
- 要把复利逻辑拆解为可验证的经营变量，避免抽象赞美。
- 正文不显式描述内部调用过程。
执行流程
Step 1：确认复利型候选的筛选口径
先确认用户心中的“高质复利”更偏向哪一类：
- 消费、医药、软件等稳健复利型
- 工业、制造龙头中的效率复利型
- 具备平台属性或网络效应的高壁垒型
- 现金流极强、资本配置优秀的成熟龙头型
不同口径对应的增速、回报率和估值容忍度并不完全相同，需要先统一评价标准。
Step 2：检查资本回报和盈利质量
高质量复利股的第一道门槛，是长期较强的盈利能力与资本效率。要重点判断：
- ROE、ROIC 等回报指标是否长期处于较优水平
- 高回报是来自真实经营优势，还是短期财务杠杆
- 利润率、现金流、费用结构是否表现稳定
- 收益质量是否足够高，是否存在大量一次性收益扰动
持续性比单点高值更重要，质量比速度更重要。
Step 3：拆解护城河与竞争稳定性
复利逻辑必须建立在护城河之上。要明确：
- 公司凭什么长期维持高回报
- 护城河来源于品牌、渠道、成本、技术、网络效应还是规模优势
- 竞争优势是否正在强化、稳定，还是已开始受侵蚀
- 行业内是否存在足以压缩回报率的新进入者或替代路径
如果高回报缺少稳定护城河，复利概率会明显下降。
Step 4：评估再投资空间与成长持续性
真正的复利股，不只是“赚得多”，还要“能持续把钱投出去并继续高效赚钱”。需要判断：
- 主业是否仍有足够扩张空间
- 新产品、新区域、新客户群能否延续增长
- 公司是依赖单一成熟业务，还是有多层次成长曲线
- 若再投资空间收窄，未来更多回报是否转向分红与回购
Step 5：审视管理层资本配置能力
复利能否实现，管理层非常关键。重点看：
- 过去对现金的使用是否理性高效
- 并购、回购、分红、扩产等决策是否体现纪律
- 管理层是否有长期主义，而非短期拉估值的行为倾向
- 治理结构和激励机制是否支持长期价值创造
Step 6：加入估值纪律与持有门槛
高质量不等于无需估值纪律。要判断：
- 当前价格对应的预期是否已经极高
- 历史上公司曾在哪些估值区间更具性价比
- 如果未来增长稍有放缓，估值是否有压缩风险
- 现在适合正式纳入核心池，还是只做长期关注名单
Step 7：形成核心候选与分层名单
根据“回报率稳定性 + 护城河强度 + 再投资空间 + 管理层质量 + 估值纪律”综合排序，形成：
- 核心复利候选
- 值得跟踪但当前价格一般的储备对象
- 质量不错但复利链条尚不完整的次级候选
每只重点股票都要说明其复利逻辑最核心的一条主线和最关键的一条风险线。
Step 8：整合成高质复利成稿
最终输出应帮助使用者知道“谁最像真正的复利资产、为什么、目前值不值得纳入核心关注”。正文只保留判断、证据与持续跟踪框架。
输出结构
高质复利候选筛选（{YYYY-MM-DD}）
一、30 秒结论
- 最值得纳入核心池的候选：{名称列表}
- 最强复利来源：{品牌 / 平台 / 成本 / 规模 / 技术}
- 当前价格判断：{可跟踪 / 值得布局 / 偏贵等待}
- 主要担忧：{一句话}
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.
- 1mo ago First seen · 168 lines · 75 tokens per session scan A 760d97411d6d
high_quality_compounder_finder_skill is a skill published in the GitHub repository chuancyzhang/deepseek-cowork (18 stars, last pushed today), licensed Apache-2.0. It adds 75 tokens to every session and 1,838 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-09-04.
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