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 dividend_growth_entry_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/dividend_growth_entry_skill)<a href="https://agentmods.dev/skills/chuancyzhang/deepseek-cowork/dividend_growth_entry_skill"><img src="https://agentmods.dev/badge/skills/chuancyzhang/deepseek-cowork/dividend_growth_entry_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/dividend_growth_entry_skill"><img src="https://agentmods.dev/badge/skills/chuancyzhang/deepseek-cowork/dividend_growth_entry_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 | $0.00063 | $0.01859 |
| Opus 5.5 | $0.00025 | $0.00744 |
| Sonnet 5.5 | $0.00013 | $0.00372 |
| Haiku 4.5 | $0.00006 | $0.00186 |
Grade A, and why
dividend_growth_entry_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.
This is a copy
81% identical to breakout_candidate_finder_skill — 186 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 174 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Dividend Growth Entry 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
本技能用于筛选既能持续提升分红、又具备稳定盈利和再投资能力的股票，并进一步判断当前是否进入更有吸引力的配置区间。输出重点不是寻找“当下股息率最高”的公司，而是发现那些能够通过长期分红增长和企业内生扩张共同创造回报的标的。
功能简介
本技能的核心功能是：寻找股息持续增长且估值回落到合理区间的入场机会。
它强调把分红增长历史、盈利质量、再投资效率、估值位置与入场时机结合起来判断，帮助使用者筛出“越拿越值钱”的收益成长股。
使用场景
- 用户希望在收益和成长之间取得平衡，而不是只追当前高息。
- 长期配置型账户需要寻找可持续提分红的优质公司。
- 某些高质量公司经历阶段回调后，需要判断是否进入更适合布局的区间。
- 团队希望构建一组中长期复利型收益成长观察池。
工作原则
- 分红增长必须建立在盈利和现金流增长基础上，不能把一次性提分红当成长期趋势。
- 进入观察池的公司应同时具备经营稳定性和资本配置纪律。
- 入场判断要把质量与价格结合起来，优质公司也可能因估值过高而暂不具吸引力。
- 需要同时讨论上行逻辑和失效条件，避免“好公司可以任何价格买”的倾向。
- 正文中不显式描述内部工具名称。
执行流程
Step 1：确认用户偏好的收益成长画像
先明确用户更偏向哪种“股息成长”：
- 低波动、稳定提分红型
- 中速成长 + 分红逐步提升型
- 高质量龙头在阶段回调后的布局型
- 兼具分红回馈与估值修复潜力的复合型
不同画像会决定后续对股息率、增长速度与估值容忍度的权重分配。
Step 2：检查分红增长是否具备连续性
先确认候选公司是否真正具备“增长中的分红”特征，而非偶发式提升。重点观察：
- 分红金额或每股分红是否具备多期上升趋势
- 分红增长是否与利润增长、现金流增长大体匹配
- 是否存在某几年异常跳升但缺乏后续延续
- 管理层是否有清晰稳定的股东回报框架
连续性优先于绝对水平，稳定性优先于短期亮眼数据。
Step 3：评估经营质量与再投资能力
股息成长股的核心，不是把钱分出去，而是既能分、又能继续赚。需要判断：
- 主业是否稳定并具备较强盈利质量
- 资本回报水平是否足以支撑长期复利
- 再投资机会是否健康，不会因高分红挤压长期成长
- 业务护城河、客户结构与行业位置是否支持持续扩张
若企业盈利质量一般，单纯提高分红并不能构成优质候选。
Step 4：识别当前价格是否进入合理观察区
即便是优质股息成长公司，也应在更合理的价格区间布局。要关注：
- 当前估值相对历史均值是偏高、合理还是偏低
- 回调是否来自短期情绪，而非长期逻辑破坏
- 当前股息率相较公司自身历史是否更具吸引力
- 若现在介入，未来回报更可能来自分红增长、估值修复还是盈利增长
Step 5：检验分红增长的脆弱点
再优质的股息成长逻辑，也需要排查潜在失效因素：
- 盈利增速放缓导致未来提分红能力下降
- 行业景气下行侵蚀现金创造能力
- 大额资本开支或并购改变资本配置优先级
- 监管、政策或竞争格局变化削弱业务稳态
对于收益成长型选股，失效边界应写得比普通成长股更清楚。
Step 6：给出候选排序与配置逻辑
按照“分红增长连续性 + 经营质量 + 估值位置 + 风险可控度”排序，形成：
- 核心跟踪候选
- 等待回调或确认的观察对象
- 逻辑不错但当前价格仍偏贵的储备名单
每只标的需写明：
- 当前吸引力来自哪里
- 更适合何种持有周期
- 最值得继续跟踪的变量是什么
Step 7：形成入场观察与后续验证框架
优质收益成长股的关键不是“一次买完”，而是建立分阶段观察框架。需要给出：
- 当前更适合观察、试探还是正式纳入核心池
- 如果继续回调，哪些区间值得提高关注
- 未来哪些财报或经营信号将验证逻辑
- 在什么情况下，应暂停或撤销原有判断
Step 8：整合成股息成长成稿
最终输出应帮助使用者知道“哪些公司真正兼顾分红增长和经营质量、当前值不值得布局、后续看什么”。正文只保留结论、依据与跟踪框架。
输出结构
股息成长候选筛选（{YYYY-MM-DD}）
一、30 秒结论
- 最值得关注的股息成长候选：{名称列表}
- 当前更适合的动作：{布局 / 等待更好区间 / 仅跟踪}
- 核心吸引力：{分红成长 / 经营质量 / 估值回落}
- 主要失效风险：{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 · 174 lines · 63 tokens per session scan A 648ed9db5a0a
dividend_growth_entry_skill is a skill published in the GitHub repository chuancyzhang/deepseek-cowork (18 stars, last pushed today), licensed Apache-2.0. It adds 63 tokens to every session and 1,859 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 81% identical to breakout_candidate_finder_skill, differing in 186 lines, and is treated as a copy.
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