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 value_dividend_candidate_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/value_dividend_candidate_skill)<a href="https://agentmods.dev/skills/chuancyzhang/deepseek-cowork/value_dividend_candidate_skill"><img src="https://agentmods.dev/badge/skills/chuancyzhang/deepseek-cowork/value_dividend_candidate_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/value_dividend_candidate_skill"><img src="https://agentmods.dev/badge/skills/chuancyzhang/deepseek-cowork/value_dividend_candidate_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 | $0.00066 | $0.01944 |
| Opus 5.5 | $0.00026 | $0.00778 |
| Sonnet 5.5 | $0.00013 | $0.00389 |
| Haiku 4.5 | $0.00007 | $0.00194 |
Grade A, and why
value_dividend_candidate_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
77% identical to breakout_candidate_finder_skill — 195 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 — 183 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Value Dividend Candidate 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
本技能用于筛选兼具“估值不激进”和“分红具备吸引力”的收益型候选股。输出重点不是按股息率高低简单排序，而是判断分红是否真实可持续、估值是否提供缓冲、业务与现金流是否能支撑未来回报，以及高股息背后是否隐藏基本面恶化。
功能简介
本技能的核心功能是：筛选估值不贵、股息稳定且分红可持续的收益型标的。
它强调把收益率、安全边际、现金流质量、资本配置与风险识别放在一起判断，帮助使用者筛出“值得拿”的股息股，而不是“被动高息”的陷阱标的。
使用场景
- 用户希望建立偏稳健的收益型股票观察池。
- 需要从大盘股、金融、公用事业、央国企等方向中筛选股息标的。
- 希望在低利率或波动市况下寻找具备现金回报的候选股。
- 团队需要评估哪些高股息公司适合长期跟踪。
工作原则
- 股息率只是起点，不是结论，必须同时评估分红安全性与业务韧性。
- 高股息若来自股价大跌而非经营稳定，应优先排查是否属于价值陷阱。
- 估值、安全边际、现金流、负债压力和资本配置必须合并判断。
- 需要区分“适合长期持有的股息股”和“仅阶段性高息但可持续性弱的标的”。
- 正文中不显式描述内部工具或检索过程。
执行流程
Step 1：确认收益目标与筛选风格
先确认用户要找的是哪类收益型标的：
- 更偏高股息防御
- 更偏低估值修复 + 分红回报
- 更偏稳健现金流资产
- 还是偏央国企重估逻辑下的收益型候选
同时确认用户更看重当前股息率、分红稳定性，还是未来回报的平衡。
Step 2：建立基础候选池
先从具备收益型特征的股票中形成初始候选，但第一轮只做范围界定，不急于结论。重点关注：
- 长期具备稳定分红记录的公司
- 行业特征上适合分红的成熟业务
- 现金流较为稳定、资本开支可控的企业
- 估值处于历史或同业相对合理区间的公司
若某些标的虽然股息率高，但业务波动极大，应在起步阶段就标记为高风险候选。
Step 3：检查分红质量与可持续性
股息投资最关键的不是“今年分得多”，而是“未来能不能继续分”。需要重点判断：
- 当前分红水平是否被利润与自由现金流覆盖
- 分红政策是否稳定，过往是否频繁波动
- 公司是否在高负债、再融资压力或大额资本开支阶段
- 管理层资本配置偏好是持续回馈股东，还是阶段性安抚市场
如果分红依赖非经常性收益、资产处置或短期财技，应明显下调评分。
Step 4：评估估值是否提供安全边际
收益型投资并不意味着可以忽视价格。要判断：
- 当前估值处于自身历史什么位置
- 与同业相比，是否存在被低估或合理定价
- 市场给低估值的原因是暂时性分歧，还是结构性问题
- 在分红之外，是否仍有估值修复空间
理想的候选股应兼顾分红回报与价格缓冲，而不是只靠票息支撑。
Step 5：识别收益背后的风险来源
高股息并不天然安全，必须主动排查：
- 业绩下滑导致未来分红能力下降
- 政策、监管、价格管制等影响现金回流
- 负债压力、融资成本或资本开支抬升
- 股东结构变化、治理问题或资本配置转向
在收益型筛选里，风险识别的权重不应低于收益率比较。
Step 6：结合行业属性与利率环境解释吸引力
收益型标的的投资吸引力，往往受外部环境影响明显。要一并判断：
- 当前利率环境是否有利于高股息资产重新定价
- 所属行业是否处于景气底部、稳定期还是潜在下行期
- 市场当前更偏爱防御收益，还是追逐成长弹性
- 若市场风格变化，高股息标的的相对吸引力是否会下降
Step 7：形成候选排序与持有逻辑摘要
将候选分层为：
- 核心收益型候选
- 价值修复 + 股息兼备候选
- 高股息但需谨慎排雷的观察对象
每只标的都要写清楚：
- 当前看点
- 分红支撑逻辑
- 估值缓冲
- 主要风险
Step 8：整合成价值股息成稿
最终输出应帮助使用者知道“哪些股票值得继续做收益型跟踪、原因是什么、风险在哪里”。正文只保留结论、证据和后续跟踪框架。
输出结构
价值股息候选筛选（{YYYY-MM-DD}）
一、30 秒结论
- 最优先关注的收益型候选：{名称列表}
- 当前最有吸引力的逻辑：{高股息 / 低估值修复 / 二者兼备}
- 整体筛选判断：{偏积极 / 选择性配置 / 谨慎排雷}
- 最大风险点：{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 · 183 lines · 66 tokens per session scan A 915652017227
value_dividend_candidate_skill is a skill published in the GitHub repository chuancyzhang/deepseek-cowork (18 stars, last pushed today), licensed Apache-2.0. It adds 66 tokens to every session and 1,944 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 77% identical to breakout_candidate_finder_skill, differing in 195 lines, and is treated as a copy.
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