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 valuation_snapshot_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/valuation_snapshot_skill)<a href="https://agentmods.dev/skills/chuancyzhang/deepseek-cowork/valuation_snapshot_skill"><img src="https://agentmods.dev/badge/skills/chuancyzhang/deepseek-cowork/valuation_snapshot_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/valuation_snapshot_skill"><img src="https://agentmods.dev/badge/skills/chuancyzhang/deepseek-cowork/valuation_snapshot_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 | $0.00054 | $0.01287 |
| Opus 5.5 | $0.00022 | $0.00515 |
| Sonnet 5.5 | $0.00011 | $0.00257 |
| Haiku 4.5 | $0.00005 | $0.00129 |
Grade A, and why
valuation_snapshot_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
80% identical to breakout_candidate_finder_skill — 202 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.
What it actually says
Valuation Snapshot 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
本技能用于快速回答“这只股票现在大致贵不贵”,并解释贵或便宜背后对应的市场预期。输出重点不是做完整估值模型,而是在有限时间内,用合适的估值口径、历史分位和同业对照,形成对当前赔率的初步判断。
执行流程
Step 1:确认适用的估值视角
先判断公司更适合用什么估值口径来观察,常见包括:
- 盈利驱动型公司,看利润类倍数
- 高增长或利润未稳定公司,看收入类倍数
- 资产属性明显公司,看净资产或重置价值
- 周期属性明显公司,看景气位置与中枢估值
若公司处于特殊阶段,应明确说明传统估值口径可能失真。
Step 2:提取当前估值水平与历史区间
收集当前估值指标,并尽可能放回历史坐标中判断:
- 当前处于历史高位、中位还是低位
- 相较过去几个关键阶段是否明显溢价或折价
- 当前估值是否已显著偏离自身经营变化速度
若历史数据本身受极端事件影响,应单独说明,不可机械使用。
Step 3:加入同业与行业参照
估值判断不能只看自身历史,还要看相对位置:
- 同行业龙头与二线公司的估值差异
- 市场给予溢价或折价的常见理由
- 当前公司所处位置是否合理反映其竞争地位、增长质量和风险结构
若是主题股或高波动股票,还要留意情绪因素对估值的短期扭曲。
Step 4:解释“估值位置”背后的预期要求
真正有用的估值判断,不是“倍数高/低”本身,而是:
- 现在的价格隐含了多强的增长与盈利改善假设
- 未来需要兑现哪些条件,当前估值才站得住
- 哪些变量一旦失手,估值最容易被压缩
这一步要把估值和经营变量重新连接起来。
Step 5:判断重估或去估值的触发因素
列出未来最可能改变估值水平的因素,例如:
- 财报超预期或低于预期
- 业务结构改善或恶化
- 行业景气趋势变化
- 政策、融资、市场风格切换
帮助使用者理解估值不是静态数字,而是动态预期的结果。
Step 6:输出简洁结论与使用建议
最终明确回答:
- 这只股票当前估值大致处于什么位置
- 这个位置是合理、偏高还是偏低
- 更适合继续观察、逢低关注,还是需要更强兑现才值得接近
输出结构
{股票名称}估值快照({YYYY-MM-DD})
一、估值结论
- 当前估值判断:{偏低 / 合理 / 偏高}
- 历史位置:{低位 / 中位 / 高位}
- 同业相对位置:{折价 / 接近 / 溢价}
二、估值核心表
| 维度 | 当前情况 | 解读 |
|---|---|---|
| 主要估值口径 | {指标} | {为何适用} |
| 当前水平 | {数值} | {说明} |
| 历史分位 | {位置} | {说明} |
| 同业对比 | {结果} | {说明} |
三、当前价格隐含了什么预期
- {预期 1}
- {预期 2}
- {预期 3}
四、为什么会有溢价或折价
溢价/折价理由
- {理由 1}
- {理由 2}
不合理之处
- {不合理点 1}
- {不合理点 2}
五、未来估值变化的关键触发
- 有利于重估的条件:{条件}
- 导致去估值的风险:{条件}
六、使用建议
- 适合的动作:{继续观察 / 等验证 / 可重点跟踪}
- 最该盯住的变量:{变量}
质量要求
- 先选对估值口径,再谈高低,不能机械套用单一倍数。
- 估值结论必须回到经营与预期,不可停留在数字表层。
- 历史与同业对照都要讲清口径和边界,避免误导性比较。
- 若公司处于异常阶段,应明确指出估值参考的局限性。
- 输出应服务“赔率判断”,而不是把估值写成孤立数学题。
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 · 140 lines · 54 tokens per session scan A 55737ae2510f
valuation_snapshot_skill is a skill published in the GitHub repository chuancyzhang/deepseek-cowork (18 stars, last pushed today), licensed Apache-2.0. It adds 54 tokens to every session and 1,287 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 80% identical to breakout_candidate_finder_skill, differing in 202 lines, and is treated as a copy.
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