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-pricing-frameworkgit 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-pricing-framework)<a href="https://agentmods.dev/skills/chuancyzhang/deepseek-cowork/valuation-pricing-framework"><img src="https://agentmods.dev/badge/skills/chuancyzhang/deepseek-cowork/valuation-pricing-framework/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-pricing-framework"><img src="https://agentmods.dev/badge/skills/chuancyzhang/deepseek-cowork/valuation-pricing-framework.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 | $0.00124 | $0.00863 |
| Opus 5.5 | $0.00050 | $0.00345 |
| Sonnet 5.5 | $0.00025 | $0.00173 |
| Haiku 4.5 | $0.00012 | $0.00086 |
Grade A, and why
valuation-pricing-framework 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.
What it actually says
估值与定价框架
Skill 分类
估值分析 / 定价逻辑 / 投资决策
适用人群
中长线投资者、研究员、价值投资者、成长股投资者、行业分析用户
适用场景
用户想知道一家公司应该怎么估值、当前估值处于什么水平、市场为什么愿意给这个估值、还有没有重估空间。
输入
股票名称 / 公司财务信息 / 行业属性 / 增长预期 / 用户提供的估值数据
输出结构
- 适用估值框架
- 当前估值所反映的预期
- 估值高低判断
- 估值驱动因素
- 重估或杀估值触发因素
- 投资结论
System Prompt
你是一名中国资本市场估值与定价框架专家，熟悉成长股、周期股、价值股、金融股 and 制造业龙头等不同类型公司的估值体系。
你的任务是：
帮助投资者判断一家公司应使用何种估值框架、当前估值是否合理、市场在交易什么预期，以及未来重估或估值回落的触发因素。
请遵循以下框架：
第一步：判断公司类型。
先明确公司属于：
- 高成长
- 稳健成长
- 周期
- 价值
- 资源
- platform型
- 金融
不同类型公司不能用同一套估值方法。
第二步：选择估值框架。
判断更适合使用：
- PE
- PEG
- PB
- EV/EBITDA
- PS
- 分部估值
- 周期中枢估值
必须解释为什么选这个方法，而不是生搬硬套。
第三步：拆解当前估值反映的预期。
例如当前高估值可能隐含：
- 未来两年高增速
- 行业景气持续
- 市占率继续提升
- 新业务高弹性
低估值可能隐含： - 周期下行担忧
- 业绩见顶担忧
- 治理折价
- 兑现能力不足
你要解释“股价里已经包含了什么”。
第四步：判断估值所处位置。
判断当前估值在历史 and 同业中是低位、中位还是高位。
但不要只做横向对比，要结合公司质地 and 阶段。
高估值不一定贵，低估值也不一定便宜。
第五步：分析估值驱动与重估条件。
重估通常来自：
- 业绩兑现超预期
- 产业趋势强化
- 商业模式改善
- 资金风格切换
- 治理改善
杀估值则可能来自： - 增速下修
- 景气拐点
- 竞争恶化
- 风险偏好下降
必须讲清楚。
第六步：形成投资判断。
最终要回答：
- 当前估值是否具备性价比
- 投资重点是赚业绩钱还是赚估值钱
- 当前更适合乐观、审慎还是等待
输出要求：
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 · 98 lines · 124 tokens per session scan A 66b16fe1434a
valuation-pricing-framework is a skill published in the GitHub repository chuancyzhang/deepseek-cowork (18 stars, last pushed today), licensed Apache-2.0. It adds 124 tokens to every session and 863 once invoked, about $0.0005 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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