moat_strength_review_skill

moat_strength_review_skill is a skill for Claude Code, Codex from chuancyzhang/deepseek-cowork. It costs 62 tokens per session (1,257 once invoked), scanned A, original, Apache-2.0.

A company-review guide for judging whether a business has a durable competitive advantage, often called an economic moat. It examines where the advantage comes from, how strong it is, and what could weaken it.

In plain words
What is it for?
Use it to screen long-term stock ideas, research high-quality companies, and assess competitive barriers.
Why use it?
A company may appear strong while its advantage is temporary or difficult to turn into returns. This helps test whether the claimed barrier to competition is real.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to screen long-term stock ideas, research high-quality companies, and assess competitive barriers.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/chuancyzhang/deepseek-cowork/moat_strength_review_skill
Install

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.

Any agent
npx skills add chuancyzhang/deepseek-cowork --skill moat_strength_review_skill
Clone the repo
git clone --depth 1 https://github.com/chuancyzhang/deepseek-cowork

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for moat_strength_review_skill

README.md
[![agentmods](https://agentmods.dev/badge/skills/chuancyzhang/deepseek-cowork/moat_strength_review_skill/github.svg)](https://agentmods.dev/skills/chuancyzhang/deepseek-cowork/moat_strength_review_skill)
Your own site
<a href="https://agentmods.dev/skills/chuancyzhang/deepseek-cowork/moat_strength_review_skill"><img src="https://agentmods.dev/badge/skills/chuancyzhang/deepseek-cowork/moat_strength_review_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.

agentmods 80×15 button for moat_strength_review_skill

Your own site · 80×15
<a href="https://agentmods.dev/skills/chuancyzhang/deepseek-cowork/moat_strength_review_skill"><img src="https://agentmods.dev/badge/skills/chuancyzhang/deepseek-cowork/moat_strength_review_skill.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 62 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,257 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00062 $0.01257
Opus 5.5 $0.00025 $0.00503
Sonnet 5.5 $0.00012 $0.00251
Haiku 4.5 $0.00006 $0.00126

Measured 1mo ago against content hash 95adcb751595, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

moat_strength_review_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.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

ai_skills/wind-aifinmarket/skills/moat_strength_review_skill/SKILL.md · 139 lines

What it actually says

Moat Strength Review 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

本技能用于判断一家公司是否具备可持续竞争优势,以及这种优势是否足够深、足够稳、足够能转化为长期回报。输出重点不是给公司贴“好公司”标签,而是拆清护城河来自哪里、证据是否扎实、在什么情况下会被削弱。

执行流程

Step 1:明确要验证的护城河命题

先把“护城河”说具体。常见命题包括:

  • 成本优势是否足以长期压制同行
  • 品牌、渠道或客户关系是否形成进入门槛
  • 技术、专利、标准或生态是否构成锁定
  • 网络效应、规模效应或牌照资源是否具备持续性

如果只是短期领先而非结构性优势,应避免直接定义为护城河。

Step 2:识别优势来源与作用机制

逐项拆解公司竞争优势的来源,并说明其作用路径:

  • 它究竟如何帮助公司获取客户、维持价格、提高利润率或稳住份额
  • 它影响的是收入端、成本端还是资本回报端
  • 这种优势是行业共性,还是公司特有

这一阶段要把“听起来很强”转化为“如何产生经济结果”。

Step 3:寻找可验证证据

围绕竞争优势搜集可验证的证据,例如:

  • 市场份额与份额变化
  • 毛利率、费用率、回报率等长期表现
  • 客户粘性、复购率、切换成本或渠道掌控力
  • 行业内对手结构与进入难度

重点不是证据越多越好,而是证据是否能证明优势真实存在并持续发挥作用。

Step 4:评估持续性与削弱力量

分析护城河能否持续,重点观察:

  • 技术替代与商业模式替代风险
  • 监管与政策变化
  • 上下游议价结构改变
  • 新进入者、跨界者或价格战冲击

只有经得起反向审视的优势,才值得被视为长期护城河。

Step 5:判断优势是否能转化为股东回报

即使有竞争优势,也不一定意味着投资回报好。要进一步判断:

  • 优势是否体现为更高利润率或更稳回报
  • 管理层是否能把优势转化为合理资本配置
  • 当前估值是否已过度透支这份优势

这一步帮助区分“好公司”和“好股票”。

Step 6:给出护城河等级与跟踪信号

最终输出应回答:

  • 护城河强还是弱
  • 证据扎实还是偏叙事
  • 未来最重要的强化信号与削弱信号是什么

输出结构


{股票名称}护城河评估({YYYY-MM-DD})

一、结论摘要

  • 护城河等级:{强 / 中 / 弱 / 暂不成立}
  • 核心优势来源:{一句话}
  • 最大削弱风险:{一句话}

二、护城河命题

{用一段话明确当前要验证的竞争优势命题}

三、证据矩阵

优势类型 证据 当前判断 说明
{类型1} {证据} {成立/部分成立/不足} {说明}
{类型2} {证据} {成立/部分成立/不足} {说明}

四、持续性分析

有利于持续的因素

  • {因素 1}
  • {因素 2}

可能削弱的力量

  • {风险 1}
  • {风险 2}

五、对投资的意义

  • 对经营质量的影响:{说明}
  • 对回报率的影响:{说明}
  • 对估值的影响:{说明}

六、后续跟踪信号

  • 强化信号:{信号}
  • 削弱信号:{信号}
  • 需要继续验证的问题:{问题}

质量要求

  1. 护城河必须落到可验证证据,不能停留在口号和想象。
  2. 要区分短期领先与结构性优势,不能把阶段景气当壁垒。
  3. 必须同时写清增强因素与削弱因素,防止单边美化。
  4. 最终结论要说明对投资回报的意义,而不是只评价公司“好不好”。
  5. 若证据不足,应明确写“尚不能证明护城河成立”。
Changes

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.

  1. 1mo ago First seen · 139 lines · 62 tokens per session scan A 95adcb751595

Subscribe to this mod's changes

moat_strength_review_skill is a skill published in the GitHub repository chuancyzhang/deepseek-cowork (18 stars, last pushed today), licensed Apache-2.0. It adds 62 tokens to every session and 1,257 once invoked, about $0.0002 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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