agentsclimarketplace

Company analyzer

Skill Haochenhust/ch-skills/skills/company-analyzer

Open-source skills — shareable & installable

Install
npx -y skills add Haochenhust/ch-skills --skill company-analyzer

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 8 stars8 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Analyze a company through three layers — story, logic, judgment — and deliver a narrative-driven report with a clear take. Use whenever the user asks to research, analyze, or deeply understand a specific company, whether the motivation is investing, career, competitive intel, partnership, or curiosity. Trigger even when the user says "研究 XX 公司", "深度分析 XX", "带我看懂 XX 这家公司", or names a company alongside investment/career/competition context without explicitly saying "analyze". NOT for quick fact lookup, real-time stock quotes, or broad multi-company surveys.

SKILL.md

8.6 KB, as published. Nobody here has run it

Company Analyzer

Produces a company analysis organized as Story × Logic × Judgment — a deliberate departure from SWOT-style templated reports. The three layers map to how humans actually understand a business: the narrative arc makes it memorable, the economic logic makes it rigorous, and the forced judgment makes it useful.

The benchmark for style and depth is 《晚点 LatePost》-class industry writing (see references/style-benchmark.md for an annotated example). If the output reads like a translated McKinsey slide with English jargon sprinkled in, it failed — even if all the facts are correct.

When to use

  • "分析一下 XX 公司" / "深度研究 XX" / "我想搞懂 XX 这家公司"
  • Weighing an investment, job, or partnership with a specific company
  • Wants to understand the company as a thesis, not a data dump

When NOT to use

  • Quick factual lookup (founding year, headcount) — answer directly
  • Real-time prices, intraday earnings reactions — route to a market data tool
  • Comparing ≥5 companies at once — depth-first, not breadth-first
  • Pure concepts/technologies (Transformer, RAG) — not a company

Writing style (非协商)

This is the hardest part to get right and the easiest to fail. Before writing any section, hold the following rules in memory.

  1. 母语化中文,不要自造中英夹杂术语。"护城河"说护城河,不要说 "moat";"资本配置"说资本配置,不要说 "capital allocation";"工艺 know-how" 最多保留 "know-how" 这样已被中文业界吸收的词。行业通用英文缩写(CAGR、PE、TIER 1、ROE、FCF)可保留;自造英译概念(process power、moat type、unit economics 当小标题用)不要。
  2. 小标题必须是带动词的一句完整论断,不是名词短语。
    • 好:「过往的路径依赖,让公司错失了新能源车的增长红利」
    • 差:「历史沿革」「业务模式」「护城河分析」
  3. 每段话都要自我解释因果。不写"它有规模优势",写"它在 X 细分市场占 Y% 份额,这让它能摊薄 Z 这条固定成本——但同一份额在 W 市场并不成立"。
  4. 因果链至少 3 层深。事件 → 为什么发生 → 业务影响 → 投资含义。停在任何中间层都是懒惰。
  5. 段间用中文连接词过渡,不要靠 markdown 视觉结构硬拼(无 emoji、无表格作为论证主体——表格只用于展示分段数字)。
  6. 数字要带解释。不要"营收 147 亿元,同比 +4.83%",要"营收 147 亿元,同比只 +4.83%,而扣非反而 -4.39%——意味着这 4.83% 增速里一多半是卖资产,不是主业。"

Workflow

Phase 0 — Scope alignment (可跳过)

Default: use AskUserQuestion to confirm three things before any web search:

  1. Target disambiguation — "Cursor" = 编辑器 (Anysphere) 还是概念?
  2. Purpose — 投资 / 求职 / 竞争情报 / 好奇. 决定哪一层权重最重.
  3. Depth — quick / standard / deep (见下方 length quota).

跳过条件: 用户在请求里已经明确指定了公司 + 目的 + 深度(或者深度从上下文清楚),直接进入 Phase 1,不要为了"按流程"打断用户. 但如果任一维度歧义(公司名有两个候选 / 没说目的 / 没说深度),必须问.

Phase 1 — Scenario branching

按公司生命周期选 ONE. 下列权重是叙事重心,不是字数机械切分.

  • A — Early-stage (<3y): Story 60% / Logic 25% / Judgment 15%. 创始人 DNA 主导;护城河是假设.
  • B — Growth (3–10y): Story 40% / Logic 40% / Judgment 20%. 商业模式走完第一个周期.
  • C — Mature (10y+, private): Story 30% / Logic 45% / Judgment 25%. 数字和结构位置主导.
  • D — Public company: 同 C,额外叠加财务层 — 见 references/financial-analysis.md.

Phase 2 — Three-layer parallel execution

派 3 个子 agent 并行. 每个 agent 负责一层,收到 prompt 后要做的第一件事是独立核实父 agent 传过来的初始事实——创始人履历、年份、关键数字这些,如果和公开资料冲突,以独立核实为准,不要照搬父 agent 的叙述。父 agent 也会出错。

  • Sub-agent 1 — Story layer. Read references/story-patterns.md.
    • 产出:创始人的来路 + 关键决策(不是流水账). 为 Mature / Public 公司,Story 可以压缩为"三到四个决定公司命运的分叉";不需要给每家公司做电影式开场.
    • 对早期公司 (A/B) 可以有场景化细节;对 Mature / Public 公司以战略事件为主(并购、合资股权比例、高管变动、重大监管事件、关键客户签入或丢失).
  • Sub-agent 2 — Logic layer. Read references/business-logic.mdreferences/moat-types.md、和 references/industry-structure.md.
    • 必须先做"行业结构分析"再讲公司. 行业层次是什么(细分应用市场各占多少、集中度如何、技术壁垒高低). 公司在这张地图上的坐标是什么. 如果上来就写"公司业务模式如何……",是失败.
    • 产出:行业结构 → 公司定位 → 收入/成本机制 → 护城河 → 配对比较(强制).
    • 配对比较是必做项,不是可选. 选 1 个最能说明问题的对手做深度对比(该对手做对了什么、该公司做错了什么,或反之). 如果找不到合适对手,要命名理由(为什么这是一个孤立市场),不能跳过.
  • Sub-agent 3 — Judgment layer. Read references/anti-patterns.md first. 在 Story + Logic 落地后运行.
    • 产出:公司现在在生命曲线的哪里、未来 3–5 年的赌注(2–3 个可证伪的杠杆)、命名立场("我会"或"我不会"或"我会等+触发条件"——骑墙算失败)、5 个监控指标.

Phase 3 — Synthesis

主 agent 把三层编织成一篇文章,不是三段拼接. 语言风格参照 references/style-benchmark.md 的《晚点》示例:平白中文、整句论断作标题、段间用"但问题在于"/"对比而言"/"所以"做过渡. 不保留英文术语标签作为 section header(不要出现 "### Moat Analysis" 这种东西).

Length quota

量化配额(中文字符数),低于下限视为研究深度不够,高于上限视为灌水.

  • Quick: 2000–3500 字
  • Standard: 7000–10000 字
  • Deep: 15000–22000 字

(注:这是 synthesis 后最终产出字数,不是三层草稿相加;单层草稿会在合成时被压缩和穿插.)

Anti-patterns (必避)

详见 references/anti-patterns.md. 短清单:

  1. 流水账年表 — "2015 年 A,2016 年 B……"
  2. 咨询八股 — 赋能 / 抓手 / 闭环 / 打造 / 生态位;英文 synergize / leverage / holistic / empower.
  3. 自造中英夹杂术语 — "moat"、"process power"、"capital allocation"、"unit economics"、"go-to-market"、"stickiness" 作为中文段落里的概念词出现.
  4. 模板骨架 — SWOT、波特五力、PEST 作为 section header.
  5. 财报文字版 — 把 10-K 数字粘贴成段落而不解读.
  6. 骑墙立场 — "既有机遇也有挑战".
  7. 综上所述 / In conclusion 总结.
  8. 名词性标题 — 章节标题是"业务模式"而不是"业务模式决定它只能做中等生意"这种带论断的句子.

Progressive disclosure (参考文件)

  • references/style-benchmark.md — 《晚点 LatePost》风格标注示例(v2 新增)
  • references/industry-structure.md — 如何做行业结构分析(v2 新增)
  • references/story-patterns.md — 五种叙事形态(成熟公司可简化使用)
  • references/business-logic.md — 收入机制 + 商业性质 + 管理层分析
  • references/moat-types.md — 7 类护城河 + 监管/IP/资本(用作思考工具,输出时用中文自然语言)
  • references/financial-analysis.md — 上市公司财务 4 步读法
  • references/anti-patterns.md — 详细负面清单 + 重写对照
  • templates/report-template.md — 建议骨架(标题按一句话论断写)

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.