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 charlieviettq/awesome-agent-skill --skill fin-m-and-agit clone --depth 1 https://github.com/charlieviettq/awesome-agent-skillWrote 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/charlieviettq/awesome-agent-skill/fin-m-and-a)<a href="https://agentmods.dev/skills/charlieviettq/awesome-agent-skill/fin-m-and-a"><img src="https://agentmods.dev/badge/skills/charlieviettq/awesome-agent-skill/fin-m-and-a/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/charlieviettq/awesome-agent-skill/fin-m-and-a"><img src="https://agentmods.dev/badge/skills/charlieviettq/awesome-agent-skill/fin-m-and-a.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 | $0.00217 | $0.05952 |
| Opus 5 | $0.00109 | $0.02976 |
| Sonnet 5 | $0.00043 | $0.01190 |
| Haiku 4.5 | $0.00022 | $0.00595 |
Grade A, and why
"fin-m-and-a" 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 9d 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
92% identical to fin-m-and-a — 9 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 — 391 lines — stays where its author put it; the contents beside it link to each section on GitHub.
併購交易整合 Playbook(M&A Integration Playbook)
定位
為什麼 EMBA 要學 M&A Playbook
M&A 是商學教育中最「跨領域」的主題:需要策略、財務、會計、稅務、法律、組織行為整合。多數 EMBA 學員(或其公司)在以下情境會遇到:
- 成長瓶頸需外部併購
- 被大型集團收購談判
- 投資 PE 基金後的併購決策
- 接班過程的家族股權重組
- IPO 前的策略性併購
- 國際擴張的跨境併購
本 skill 的定位:不是單純估值工具(DCF、可比公司法),而是整個交易生命週期的導航。估值只是其中一個模組。
與相近 Asgard skill 的邊界
biz-dcf— 估值技術(DCF 建模)fin-modeling— 財務建模工具biz-financial-ratios— 比率分析biz-value-chain— 策略工具biz-corporate-governance(本 repo)— 治理結構- 本 skill — 併購交易的整體 playbook,協調上述工具並加入 DD、交易結構、PMI、合約層
何時使用
觸發條件
- 任何併購案從「要不要做」到「怎麼做」的決策
- 估值報告爭議調解
- 交易結構設計(含跨國)
- SPA(Share Purchase Agreement)條款談判準備
- PMI 規劃與執行
- 敵意收購防禦/公開收購應對
- EMBA 財管組個案、企業併購模擬
不適用
- 單純 DCF 估值 → Asgard
biz-dcf - 純財務建模 → Asgard
fin-modeling - 投資組合理論 → Asgard
grad-fama-french - 單一上市公司財報分析 → Asgard
data-financial-analysis
IRON LAW — M&A 三條鐵律
IRON LAW 1:70% 併購案毀滅股東價值
多個學術研究(KPMG、BCG、McKinsey 長期追蹤)顯示:
買方股東 1 年後累積異常報酬為負的比例約 60–70%。
不是「為什麼要併購」,而是「為什麼不做時勢會更好」。
沒通過這個挑戰的併購案應放棄。
IRON LAW 2:綜效(Synergy)永遠被高估
成本綜效(裁員、共採)達成率約 70%;
營收綜效(交叉銷售、整合市場)達成率 < 30%。
任何營收綜效在估值模型中都該打 0.3 係數。
「保守估計綜效」是所有併購報告的底線。
IRON LAW 3:整合(PMI)在 Day 1 之前就要規劃
60% 的併購失敗可追溯到 PMI 規劃不足。
交易結束才開始想怎麼整合 = 已經輸一半。
DD 階段必須產出初版 100-day plan,簽約前完成詳版。
Rationalization Table — 當 Claude 想「本案例外」時,先自問
| 可能想 | 但 Iron Law 仍適用,因為 |
|---|---|
| 「這案子戰略合理、估值便宜,推薦進行」 | 仍要挑戰「不做時勢會不會更好」;若答案是「差不多」,傾向放棄而非推進 |
| 「承諾綜效是財務部認真估算的 X 億,應全額納入估值」 | 營收綜效達成率 < 30%,必須打 0.3 係數;成本綜效 × 0.7;財務綜效 × 0.5 |
| 「DD 完成後再規劃 PMI」 | 60% 併購失敗源於 PMI 晚;PMI 初版必須在 DD 階段就啟動、簽約前完成詳版 |
八大交易模組
┌───────────────────────────────────────┐
│ 模組 8:整合(PMI) │
│ 100-day plan、文化融合、人才保留 │
├───────────────────────────────────────┤
│ 模組 7:合約條款(SPA) │
│ 關鍵條款、Reps & Warranties │
├───────────────────────────────────────┤
│ 模組 6:交易結構 │
│ 股權 vs. 資產、支付工具、稅務 │
├───────────────────────────────────────┤
│ 模組 5:綜效分析 │
│ 營收/成本/稅務/財務四類 │
├───────────────────────────────────────┤
│ 模組 4:估值與估值橋 │
│ EV-to-Equity、三種方法交叉驗證 │
├───────────────────────────────────────┤
│ 模組 3:盡職調查(DD) │
│ 財稅/法律/商業三大類 │
├───────────────────────────────────────┤
│ 模組 2:目標篩選 │
│ 策略契合度、市場地位、可併性 │
├───────────────────────────────────────┤
│ 模組 1:戰略動機 │
│ Why M&A vs. Organic vs. JV │
└───────────────────────────────────────┘
What ships with it
8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 9d ago First seen · 391 lines · 217 tokens per session scan A 596b11508f5c
"fin-m-and-a" is a skill published in the GitHub repository charlieviettq/awesome-agent-skill (25 stars, last pushed 1mo ago), licensed MIT. It adds 217 tokens to every session and 5,952 once invoked, about $0.0011 per session on Opus 5. A static security scan graded it A with 0 findings. It is 92% identical to fin-m-and-a, differing in 9 lines, and is treated as a copy.
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