olo-deal-memo

Investment memorandum generation for M&A — structured deal write-ups from the acquirer's perspective with data-backed analysis

3,891 stars

Best use case

olo-deal-memo is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Investment memorandum generation for M&A — structured deal write-ups from the acquirer's perspective with data-backed analysis

Teams using olo-deal-memo should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/olo-deal-memo/SKILL.md --create-dirs "https://raw.githubusercontent.com/openclaw/skills/main/skills/aniebyl/olo-deal-memo/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/olo-deal-memo/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How olo-deal-memo Compares

Feature / Agentolo-deal-memoStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Investment memorandum generation for M&A — structured deal write-ups from the acquirer's perspective with data-backed analysis

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

Related Guides

SKILL.md Source

# Investment Memorandum Generation

Generate structured investment memos from the acquiring company's perspective.

## Perspective

- Written **for** the acquiring company (use "we" for acquirer)
- Written **about** the target company (use company name or "the target")
- Align with acquirer's investment thesis and acquisition strategy
- Present balanced view: opportunity AND risk

## Memo Structure

### 1. Executive Summary (1 page)
- Transaction overview: target name, sector, deal size, structure
- Strategic rationale in 3-4 bullet points
- Key financial metrics (revenue, EBITDA, growth, valuation)
- Recommendation: Proceed / Proceed with Conditions / Pass

### 2. Company Overview
- Business description and history
- Products/services and revenue mix
- Customer base (count, concentration, retention)
- Management team assessment
- Organizational structure and headcount

### 3. Market & Competitive Position
- Industry overview and growth outlook (reference market intelligence)
- Competitive landscape and target's positioning
- Sustainable competitive advantages (moats)
- Key risks to market position

### 4. Financial Analysis
- Historical financials (3-5 years): revenue, EBITDA, margins, FCF
- Revenue quality: recurring %, customer concentration, cohort analysis
- Working capital dynamics and cash conversion
- CapEx requirements and capital intensity
- Key financial trends and inflection points

### 5. Valuation
- DCF analysis (base/bull/bear cases)
- Comparable company analysis (public comps)
- Precedent transaction analysis
- Implied valuation range and recommended offer price
- Sensitivity analysis on key assumptions

### 6. Strategic Rationale & Synergies
- Revenue synergies (cross-sell, market expansion, pricing)
- Cost synergies (overlap elimination, procurement, shared services)
- Timeline to achieve synergies (Year 1 / Year 2 / Year 3)
- Integration complexity and risk assessment
- Synergy value vs. premium paid analysis

### 7. Risk Assessment
- Deal-specific risks (top 5, ranked by impact × likelihood)
- Mitigation strategies for each risk
- Deal-breaker thresholds
- Sensitivity of returns to key risk scenarios

### 8. Transaction Structure & Returns
- Proposed structure (asset vs. stock, cash vs. equity mix)
- Sources and uses of funds
- Pro forma leverage and coverage ratios
- Expected returns: IRR, MOIC, payback period
- Key assumptions driving returns

### 9. Recommendation & Next Steps
- Clear recommendation with confidence level
- Conditions or diligence items to resolve
- Proposed timeline for next phase
- Required approvals and process steps

## Data Aggregation Strategy

1. Pull existing DD data from platform (fast, <1s)
2. Fill gaps with RAG queries over uploaded documents (medium, ~3s)
3. Augment with market context via web research (slower, ~5s)
4. Synthesize into narrative sections with AI (~5-10s)

## Quality Standards

- Every claim backed by data point with source
- Financial figures must reconcile across sections
- Clearly separate facts from assumptions from opinions
- Use conditional language for projections ("we estimate", "management projects")
- Flag data gaps explicitly rather than filling with generic text
- Total generation target: 15-20 seconds

## Output Formats

- **Markdown**: Primary format for platform display and editing
- **PDF**: Professional layout for IC distribution
- **PPTX**: Presentation format for deal committee meetings
- **Excel**: Supporting financial model and sensitivity tables

Related Skills

Deal Desk — Structured Deal Review & Approval

3891
from openclaw/skills

Run every non-standard deal through a repeatable review process. Catch margin leaks, enforce discount guardrails, and close faster with pre-approved terms.

Agent Memory Architecture

3891
from openclaw/skills

Complete zero-dependency memory system for AI agents — file-based architecture, daily notes, long-term curation, context management, heartbeat integration, and memory hygiene. No APIs, no databases, no external tools. Works with any agent framework.

memory-cache

3891
from openclaw/skills

High-performance temporary storage system using Redis. Supports namespaced keys (mema:*), TTL management, and session context caching. Use for: (1) Saving agent state, (2) Caching API results, (3) Sharing data between sub-agents.

General Utilities

Memory

3891
from openclaw/skills

Infinite organized memory that complements your agent's built-in memory with unlimited categorized storage.

Memory Management

auto-memory

3891
from openclaw/skills

Indestructible agent memory — permanently stored, never lost. Save decisions, identity, and context as a memory chain on the Autonomys Network. Rebuild your full history from a single CID, even after total state loss.

AI Persistence & Memory

Triple-Layer Memory System

3880
from openclaw/skills

三层记忆系统 - 解决 AI Agent 长对话记忆丢失和上下文管理问题

Memory & Context Management

agent-memory-os

3891
from openclaw/skills

Stop agents from "forgetting, mixing projects, and rotting over time" by giving them a practical memory operating system: global memory, project memory, promotion rules, validation cases, and a maintenance loop.

benos-memory-core

3891
from openclaw/skills

Core runtime/volatile memory module for BenOS agent environment. Use to: store and retrieve active session state, open loops, decisions, and scratch notes at runtime.

supermarket-deals

3891
from openclaw/skills

Search German supermarket flyers (Aldi, Lidl, REWE, EDEKA, Kaufland) for product deals via Marktguru. Results ranked by best price per litre (EUR/L). No API key needed.

elite-longterm-memory

3891
from openclaw/skills

Ultimate AI agent memory system with WAL protocol, vector search, git-notes, and cloud backup. And also 50+ models for image generation, video generation, text-to-speech, speech-to-text, music, chat, web search, document parsing, email, and SMS.

memory-agent

3891
from openclaw/skills

维护用户审美偏好与创作历史,为其他 Agent 提供可复用的风格参考。当开始新任务或用户表达喜好时触发。

bamdra-memory-upgrade-operator

3891
from openclaw/skills

Safely install, uninstall, reinstall, or upgrade the Bamdra OpenClaw memory suite when stale config, existing plugin directories, or partial installs break normal `openclaw plugins install` flows.