---
name: olo-deal-screening
version: 1.0.0
description: Target company evaluation and deal qualification for PE and strategic buyers
author: ololand.ai
author_url: https://ololand.ai
license: MIT
triggers:
  - deal screening
  - target evaluation
  - investment criteria
  - acquisition criteria
  - deal qualification
  - deal fit
  - buyer criteria
  - deal scoring
tags:
  - finance
  - screening
  - m-and-a
  - due-diligence
  - private-equity
---

# Deal Screening for M&A

Score and qualify acquisition targets against buyer investment criteria.

## Screening Framework

Evaluate targets across five dimensions, each scored 0-100:

### 1. Strategic Fit (25% weight)
- Industry/sector alignment with buyer portfolio
- Geographic fit (markets, operations, customer base)
- Product/service complementarity
- Technology or capability gap fill
- Brand and market position value

### 2. Financial Profile (25% weight)
- Revenue scale (minimum threshold check)
- Revenue growth trajectory (3-year trend)
- EBITDA margin vs. industry benchmark
- Revenue quality (recurring vs. one-time, customer concentration)
- Working capital efficiency

### 3. Valuation Attractiveness (20% weight)
- EV/EBITDA vs. comparable transactions
- EV/Revenue vs. sector median
- Implied IRR at estimated purchase price
- Multiple arbitrage potential (buy low, exit higher)

### 4. Risk Profile (15% weight)
- Customer concentration (top 10 customers as % of revenue)
- Key-person dependency
- Regulatory exposure
- Technology obsolescence risk
- Litigation or compliance issues

### 5. Execution Feasibility (15% weight)
- Management team quality and retention likelihood
- Integration complexity estimate
- Competitive auction dynamics
- Seller motivation and timeline
- Financing availability

## Scoring Output

```
Overall Fit Score: 78/100 — PROCEED TO DD

Strategic Fit:     85/100 ████████░░
Financial Profile: 72/100 ███████░░░
Valuation:         80/100 ████████░░
Risk Profile:      68/100 ██████░░░░
Execution:         82/100 ████████░░

Recommendation: PROCEED TO DD
Key Strengths: [top 3]
Key Concerns: [top 3]
Suggested Next Steps: [prioritized actions]
```

## Thresholds

| Score Range | Recommendation |
|-------------|----------------|
| 80-100 | Strong fit — prioritize for DD |
| 65-79 | Good fit — proceed with caution |
| 50-64 | Marginal — requires strategic justification |
| Below 50 | Poor fit — pass unless compelling thesis |

## Deal-Breaker Checks (Auto-Fail)

Before scoring, check for absolute disqualifiers:
- Revenue below buyer's minimum threshold
- Negative EBITDA (unless growth-stage thesis)
- Active material litigation exceeding 20% of EV
- Sanctioned entities in ownership chain
- Industry explicitly excluded by buyer mandate

## PE-Specific Criteria

For financial sponsor buyers, additionally evaluate:
- **LBO feasibility**: Can the deal be levered 3-5x EBITDA?
- **Value creation levers**: Revenue growth, margin expansion, add-ons, multiple expansion
- **Exit path**: IPO viability, strategic buyer universe, sponsor-to-sponsor
- **Hold period returns**: Target 20-25% gross IRR over 3-5 years
- **Fund fit**: Check size, vintage, sector focus, geographic mandate

## Output Format

Provide structured JSON-compatible output with:
- `overall_score`: 0-100
- `recommendation`: proceed_to_dd | proceed_with_caution | pass
- `dimension_scores`: object with each dimension
- `deal_breakers`: list of any auto-fail conditions triggered
- `strengths`: top 3 positive factors
- `concerns`: top 3 risk factors
- `next_steps`: prioritized action items


---
Source attribution: modbender
Original project: https://github.com/modbender/skill-library-mcp/blob/d9c8fe903b5b4e82237dbe8d0df8567783022e18/data/olo-deal-screening/SKILL.md
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2025 skill-library-mcp contributors

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: modbender
Original project: https://github.com/modbender/skill-library-mcp/blob/d9c8fe903b5b4e82237dbe8d0df8567783022e18/data/olo-deal-screening/SKILL.md
Attribution basis: Source repository record

Fields of work: Finance & Accounting, Marketing, Sales
Roles: Account Executive, Business Development, CFO / FP&A, Financial Analyst, Growth Marketer
Specialties: Specialty not specified
Tools: Json
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: 2d339f2262364cade9ece788398ebe91f8612c63412774bdb290525cd1f04e7f