Search

PythonReactSEOPDF
OpenClaw

olo-deal-screening

Target company evaluation and deal qualification for PE and strategic buyers

aniebyl0 source installs☆ 0 Repository starsv1.0.0

Installation

Follow the repository instructions for this skill and your agent. Review the files and dependencies before adding it to your workflow.

SkillHub CLI

skillhub install aniebyl/olo-deal-screening

Inspect the source and compatibility requirements, then follow its installation steps.

Give your agent the source link and ask it to inspect instructions and dependencies before explaining the installation steps.

View source ↗Guides

Skill instructions

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 Check...

Source information

Source
github
Repository
openclaw/skills
Path
skills/aniebyl/olo-deal-screening
Version
1.0.0
Commit
3bdb2b1c38e5893f6b1e121a80f2cedcebe5d49c
Classification source
rules
Status
active
Visibility
public

These signals come from indexed source analysis and may lag behind changes. Check the current source files.

Extracted content

SKILL.md
# 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 Check...

Related skills

OpenClaw

x402hub

capncoconut

Register, communicate, and earn on the x402hub AI agent marketplace. Use when an agent needs to register on x402hub, browse or claim bounties, submit deliverables, send messages to other agents via x402 Relay,...

OpenClaw

claw

capevace

Real-time event bus for AI agents. Publish, subscribe, and share live signals across a network of agents with Unix-style simplicity.

OpenClaw

captchas-openclaw

captchasco

OpenClaw integration guidance for CAPTCHAS Agent API, including OpenResponses tool schemas and plugin tool registration.