Purpose
Convert a raw list of potential acquisition targets into a defensible, weighted, and ranked shortlist — scoring each candidate against a defined strategic and financial rubric and adding fit commentary — so corporate development can focus diligence resources on the highest-conviction targets and defend the shortlist to an investment committee.
When to Use
Invoke this skill when the user asks to:
- Screen or evaluate a list of potential acquisition targets
- Prioritize an M&A pipeline or inbound deal flow
- Build a ranked acquisition shortlist for an investment committee or board
- Score targets against a strategic thesis or acquisition criteria
- Compare multiple candidates on strategic and financial fit
- Filter a long list of companies down to a diligence-ready set
Inputs Required
Before starting, collect:
- Target list: The candidate companies to screen — names plus whatever data is available (revenue, growth, margins, geography, product/capability)
- Strategic rationale: The thesis driving the M&A search (e.g., enter a new geography, acquire a capability, consolidate a category, add recurring revenue)
- Strategic criteria and weights: The dimensions that matter and their relative importance (capability fit, geography, customer overlap, technology, team)
- Financial criteria: Target ranges for revenue size, growth rate, and margin profile — plus any hard filters (minimum revenue, profitability requirement)
- Deal constraints: Approximate budget/valuation ceiling, structure preferences (majority vs. tuck-in), and integration capacity
- Dealbreakers: Absolute disqualifiers (e.g., no distressed assets, no companies below X% gross margin, no specific geographies)
If weights are not provided, apply a default balanced weighting (strategic fit 50%, financial profile 35%, integration feasibility 15%) and state the assumption. If data is missing for a candidate on a given criterion, score it as "Unknown / diligence required" rather than guessing.
Instructions
Step 1: Establish the Scoring Rubric
Before scoring any candidate, lock the rubric so scores are comparable:
- Convert each strategic and financial criterion into a 0–5 scale with explicit anchors (e.g., Capability Fit: 5 = directly fills the named capability gap; 3 = adjacent/partial; 1 = tangential; 0 = irrelevant)
- Confirm the weights for each criterion; normalize so they sum to 100%
- Identify hard filters (dealbreakers) — any candidate failing one is flagged as disqualified regardless of weighted score
- Define the financial bands (revenue, growth, margin) as scoring anchors, not just pass/fail — a candidate slightly outside a range should lose points, not be eliminated, unless it's a stated hard filter
Step 2: Apply Hard Filters First
Run every candidate through the dealbreaker filters. For each disqualified candidate, record the single specific reason (e.g., "Below $10M revenue floor," "In excluded geography — LATAM," "Sub-40% gross margin"). Disqualified candidates are set aside and reported separately, not scored on the full rubric.
Step 3: Score Surviving Candidates Across Five Dimensions
For each remaining candidate, assign a 0–5 score with a one-line justification on each:
1. Capability & Product Fit — Does the target fill the specific gap in the thesis? Score against the named capability, not "good business overall."
2. Financial Profile — Composite of revenue size (scale relevance), growth rate (trajectory vs. target band), and margin profile (quality of earnings). Note where each sub-metric lands relative to the stated bands.
3. Market & Geographic Fit — Does the target extend reach into a priority geography, segment, or customer base? Score customer overlap (accretive vs. redundant) explicitly.
4. Integration Feasibility — Technology stack compatibility, cultural/operational similarity, size digestibility relative to the acquirer, and integration capacity. Larger and more complex targets score lower here.
5. Strategic Optionality — Does owning this target create follow-on moves, defensive value, or a platform for further roll-up? Distinguish one-time value from compounding value.
Step 4: Calculate Weighted Scores and Rank
- Multiply each dimension score by its weight and sum to a weighted composite (0–100 scale)
- Rank all surviving candidates from highest to lowest
- Assign each candidate a pursuit tier:
- Tier 1 — Pursue Now: composite ≥ 75, no critical unknowns
- Tier 2 — Develop / Diligence: composite 55–74, or high score with key data gaps
- Tier 3 — Watch / Deprioritize: composite 40–54
- Below 40 or disqualified: pass, with reason
Step 5: Write Per-Candidate Strategic Fit Commentary
For each Tier 1 and Tier 2 candidate, write 2–4 sentences covering:
- The single strongest reason to pursue (tied to the thesis)
- The most material risk or unknown (integration, valuation, data gap, competitive process)
- A recommended next action (e.g., "Initiate outreach," "Request financials before advancing," "Confirm customer overlap before valuing")
Step 6: Produce the Shortlist Summary
Close with a portfolio-level read: how many candidates cleared each tier, whether the pipeline is deep enough to support the thesis, the top 3 targets in priority order, and any gap in the pipeline the search should be expanded to fill.
Output Format
- Structure: Rubric summary → disqualified list → scored ranking table → per-candidate commentary → shortlist summary
- Length: 900–1,400 words, scaling with number of targets
- Format: A ranked scoring table (candidate, five dimension scores, weighted composite, tier) followed by structured commentary blocks
- Sections: Scoring Rubric & Weights → Disqualified Targets (with reasons) → Ranked Scoring Table → Strategic Fit Commentary (Tier 1 & 2) → Shortlist Summary & Recommendation
- Tone: Corporate development director — analytical, evidence-anchored, decisive; comfortable stating a recommendation and naming risks, not hedging
Quality Checklist
Before delivering output, verify:
- [ ] Every candidate is accounted for — either scored, tiered, or explicitly disqualified with a stated reason
- [ ] Each dimension score has a one-line justification, not a bare number
- [ ] Weights are stated, sum to 100%, and are actually applied in the composite calculation
- [ ] The ranking table sorts candidates by weighted composite, highest to lowest
- [ ] Missing data is flagged as "diligence required" rather than silently assumed
- [ ] Every Tier 1 and Tier 2 candidate has commentary with a strongest reason, a key risk, and a next action
- [ ] The shortlist summary names the top 3 targets in explicit priority order
Notes
- Integration feasibility is the most frequently underweighted dimension — a strategically perfect target that the acquirer cannot digest is a lower-conviction deal than the score alone suggests. Weight it deliberately.
- Where a candidate scores high but rests on unverified financials, place it in Tier 2 (diligence) rather than Tier 1 — the tier should reflect conviction, not just the raw number.
- Redundant customer overlap is often mistaken for synergy; a target whose customers you already own adds less strategic value than one that opens a net-new base. Score overlap as a distinct factor.
- This screen sizes and ranks a pipeline; it is not a valuation. Flag when the strongest strategic fit is likely to command a premium the deal constraints won't support.
How to use this skill
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