/deal-diligence-checklist

⚠️ Not legal, financial, or regulatory advice. This output is a drafting and triage aid generated by a product-organization skill, not counsel. No attorney-client relationship, investment advisory relationship, or regulatory-compliance relationship is created by its production or use. Jurisdiction-specific questions, contested matters, deal structure decisions, regulatory exposure assessment, and any decision with material legal, financial, or regulatory consequences require review by licensed attorneys, qualified financial advisors, and competent regulatory counsel in the relevant jurisdictions. Do not rely on this output as the sole basis for any deal-structuring, investment, integration, or regulatory decision.

Jurisdiction Assumed: {jurisdiction — default: target's home jurisdiction plus any jurisdictions in which the target derives material revenue, flagged explicitly in output}. If your jurisdiction differs, treat every finding below as a hypothesis to verify with local counsel.

1. Archetype Parameterization — Why One Skill, Not Five

An M&A due diligence checklist is not a static document. A strategic bolt-on and a financial buyout share very little diligence DNA: the bolt-on cares about capability absorption, the buyout cares about cost base and working capital release. An AI-first talent acquisition cares about training data consent flows, which a distressed turnaround cares about approximately not at all. The temptation is to produce five separate checklists. That temptation is wrong. Five separate checklists means five maintenance burdens, five drift surfaces, five opportunities for the hook ordering to diverge, and five places for the AI Target Addendum to get out of sync when the EU AI Act's next implementing regulation lands. The right structural choice is one skill parameterized by deal_archetype, sharing a single hook bank, a single layer framework, a single set of anti-patterns, and a single scaffolding discipline — with archetype-driven reweighting of hooks, workstreams, and layer emphasis as the output shape the user sees.

The --archetype input drives three transformations. First, it reweights the five EthosData layers from ma-value-stack so that the dominant layers for the archetype are foregrounded and the non-dominant layers are sent to a supplementary section. Second, it reorders the 25 base hooks from m-and-a-playbooks Section 7 into P0 (deal-killing — must answer before term sheet), P1 (material — must answer before close), and P2 (nice-to-have — post-close 100-day plan) using the hook-by-archetype weighting matrix in that pack. Third, when --archetype ai-first-target or --sector ai is set, it triggers the AI Target Addendum module (Section 11) that adds roughly 30 AI-specific questions covering EU AI Act, NIST AI RMF, FTC Section 5, OECD AI Principles, and the IP/data/talent retention questions that dominate AI-first deal risk. The archetype is the organizing principle; everything else in the skill is a reweighting of shared content against that principle.

2. Purpose

Produce a tailored, archetype-aware due diligence question set that: (a) mechanically consumes the 25 base hooks from m-and-a-playbooks v1.1.0 Section 7 with archetype-driven weighting, (b) foregrounds the dominant layers from ma-value-stack v1.0.0 for the selected archetype, (c) triggers domain-specific addenda (AI Target, sector modules, jurisdiction modules) on demand, and (d) identifies team capability gaps that could invalidate the diligence itself (e.g., no AI architect on an AI-first deal). The output is a structured list of questions with evidence type, responsibility, and completion milestone — not an analysis, not a recommendation, and not a substitute for substantive diligence.