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Deal architect

Skill alzadjaliaafra-hash/murshidi-knowledge-layer/models/deal-architect

Modular, fine-tuning-ready knowledge architecture for financial-domain LLMs — 9 domain models, each an activatable skill with knowledge corpus, instruction dataset, and held-out evals.

Install
npx -y skills add alzadjaliaafra-hash/murshidi-knowledge-layer --skill deal-architect

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Deal structuring and execution toolkit — pro-forma modelling, enterprise value bridge, accretion/dilution, LBO screening, goodwill allocation, and the full HBS alternative-investments framework covering PE fund structure, venture capital, growth equity, buyouts, distress investing, rescue financing, and secondaries. Activate with /deal, "M&A", "LBO", "acquisition", "accretion", "buyout", or "deal structure".

SKILL.md

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Deal Architect

An M&A and private-equity execution framework combining a quantitative deal toolkit with the alternative-investments strategy canon.

M&A Execution Toolkit

The standard analysis sequence for any acquisition:

  1. Pro-forma income statement — acquirer + target + synergies − integration costs − incremental intangible amortisation
  2. Enterprise value bridge — market cap + debt + preferred + non-controlling interest − cash = EV
  3. Comparable company valuation — EV/EBITDA, EV/Revenue, P/E with implied equity and per-share values
  4. Accretion/dilution — pro-forma EPS vs. acquirer standalone EPS, with financing-mix sensitivity (equity issuance, new debt, cash on hand)
  5. Sources & uses — must reconcile to zero, always
  6. Goodwill & intangibles — purchase price − book equity = excess; allocate to identifiable intangibles with defined useful lives; the remainder is goodwill (no amortisation, annual impairment test)
  7. Deal interpretation — accretive or dilutive, implied multiples vs. comps, financing risk, exit positioning

LBO Mechanics

Quick screen: target EBITDA × maximum leverage multiple = debt capacity. Full sequence: financial projections → exit-year EBITDA → exit multiple → enterprise value → subtract net debt → equity value → sponsor share → IRR and Multiple of Money.

The value creation equation governs every buyout thesis:

ΔEquity = Δ(Multiple × EBITDA) − (ΔDebt − ΔCash)

Three levers, in declining defensibility: EBITDA growth, cash generation (net debt reduction), and multiple expansion. A thesis resting on multiple expansion alone is a market-timing bet, not a value-creation plan.

Private Equity Strategy Frameworks

StrategyValue-addPrincipal risksMitigation
Venture CapitalDomain expertise, execution, go-to-marketPeople, product, marketActive sourcing, staged investing
Growth EquitySourcing, diligence, termsInformation uncertainty, minority controlSourcing expertise, capital-structure design
BuyoutsCapital investment, operational improvementExecution, exit timingControl, leverage discipline
Emerging MarketsOperational improvement via minority stakesMacro, currency, informationLocal partnerships, structure

Fund structure: GP/LP dynamics across a 10-year lifecycle (5-year investment period + 5-year harvesting). Returns measured by MoM = realized equity value ÷ initial equity investment, and IRR.

Distress investing: buy debt strategically to emerge as the post-restructuring equity holder ("loan-to-own"); value creation primarily via multiple recovery in companies with sound business models but broken balance sheets.

Rescue financing: private-debt investing conducted openly with senior management. The three value inputs are Capital (deep reserves), Certification (reputation effect on other stakeholders), and Expertise (structuring capability). Incremental returns derive from illiquidity, diligence depth, smaller company size, and operational improvement.

Secondaries: LP-stake purchases at a discount give buyers immediate cash-flow access and mitigate the J-curve; sellers gain liquidity. Execution depends on sourcing, diligence, and disciplined discount pricing.

Output Protocol

Every deal analysis shows sources & uses reconciled to zero, the EV bridge, the accretion/dilution result with sensitivity, the LBO return decomposition against the value creation equation, and a verdict with the trade-offs named.

Resources

  • knowledge/methodology.md — toolkit walkthrough with worked examples
  • dataset/train.jsonl — instruction-tuning pairs
  • eval/eval.jsonl — held-out evaluation questions

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