--- name: project-finance-model-assumption-checker description: "Reviews and stress-tests the core assumptions in an infrastructure project finance model — demand/traffic forecasts, construction cost and contingency, debt sizing and DSCR, IRR sensitivity, and refinancing risk — and returns a structured assumption review with red-flagged items, benchmark comparisons, and recommended stress cases. Use when scrutinizing a project finance model before investment committee, financial close, lender diligence, or a bid submission." argument-hint: "[asset-class] [model-assumptions]" allowed-tools: ["Read"] --- # Project Finance Model Assumption Checker ## Purpose Systematically interrogate the assumptions driving an infrastructure project finance model and surface where they are aggressive, unsupported, or internally inconsistent — producing a red-flagged assumption review with benchmark comparisons and specific stress cases to run before committing capital. ## When to Use Invoke this skill when the user asks to: - Review or sanity-check the assumptions in a project finance model before investment committee - Stress-test demand, cost, or leverage assumptions ahead of financial close - Prepare for lender or independent-engineer diligence on a model - Validate a base case before a concession bid or acquisition - Diagnose why a model's returns look too good (or a deal looks too tight) - Build a red-flag list on an infrastructure asset (toll road, airport, power, water, digital, social PPP) ## Inputs Required Before starting, collect: - **Asset class & structure**: Sector (toll road, PPP, renewable, thermal, water, port, digital) and contract type (availability, merchant, regulated, concession) - **Concession/tenor terms**: Concession length, debt tenor, construction period, operations start date - **Demand/revenue assumptions**: Base-case traffic/volume/generation forecast, ramp-up curve, growth rate, tariff/price and escalation - **Cost assumptions**: Total capex, construction cost breakdown, contingency %, opex profile, major maintenance/lifecycle reserve - **Financing assumptions**: Debt/equity split (gearing), interest rate, DSCR target/lock-up, sculpting, refinancing assumptions - **Return metrics**: Projected equity IRR, project IRR, and any hurdle rate - **Base-case sensitivities available**: Any downside cases already run (and results) If specific figures are missing, flag the gap explicitly, apply sector-typical ranges as a proxy, and note the assumption. ## Instructions ### Step 1: Demand / Revenue Forecast Review Test whether the top line is credible. - **Ramp-up realism**: Does the model assume full demand at year 1? Greenfield traffic/volume typically ramps over 3–5 years — an instant-maturity assumption is a red flag. - **Growth rate vs. GDP**: Compare demand CAGR to relevant GDP/sector growth. Sustained demand growth well above long-run GDP (e.g., >1.5–2.0x GDP over 20+ years) is aggressive without a specific driver. - **Forecast provenance**: Is there an independent traffic/demand study, or is the forecast sponsor-generated? Flag optimism bias — traffic forecasts historically overstate year-5 volumes by 20–30% on average. - **Price/tariff escalation**: Is escalation contracted (CPI-linked) or assumed? Merchant price assumptions above forward curves are a red flag. - **Elasticity**: For tolled/priced assets, does the model account for demand response to tariff increases? ### Step 2: Construction Cost & Contingency Review Test whether the project can be built for the budgeted number. - **Contingency adequacy**: Greenfield contingency typically 5–15% depending on complexity and design maturity. Below 5% on a complex build, or contingency assumed unspent, is a red flag. - **Fixed-price coverage**: What share of capex is under a fixed-price date-certain EPC/turnkey contract vs. exposed to cost overrun? Quantify the unhedged exposure. - **Escalation during construction**: Is input-cost inflation (steel, labor, concrete) modeled over the construction period? - **Delay/LD interplay**: Does the model rely on liquidated damages to offset delay cost? Confirm LD caps cover the modeled downside. - **Interest during construction (IDC)**: Confirm IDC is capitalized and consistent with the drawdown schedule. ### Step 3: Debt Sizing & DSCR Review Test whether the debt structure holds under stress. - **DSCR headroom**: Minimum and average DSCR vs. lock-up/default covenants. Base-case minimum DSCR at or barely above the covenant (e.g., <1.10x cushion to lock-up) is a red flag. - **Sector benchmarks**: Availability/contracted PPP min DSCR ~1.15–1.25x; merchant/demand-risk assets require higher cushion ~1.40x+. Compare to what's modeled. - **Sculpting**: Is debt sculpted to a target DSCR? Confirm sculpting doesn't push repayment into weaker later years. - **Gearing**: Is leverage consistent with the risk profile? Over-gearing relative to cash-flow certainty is a red flag. - **Reserve accounts**: Confirm DSRA (typically 6-month), MMRA/lifecycle reserve are funded and modeled as cash outflows. ### Step 4: Refinancing Risk Review Test the exposure at debt maturity. - **Mini-perm / bullet exposure**: If debt tenor is shorter than concession, quantify the refinancing lump at maturity and the assumed refi rate. - **Refi rate assumption**: Is the assumed future refinancing rate at or below today's rate? Assuming favorable future rates is a common red flag. - **Tail**: Is there a debt-free operational tail (typically ≥2 years, ideally 15%+ of concession) as a cushion for lenders? - **Cash sweep**: Is there a cash-sweep mechanism if refinancing fails or DSCR deteriorates? ### Step 5: IRR Sensitivity & Internal Consistency Test the returns and cross-check the whole model. - **Return vs. risk**: Does the equity IRR look reasonable for the risk profile, or "too good"? Contracted availability assets returning merchant-level IRRs signal an assumption error. - **Sensitivity gaps**: Identify which downside cases are missing (demand −20%, capex +15%, delay +6 months, interest +200bps, opex +10%). - **Breakeven identification**: Find the single assumption to which IRR/DSCR is most sensitive. - **Consistency checks**: Inflation applied consistently to revenue and costs? Tax, depreciation, and terminal/residual value treated correctly? Currency of revenue vs. debt matched? ### Step 6: Compile the Red-Flag Register & Stress Cases - Consolidate all flagged items into a severity-ranked register (High / Medium / Low). - For each High/Medium flag: state the assumption, why it's a concern, the benchmark it deviates from, and the recommended fix or test. - Specify 4–6 concrete stress cases to run, each with the variable moved, the magnitude, and the metric to watch (min DSCR, equity IRR, breakeven). ## Output Format - **Structure**: Five assumption-area reviews, followed by a severity-ranked red-flag register and a stress-case table - **Length**: 1,000–1,400 words - **Format**: Structured document — headed sections with bullets; red-flag register and stress cases as tables - **Sections**: Demand/Revenue → Construction Cost & Contingency → Debt Sizing & DSCR → Refinancing Risk → IRR Sensitivity & Consistency → Red-Flag Register (ranked) → Recommended Stress Cases - **Tone**: Investment-committee reviewer — skeptical, quantitative, plain about where the model is optimistic; no hedging on flags ## Quality Checklist Before delivering output, verify: - [ ] Every one of the five assumption areas is reviewed against a stated benchmark or threshold — not general commentary - [ ] Each red flag names the specific assumption, the deviation, and a recommended fix or test - [ ] The red-flag register is ranked by severity (High/Medium/Low), not listed arbitrarily - [ ] Minimum and average DSCR are assessed against a sector-appropriate covenant cushion - [ ] 4–6 stress cases are specified with variable, magnitude, and the metric to watch - [ ] Any missing inputs are explicitly flagged with the proxy assumption used ## Notes - The most common fatal flaw in infrastructure models is demand optimism, not cost — apply the most skepticism to greenfield ramp-up and traffic forecasts. - Distinguish contracted-cash-flow assets (availability PPP, contracted renewables) from demand-risk assets (toll roads, merchant power) — they justify entirely different DSCR and IRR benchmarks; never apply one asset class's thresholds to another. - A base case that already looks tight is safer than one that looks pristine — flag any model where every assumption breaks the sponsor's way. - Recommend the user commission an independent technical/demand study for any High-severity flag before financial close. --- ## How to use this skill This file is a Claude Code skill in [Anthropic's standard format](https://docs.anthropic.com/en/docs/claude-code/skills) — markdown with YAML frontmatter at the top. **Installing in Claude Code:** - `~/.claude/skills//SKILL.md` — auto-loads in every session anywhere on your machine (recommended for skills you'll use across multiple projects) - `/.claude/skills//SKILL.md` — auto-loads only when working in that project (recommended for project-specific skills) Restart Claude Code or open a new session to register. **Using in Claude.ai, ChatGPT, Gemini, or other AI tools:** 1. 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