scenario-modeling

When to use

• A board pack, fundraise process, or annual plan needs base / upside / downside scenarios — not a single forecast number with a sensitivity table bolted on.
• A strategic bet (build / buy / partner, geo-expansion, pricing change) needs the shape of its downside before it locks; "what's the worst defensible outcome" is the load-bearing question.
• A founder or finance-partner needs to compare two options by their optionality, not their expected value — the option whose downside is bounded wins, not the option with the highest mean.

Do NOT use for per-customer economics (route to unit-economics-modeling (O1)), forecast-call construction (route to forecasting (O2)), or runway-shape reasoning (route to runway-cognition (O3)). This skill composes all three; it doesn't replace them.

Cognition cluster

• Mental model 21 — Second-order thinking. Each scenario is a chain: revenue → margin → burn → runway → fundraise → dilution. Single-statement scenarios (just revenue) skip the chain and read like wishlists. See mental-models.md § 21.
• Mental model 29 — Premortem. "It's two windows from now and the downside scenario happened. Walk back." The premortem forces concrete failure paths into the model; without it the downside is just a 20 % discount on base. See mental-models.md § 29.
• Mental model 26 — Optionality. Optionality = preserved future choices. Read each scenario by what choices it preserves vs forecloses. Bounded downside + preserved optionality > unbounded upside with foreclosed optionality. See mental-models.md § 26.
• Context-spine — org-stage + fiscal-period + product. Read org-stage for which scenarios matter (pre-revenue → upside is traction speed; growth → downside is competitive pressure). Read fiscal-period for the modeling horizon. Read product for which revenue lines are real vs roadmap. See context-spine.

Procedure

Step 0: Inspect and pull the upstream frames

Read the three Wing-4 inputs:

1. unit-economics-frame.md from O1 — CAC, LTV, contribution margin per channel and blended; burn-multiple direction.
2. forecast-band.json from O2 — construction shape, commit / best-case / pipeline, confidence band, retro signature.
3. runway-frame.md from O3 — current cash, burn shape, band-target, fundraise verdict, cut-or-grow verdict.

If any frame is missing, STOP and route to the producing skill. Do not synthesize the upstream frame; that breaks the boundary.

Step 1: Define the three scenario shapes

1. Base = O2 commit + O3 status-quo burn + O1 channel mix unchanged. The "everything you've already said" scenario.
2. Upside = O2 best-case + O1 channel-mix shift to highest LTV/CAC + one named tailwind (segment proof / channel scale / pricing). Name the tailwind; un-named upside is wishlisting.
3. Downside = O2 commit-band lower bound + O1 channel mix shift to declining channel + one named headwind (segment churn / channel saturation / competitive entry). Name the headwind; un-named downside is "everything is 20 % worse".

Step 2: Construct three statements per scenario

For each of the three scenarios, write three statements — not three full models, three statements:

1. Revenue statement — top-line shape over the horizon. Cite which O2 inputs flex.
2. Margin statement — gross + contribution shape. Cite which O1 inputs flex (channel mix, pricing, COGS).
3. Cash statement — net burn → runway gap shape. Cite the O3 band-target it lands against.

Each scenario = nine numbers (three statements × three time slices: now / mid-horizon / end-horizon) plus a one-line "what made this scenario this scenario" explanation.

Step 3: Premortem each scenario

1. Base premortem — "if base under-delivers by 20 %, which input was the load-bearing assumption?" If the answer is one input, the base is fragile; demote that input and recompute.
2. Upside premortem — "if upside hits, did we have the operating capacity to absorb it?" Upside without operating-capacity reasoning is a fundraise pitch, not a plan.
3. Downside premortem — "if downside hits, what's the next decision and when?" Tag the decision points; un-tagged downsides have no operational meaning.

Step 4: Run sensitivity, not Monte Carlo

For each scenario, vary the single load-bearing input (named in Step 3) by ±20 %. Read whether the runway-gap verdict changes shape.

A scenario whose verdict is stable under ±20 % sensitivity on its load-bearing input is robust. A scenario whose verdict flips is brittle; demote it into a "what if" footnote, not a board-pack scenario.

Monte-Carlo is over-precise for this cognition; the question is "does the verdict survive the obvious sensitivity?" — not "what's the 95th percentile?".

Step 5: Read the optionality, not just the mean

For each scenario, write the option-preservation note:

• "This scenario preserves: ___ (named choices)."
• "This scenario forecloses: ___ (named choices)."

The decision shape is rarely "pick the highest mean". It's "pick the scenario whose foreclosures we can live with and whose preserved options match the strategy".

Step 6: Emit the scenario bundle

Produce scenario-bundle.md — the artifact strategist (T2 / P1 build-buy-partner) and finance-partner (T1) read for downstream decisions. Per docs/guidelines/wing4-handoff.md § Chain 1.

Related Skills

WHEN to use this

• Constructing base / upside / downside for a board pack, fundraise, or strategic bet.
• Comparing two strategic options by their optionality and downside shape.

WHEN NOT to use this

• Per-customer economics — route to unit-economics-modeling (O1).
• Forecast-call construction — route to forecasting (O2).
• Runway shape / fundraise timing — route to runway-cognition (O3).
• Insource-vs-outsource-vs-acquire decisions — route to build-buy-partner (P1); P1 consumes this skill's bundle.
• Whole-business intrinsic value with terminal value — route to dcf-modeling.

Wing-4 handoff: this skill reads unit-economics-frame.md (O1), forecast-band.json (O2), runway-frame.md (O3); emits scenario-bundle.md consumed by P1 and T1. Per docs/guidelines/wing4-handoff.md § Chain 1.

When the agent should load this

• "Model base / upside / downside for the board pack."
• "What does the downside look like if growth halves?"
• "Compare option A and option B by their downside shape."
• "Wie sehen unsere Szenarien aus?"

Output

1. scenario-bundle.md (Wing-4 handoff) — three scenarios × three statements × three time slices, plus tailwind / headwind names, sensitivity-stability flag, and optionality note per scenario.
2. load-bearing-inputs.md — one input named per scenario as the load-bearing assumption; sensitivity result on each.
3. optionality-map.md — preserved / foreclosed choices per scenario; decision-shape recommendation.
4. premortems.md — base / upside / downside premortems with named failure paths and tagged decision points.

Gotcha

• "Downside = base × 0.8" is the most common deception. Downside must have a named headwind; without it the scenario doesn't model anything.
• Upside without operating-capacity reasoning is a fundraise narrative, not a plan. Capacity (hiring lag, infrastructure, support load) is the inversion check.
• Monte-Carlo simulations on a forecast that has a typed confidence band are over-precise theatre. Sensitivity ±20 % on the load-bearing input is the honest test.
• Three-statement = three statements per scenario, not three full models. Nine numbers + one-line explanation = a readable bundle.

Do NOT

• Do NOT construct scenarios without the three upstream frames (O1 / O2 / O3) — that breaks Wing-4 cognition boundaries.
• Do NOT name a scenario without naming its tailwind / headwind / decision points — the labels are the cognition.
• Do NOT optimise on mean; optimise on optionality + downside shape.

Runnable example

Series-A SaaS, annual plan window.

• Step 0 — frames: O1 says blended LTV/CAC 3.1, burn-multiple 1.8 decel. O2 says hybrid forecast, commit $6.3M, best-case $8.1M, band ±14 %. O3 says runway gap −5mo, fundraise=raise.
• Step 1 — Base = $6.3M revenue, status-quo burn. Upside = $8.1M, tailwind = "enterprise segment proof point closes Q2". Downside = $5.4M, headwind = "competitor enters mid-market, churn spikes 2× in Q3".
• Step 2 — three statements × three slices each. Base lands at gap −5mo; upside closes to −1mo; downside opens to −9mo.
• Step 3 — base premortem: load-bearing = enterprise segment close rate. Upside premortem: capacity = need 2 AEs by Q1 or upside is shape-wrong. Downside premortem: decision point = if churn ≥ 1.5× by mid-Q3, cut non-headcount + tighten fundraise narrative.
• Step 4 — sensitivity on close-rate ±20 %: base flips brittle (gap shape changes); demote to "fragile-base" footnote, re-run with conservative close rate.
• Step 5 — upside preserves M&A optionality; downside forecloses pricing-experiment optionality. Recommendation: plan against re-run base, raise narrative on upside, hold downside as triggered playbook.