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Decision Tree

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Activate when: user says 'help me choose between two options with different risks', 'I need to map out what could happen if we go with X', 'we have a sequent... Skill: Decision Tree Owner: deciqai Summary: Activate when: user says 'help me choose between two options with different risks', 'I need to map out what could happen if we go with X', 'we have a sequent... Tags: latest:1.0.5 Version history: v1.0.5 | 2026-07-16T17:56:56.683Z | user Description tail link + agents machine-readable metadata line (deciqai.com/s/decision-tree.json) v1.0.4 | 2026-07-08T10:58:57.760Z | user

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About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 995 downloads reported by the source. Last updated 10/11/2026.

Avoid when

  • Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Install and run

Setup complexity: low.

clawhub skill install s17a4mqcnk515kvaca5ze55d0x88pfpx:decision-tree
  1. Setup complexity is classified as HIGH. You must provision dedicated cloud infrastructure or an isolated VM. Do not run this directly on your local workstation.
  2. Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-deciqai-decision-tree/snapshot"

Run-check

$0.02 USD

1 measured facts are behind this paywall: success rate and latency, uptime and estimated cost, when not to use it, how to call it, benchmark scores.

Agents pay $0.02 in USDC. A card payment is $0.50, the smallest a card allows.

Documentation

CLAWHUB

141,506 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: decision-tree
description: "Activate when: user says 'help me choose between two options with different risks', 'I need to map out what could happen if we go with X', 'we have a sequential decision — first we do A then depending on results we do B', 'what is the expected value of this investment given uncertain demand'. Do NOT activate when: the decision is a one-shot choice with no sequential stages (use simple EV instead); probabilities cannot be estimated even roughly and uncertainty is too deep to quantify. More: deciqai.com/c/decision-tree"
---

# Decision Tree

## Overview

A **decision tree** maps a multi-stage decision: decision nodes (squares) for choices you control, chance nodes (circles) for outcomes you don't, probabilities on every branch, payoffs at the leaves — then rollback right-to-left to get expected value at the root. First systematized by John F. Magee (HBR, 1964); formalized by Howard Raiffa (1968). Its biggest value: converting "I feel we should expand" into "what probability do you assign to high demand?" — making every assumption explicit and contestable.

Composes with `expected-value-and-kelly` (EV scaffold + bet sizing), `probabilistic-thinking` (calibration per node), `inversion` (rollback = working outcomes backward), `mece` (branches must be MECE so probabilities sum to 1.0).

## When to Use

- Decision has sequential stages (decide → learn → decide again)
- Outcomes uncertain; probabilities can be estimated (even roughly)
- Payoffs quantifiable (NPV, revenue, cost, lives saved)
- Multiple stakeholders need a shared visual model to align on assumptions
- Sizing a big irreversible bet under AI uncertainty — build vs. buy vs. wait on AI capex, a fab investment, or committing while AI adoption / AI valuations are unproven

**Not when:** one-shot choice with no stages; probabilities unestimable; payoffs purely qualitative; branch set too large (use scenario planning instead).

## Coaching Novices (Adaptive Front Door)

- **Engine mode:** user has a concrete multi-stage decision → run The Process directly.
- **Coach mode:** user is unfamiliar or has no concrete case → guide step by step.

In Coach mode, respond one step at a time. Each [WAIT] is a hard stop — output only that step's question, then stop.

1. **One-line:** a decision tree converts "I feel" into "what probability do you assign?" — making assumptions explicit so they can be argued about.
2. **Check fit.** Sequential stages? Uncertain outcomes? Quantifiable payoffs? If yes to all three, a tree applies.
3. **Elicit their real case.** What's the initial choice? What uncertain outcomes follow? What payoffs result?
> **[WAIT — do not advance until user responds]**
4. **Run The Process one step at a time** with their input — draw structure, assign probabilities, assign payoffs, roll back.
> **[WAIT — do not advance until user responds]**
5. **Close** by naming the insight: the threshold at which the recommendation flips, and whether to gather more data.

_meta.json

{
  "ownerId": "kn754b8sk22s8c6gjxt02bftbn88q7ye",
  "slug": "decision-tree",
  "version": "1.0.5",
  "publishedAt": 1784224616683
}

references/sources.md

# Sources — decision-tree

> *Primary sources for the [decision-tree](../SKILL.md) skill.*

## Sources

- Magee, J. F. (1964). "Decision Trees for Decision Making." *Harvard Business Review*, July–August, 126–138. The original practitioner framework.
- Magee, J. F. (1964). "How to Use Decision Trees in Capital Investment." *Harvard Business Review*, September–October, 79–96. Capital investment application.
- Raiffa, H. (1968). *Decision Analysis: Introductory Lectures on Choices under Uncertainty.* Addison-Wesley. The academic formalization; introduces EVPI and formal rollback.
- Howard, R. A. (1966). "Decision Analysis: Applied Decision Theory." *Proceedings of the 4th International Conference on Operational Research.* Stanford. Early theoretical grounding.
- Howard, R. A., & Matheson, J. E. (Eds.) (1983). *Readings on the Principles and Applications of Decision Analysis.* Strategic Decisions Group. Comprehensive practitioner reference.
- Clemen, R. T., & Reilly, T. (2013). *Making Hard Decisions with DecisionTools.* Cengage. Standard textbook treatment.
- Eisenhower, D. D. (1948). *Crusade in Europe.* Doubleday. First-hand account of the D-Day weather go/no-go decision (worked example).
- Stagg, J. M. (1971). *Forecast for Overlord.* Ian Allan. The SHAEF chief meteorologist's account of the June 1944 forecasts.
- Major U.S. hyperscaler quarterly earnings guidance and calls, 2024–2026 (Microsoft, Alphabet, Amazon, Meta). Primary source for the reported surge in AI-related capital expenditure and management's commentary on committing to AI capacity under uncertain returns. Context for the AI build-vs-buy-vs-wait worked example.
- Mainstream financial-press coverage of the 2024–2026 "AI capex / overbuild / bubble" debate (e.g., The Wall Street Journal, Financial Times, The Economist, Reuters). Durable, widely-reported context for how large firms weighed heavy AI infrastructure investment against the risk of stranded capacity.
- Leading semiconductor foundries' and integrated device makers' public capital-expenditure guidance and management commentary, 2024–2026 (as reported in earnings materials and mainstream financial press). Context for the chipmaker leading-edge-fab worked example: the multi-year, multi-billion-dollar nature of advanced-node fab commitments made ahead of confirmed AI-driven demand.
- Widely reported industry coverage of the semiconductor sector's historical boom-bust capacity cycles and of the 2024–2026 debate over whether AI-driven demand for leading-edge chips is durable or a potential overbuild (e.g., trade press such as SemiAnalysis-style analysis alongside general financial press). Durable, well-documented context for the high/low-demand probability framing; no specific figures in the worked example are drawn from any single filing.

examples/chipmaker-leading-edge-fab-investment-2024-2026.md

# Method in Action: A Chipmaker's Leading-Edge Fab Investment Under AI Uncertainty (2024–2026)

> *Example for the [decision-tree](../SKILL.md) skill.*

Between 2024 and 2026, one of the largest irreversible capital bets in the economy was a semiconductor manufacturer's decision to commit to leading-edge fabrication capacity. A single advanced logic fab is a multi-year, multi-billion-dollar commitment made *years* before the demand that would fill it is confirmed. The AI wave made this sharper on both sides: the *upside* case (AI accelerators and their supply chain filling every wafer) and the *downside* case (a capacity glut if AI demand or monetization disappoints, echoing prior semiconductor boom-bust cycles). This is a textbook decision-tree problem — sequential, uncertain, quantifiable — and the tree does exactly what Magee promised in 1964: it converts "AI demand is unstoppable, we must break ground now" into "what probability do you put on sustained high demand, and what does the tree say if you are wrong?"

This worked example applies the skill's six Process steps to a **stylized composite** of that decision. The framing (large fab now vs. phased/modular fab vs. defer, under high/low AI-driven demand) mirrors the real 2024–2026 debate; the probabilities and dollar payoffs below are **illustrative inputs chosen to demonstrate the method**, not figures from any company's filings.

---

**Step 1 — Root.** Define the decision. A chipmaker's board must choose how to commit leading-edge wafer capacity over a multi-year horizon. Draw the root square with three branches:

- **Large fab now** — commit to a full-scale advanced fab immediately. Highest fixed cost and longest lead time, best unit economics *if* demand shows up, most stranded capital if it does not.
- **Phased / modular fab** — build the shell and a first module now, with a pre-engineered *option to expand* tooling later once demand is clearer (directly the small-plant-with-expansion structure Magee introduced in 1964).
- **Defer** — hold the large commitment ~12 months, gather adoption and pricing signal, then decide. Deferral is not free: a fab's multi-year construction-and-qualification lead time means capacity decided late arrives late, potentially after the window of peak demand.

Decision-maker: the board / CFO. Timeline: leading-edge fab lead times run into years — which is precisely why "wait" carries a real delay cost.

**Step 2 — Chance nodes.** Each branch runs into an uncertainty the firm does not control. Draw a circle on each. The dominant chance node is **AI-driven wafer demand**, which must be MECE:

- *High, durable demand* — AI-accelerator and adjacent demand keeps leading-edge capacity near-full for years.
- *Low / disappointing demand* — AI adoption or monetization stalls, or competitors add capacity too; the new fab runs underutilized (the "AI capex glut / bubble" fear that ran through the 2024–2026 debate).

For **Phased / modular**, a second-stage decision node

examples/eisenhower-1944-d-day-weather-decision.md

# Method in Action: Eisenhower's D-Day Weather Decision (June 1944)

> *Example for the [decision-tree](../SKILL.md) skill.*

On June 4–5, 1944, General Dwight D. Eisenhower faced the highest-stakes go/no-go decision of the war: launch Operation Overlord into marginal weather, or postpone. The decision was never written up as a formal tree — the method postdates it — but the deliberation at Southwick House maps onto the decision-tree process almost node for node, which is why decision analysts have cited it ever since.

**The root decision node (square):** Go on June 6, or postpone. The branch set was tightly constrained: the invasion needed moonlight for the airborne drops and a low dawn tide to expose beach obstacles, so only a few days per month qualified. If June 6 was scrubbed, the next tidal window was around June 19 — roughly two weeks away.

**The chance nodes (circles):** Each option led straight into an uncertainty Eisenhower did not control.

- *Go, June 6* → chance node: does the brief break in the storm forecast by Group Captain James Stagg, SHAEF's chief meteorologist, actually hold? If yes: landings proceed under rough but workable conditions. If no: landing craft swamped, air support grounded, the assault force wrecked on the beaches.
- *Postpone to June 19* → chance node: unknown weather two weeks out (beyond any 1944 forecast horizon), compounded by near-certain costs — nearly 200,000 troops were already sealed aboard ships and briefed, and every day of delay increased the odds that German intelligence would fix the time and place of the invasion.

**Probabilities:** Stagg's team forecast a short interval of improved weather for June 6 amid a stormy period — a genuine probabilistic judgment made against disagreeing British and American forecasting teams. Illustrative reconstruction (not historical record): call the window-holds branch p ≈ 0.7, and the June 19 branch a coin flip at best with the secrecy-compromise cost accruing regardless. No participant wrote such numbers down; the structure of the argument, however, was exactly this.

**Payoffs:** The asymmetry drove the answer. *Go and the window holds*: the liberation of Western Europe begins. *Go and it fails*: a bloody repulse — but a repulse that a second attempt could survive. *Postpone*: certain, compounding losses (secrecy, morale, tide, the Soviet-front timetable) plus an unresolved weather gamble two weeks later. Postponement was not the "safe" branch; it was a different bet with worse certain costs.

**Value of information — the June 4 postponement:** Eisenhower's first move was a textbook EVPI play. On June 4 he scrubbed the original June 5 date and bought exactly one more forecast cycle — trading one day of the window for one more update from Stagg. When Stagg's June 5 briefing confirmed the break, the value of further waiting had collapsed: no additional forecast could resolve the remaining uncertainty before the tide window closed. The cost of more information no

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