best-in-world-strategy

name	best-in-world-strategy
description	Excellence-first strategic decision support. This skill should be used when users need to choose between options, pressure-test a plan, evaluate risk, or make a specific decision — across security, product, growth, operations, org design, and finance. Use when there is a decision with tradeoffs to score and a recommendation to make. For pure research ("what does the best in the world do about X?"), use best-in-world-research instead.
version	1.1.0
last_updated	"2026-03-22T00:00:00.000Z"

Best-In-World Strategy

Set an excellence baseline first. Adapt intentionally second.

Modes

Use Speed mode by default. Reserve Full mode for explicitly high-stakes or complex decisions.

• Speed (default): frame the decision → world-class baseline → recommendation → downside controls. Rigorous but concise. No options scoring, no red-team unless the stakes clearly warrant it.
• Full (when user asks for comprehensive analysis, or the decision is clearly high-stakes / irreversible): complete 9-step workflow with constraint ledger, options scoring, red-team, and learning loop.
• Evidence-Backed modifier (optional): add external citations to either mode when user explicitly asks.

Switch to Risk-Acceptance Override when user states willingness to accept risk and move quickly — drops options scoring, keeps assumptions + failure modes + downside controls.

Workflow

1. Frame the decision.

• Write one decision sentence.
• Capture decision type: architecture, security/risk, product, go-to-market, operations, org/talent, capital/finance.
• Capture objective, owner, time horizon, and success metric.

2. Build a constraint ledger.

• Separate hard constraints from soft constraints.
• Record budget, timeline, legal, staffing, and political constraints.

3. Set the best-in-world baseline.

• Identify 2-4 expert archetypes relevant to the decision.
• For each archetype, output:
  • principle
  • expected benefit
  • cost/complexity
  • confidence
• Mark each item as non-negotiable or adaptable.

4. Apply first principles.

• Separate facts, assumptions, and conventions.
• State must-be-true conditions.
• Isolate the smallest causal drivers.

5. Run red-team pressure testing.

• Ask hard skeptical questions.
• Surface first-order and second-order failure modes.
• Define one disconfirming test.

6. Generate options.

• Option A: world-class ideal.
• Option B: pragmatic high-confidence.
• Option C: minimum viable risk-controlled.

7. Score options.

• Score each option 1-5 on:
  • impact
  • risk
  • cost
  • speed
  • reversibility
  • learning value
• Use default weights unless user provides custom weights:
  • impact 30%
  • risk 20%
  • cost 15%
  • speed 15%
  • reversibility 10%
  • learning value 10%

8. Recommend and sequence.

• Recommend one option and explain the tradeoffs.
• Sequence as now, next, later.
• Mark major decisions as two-way door or one-way door.
• Define downside controls:
  • max acceptable loss
  • kill criteria
  • revisit trigger/date
• Include 2-3 no-regret moves.

9. Define learning loop.

• Define what to measure.
• Define what would change the current strategy.

Risk-Acceptance Override

When user accepts risk explicitly:

1. Keep rigor floor.
2. Switch to speed output.
3. Include this line exactly: Decision owner accepts risk and prefers speed over additional validation at this stage.
4. Keep mandatory safeguards:

• assumptions list
• confidence tags (high, medium, low)
• top failure modes
• one disconfirming test
• max acceptable loss
• kill criteria
• revisit trigger/date

Output Contract (Speed — default)

## Decision
[One sentence]

## What the Best in the World Would Do
[2–3 bullets. Named practitioners or archetypes. Specific practices, not principles.]

## Recommendation
[One option. Why this one. What's intentionally deferred.]

## Key Assumptions
[2–3 items with confidence tags: high / medium / low]

## Downside Controls
- Max acceptable loss:
- Kill criteria:
- Revisit trigger/date:

Output Contract (Full)

## Decision

## Mode
- Selected mode:
- Risk-Acceptance Override:

## Decision Type

## Objective and Success Metric
- Objective:
- Success metric:
- Time horizon:
- Owner:

## Constraint Ledger
- Hard constraints:
- Soft constraints:

## What the Best in the World Would Say
- Expert archetype 1:
  - Principle:
  - Expected benefit:
  - Cost/complexity:
  - Confidence:
- Expert archetype 2:
  - Principle:
  - Expected benefit:
  - Cost/complexity:
  - Confidence:
- Non-negotiables:
- Adaptable elements:

## First-Principles Breakdown
- Facts:
- Assumptions:
- Conventions:
- Must-be-true conditions:
- Causal drivers:

## Red-Team Questions and Failure Modes
- Hard questions:
- First-order failures:
- Second-order failures:
- Disconfirming test:

## Strategic Options
- Option A (world-class ideal):
- Option B (pragmatic high-confidence):
- Option C (minimum viable risk-controlled):

## Option Scores (1-5)
- Weights used:
- Option A:
- Option B:
- Option C:

## Recommendation and Tradeoffs
- Recommended option:
- Why:
- What is intentionally deferred:

## Sequenced Plan
- Now:
- Next:
- Later:
- Two-way door decisions:
- One-way door decisions:

## Downside Controls
- Max acceptable loss:
- Kill criteria:
- Revisit trigger/date:

## No-Regret Moves
- Move 1:
- Move 2:
- Move 3:

## Learning Loop
- What to measure:
- What would change this strategy:

Output Contract (Speed Mode)

## Decision

## Why This Option

## Key Assumptions (with confidence tags)

## Top Risks and Failure Modes

## Next 3 Moves (now)

## Downside Controls
- Max acceptable loss:
- Kill criteria:
- Revisit trigger/date:

## Disconfirming Test

Quality Bar

• Avoid generic advice.
• Avoid authority theater; output principles, not name-dropping.
• Make tradeoffs explicit.
• Mark confidence for major claims.
• Prefer falsifiable statements over slogans.

Reference

Load references/question-bank.md when deeper adversarial questioning is needed.