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best-in-world-strategy

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.

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Files included (2)

  • SKILL.md6.0 KB
  • references/question-bank.md2.5 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

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.
  1. Build a constraint ledger.
  • Separate hard constraints from soft constraints.
  • Record budget, timeline, legal, staffing, and political constraints.
  1. 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.
  1. Apply first principles.
  • Separate facts, assumptions, and conventions.
  • State must-be-true conditions.
  • Isolate the smallest causal drivers.
  1. Run red-team pressure testing.
  • Ask hard skeptical questions.
  • Surface first-order and second-order failure modes.
  • Define one disconfirming test.
  1. Generate options.
  • Option A: world-class ideal.
  • Option B: pragmatic high-confidence.
  • Option C: minimum viable risk-controlled.
  1. 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%
  1. 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.
  1. 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.

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