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Best In World Strategy

Community
stevembarclay
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.

Overview

Publisherstevembarclay
Repositorypencilplaybook
Skill namebest-in-world-strategy
Stars
52
Forks
3
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by stevembarclay on GitHub. Read the source before you install it.

Installation

Install the Best In World Strategy AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/stevembarclay/pencilplaybook/tree/main/.claude/skills/best-in-world-strategy
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/stevembarclay/pencilplaybook.git /tmp/pencilplaybook
mkdir -p .claude/skills
cp -r /tmp/pencilplaybook/.claude/skills/best-in-world-strategy .claude/skills/best-in-world-strategy
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Best In World Strategy in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Best In World Strategy on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Best In World Strategy is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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)

markdown
## 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)

markdown
## 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)

markdown
## 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.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Best In World Strategy AI skill do?

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.

Why use Best In World Strategy on TypingMind?

Because you install it once and use it with any model. Best In World Strategy is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Best In World Strategy in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/stevembarclay/pencilplaybook/tree/main/.claude/skills/best-in-world-strategy. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Best In World Strategy?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Best In World Strategy?

As many as you like. As long as a model supports skills, you can use Best In World Strategy with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Best In World Strategy AI skill free?

Yes. It is published on GitHub by stevembarclay under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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