Make AI think like a world-class CEO chief of staff: structure decisions, detect bias, predict competition, manage stakeholders.
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AI gives advice easily. But decomposing trade-offs, detecting cognitive bias, predicting competitive responses, and navigating stakeholder dynamics — that's the decision quality CEOs actually need.
If you face these problems regularly, this is built for you:
| Scenario | Typical Questions |
|---|---|
| 🎯 Strategic Decisions | Enter a new market? Build or acquire? Keep investing or cut losses? |
| 💰 Resource Allocation | 5 projects, budget for 2 — which ones? Growth vs product vs org? |
| 🔥 Crisis Response | CTO leaving for a competitor — what do you do in 2 hours? |
| 🤝 Stakeholder Management | How to align board, investors, and leadership? Manage reorg politics? |
| ⚔️ Competitive War Gaming | If we cut prices / launch a feature, how will competitors react? |
Most "CEO advisor" AI prompts share the same weaknesses:
User: "Should we enter the Japan market?"
Generic AI:
"Entering the Japan market is an important strategic decision. You should
consider market size, competitive landscape, localization costs, and
regulatory requirements. I recommend conducting market research, assessing
risks, and developing a detailed plan. Would you like me to help you
identify some key questions to consider?"
❌ Sounds polished but stays vague
❌ Lists frameworks but doesn't choose the right one
❌ Gives advice without surfacing trade-offs
❌ Doesn't check cognitive bias (are you chasing FOMO because a competitor entered?)
❌ Doesn't predict competitive response (how will local players fight back?)
❌ Doesn't map stakeholders (who on your board supports vs opposes this?)
User: "Should we enter the Japan market?"
CEOskill:
【Decision Classification】Type 1 (One-Way Door) — High entry cost, high exit cost
【Bias Check】
- Are you experiencing FOMO because a competitor just entered?
- Are you overestimating first-mover advantage? (Japan rewards localization depth)
- Are you underestimating regulatory/cultural barriers?
【4 Options + Trade-offs】
A. Wholly-owned subsidiary ($5M, 18-month payback, risk: localization failure)
B. JV / acquire local player ($8M, 6-month launch, risk: integration conflict)
C. Lightweight pilot ($500K, validate PMF first, risk: miss the window)
D. Don't enter (save $5M+, focus current markets, risk: competitor builds moat)
【Competitive War Gaming】
- How will the top 3 local players respond? (Price war / channel lockout / regulatory lobbying)
- What does your competitor's performance look like 6 months after entry? (User growth vs burn rate)
【Stakeholder Map】
- Board: 2 support (bullish on international), 1 opposes (fears resource dilution)
- Investors: care about 18-month ARR growth, geography-agnostic
- Leadership: VP Sales supports, CFO neutral-leaning-against
【Recommendation】
Option C (pilot) + 6-month decision gate.
Rationale: Japan PMF uncertainty is high; going all-in is premature.
Next Steps:
1. Find 1-2 local design partners (2 weeks)
2. Run MVP to validate willingness to pay (3 months)
3. Data-driven decision on A/B/D (month 6)
✅ Structured decision workflow
✅ At least 3 comparable options + do nothing
✅ Explicit trade-offs
✅ Cognitive bias check
✅ Competitive + stakeholder response prediction
✅ Next steps + decision gate
| Capability | Generic AI | Consultant Prompt | CEOskill |
|---|---|---|---|
| Decision classification (one-way / two-way / crisis) | ❌ | ✅ Enforced | |
| At least 3 options + do nothing | ❌ | ✅ Enforced | |
| Explicit trade-offs (not just conclusions) | ❌ | ✅ | |
| Cognitive debiasing (FOMO / sunk cost / anchoring) | ❌ | ❌ | ✅ |
| Competitive response prediction (war gaming) | ❌ | ✅ | |
| Stakeholder mapping (board / investors / leadership) | ❌ | ✅ | |
| Quantitative analysis (Monte Carlo / ICE / NPV) | ❌ | ❌ | ✅ |
| Crisis / Prioritization / War Game modes | ❌ | ❌ | ✅ |
Not "giving advice" — decomposing decisions:
- Decision classification: One-Way Door / Two-Way Door / Crisis / Strategic Bet
- Framework selection: Automatically picks the best framework for the scenario
- Option generation: Forces at least 3 comparable options + do nothing
- Trade-off analysis: Explicit cost, risk, and resource requirements for each option
The biggest enemy of CEO decisions is not lack of information — it's cognitive bias:
- Anchoring: Are you anchored to a number set by a competitor / investor / advisor?
- Sunk cost: Are you continuing because "we already spent $X on this"?
- Confirmation bias: Are you only looking for data that supports your preferred option?
- FOMO: Are you doing this because "everyone else is"?
- Availability bias: Are you overweighting a recent vivid event?
See: references/cognitive-debiasing.md
Don't just analyze yourself — predict the opponent:
- If we cut prices, will competitors match or differentiate?
- If we enter their market, will they counterattack or play defense?
- If we acquire this company, how will competitors respond?
Executive decisions aren't just about "right vs wrong" — they're about "who supports vs who opposes":
- Board: Who is your ally? Who opposes? Who is the swing vote?
- Investors: What metrics do they care about? What's their tolerance?
- Leadership team: Who benefits / loses from this decision?
- Key employees: Who might leave because of this?
See: references/stakeholder-playbook.md
Not just qualitative — quantitative support built in:
- Monte Carlo simulation: Probability distributions under uncertainty
- ICE scoring: Impact / Confidence / Ease for quick prioritization
- NPV / IRR: Investment return analysis
- Expected Value: Probability-weighted outcome calculation
- Scenario Planning: Best / base / worst case analysis
See: scripts/analysis_tools.py
- Enter a new market? Acquire or build? Cut losses or keep investing?
- Example: M&A Acquisition Decision
- Multiple projects competing for resources. Which first? How to split budget?
- Example: 5-Initiative Prioritization
- Key executive departure, production outage, PR crisis — what to do first?
- Example: CTO Departure Crisis
- If we do X, how will competitors respond?
More examples: examples/
Treat it as your CEO chief of staff:
"Should we enter the Japan market?"
"5 projects, budget for 2 — how to prioritize?"
"CTO is leaving for a competitor. What do I do in the next 2 hours?"
"If we cut prices 20%, how will competitors respond?"
Not generic advice, but:
- Decision classification — What type of decision is this? Why does it matter?
- Bias check — What are your blind spots?
- Option comparison — At least 3 options + do nothing, with trade-offs
- Competitive / stakeholder analysis — How will others react?
- Recommendation + Next Steps — What to do, and what comes next?
Load SKILL.md directly:
# OpenClaw
openclaw skill install https://github.com/AIPMAndy/CEOskill
# Claude Code / Cursor
Add SKILL.md to your project contextSee: RUNTIME.md
Core principle:
- Text-only runtimes can run the basic workflow
- Web search makes strategic output stronger
- Python / code execution unlocks quantitative analysis
.
├── SKILL.md # Main skill definition (core)
├── references/
│ ├── frameworks.md # Decision framework library
│ ├── cognitive-debiasing.md # Cognitive bias detection
│ ├── stakeholder-playbook.md # Stakeholder management
│ └── war-gaming.md # Competitive simulation
├── scripts/analysis_tools.py # Quantitative analysis tools
├── examples/ # Real-world cases
│ ├── mna-acquisition-decision.md
│ ├── cto-departure-crisis.md
│ └── prioritization-five-initiatives.md
└── evals/evals.json # Evaluation cases
- Main skill definition
- Decision framework references
- Cognitive debiasing
- Stakeholder playbook
- War gaming
- Analysis tools
- Add 10+ real-world cases (M&A / layoffs / fundraising / reorg)
- Add benchmark / eval results
- Add video demos
- Add more Agent Runtime adapters
Contributions welcome:
- Real decision cases (anonymized)
- New decision frameworks
- Stronger eval cases
- Agent Runtime adapters
Please read CONTRIBUTING.md first.
MIT License.
- Give the repo a ⭐ Star
- Submit a real decision case or PR
- Share with someone who needs it
Turn AI decision support from a personal tool into a reusable decision toolkit.
Made with 🧠 by Andy