$ARGUMENTS

references/output-formats-core.md

healthcare

education

supply-chain

custom

Pick a number, paste your own scenario, or type "guide me".

1. Situation + trigger: "What is happening, and what forced this to your attention now?"
2. Stakes + players: "Who is involved, what do they want, and what is at stake?"
3. Constraints + unknowns: "What limits your options, and what do you wish you knew?"

$ARGUMENTS

list

~/.claude/wargames/

**Scenario:**

**Tier:**

**Status:**

**Turns:**

• list active

  — filter to

  status: In Progress

  only

• list biz

  — filter by domain tag

• list complex

  — filter by tier

references/output-formats-core.md

resume [# | keyword], list [active | domain | tier]

• Exploratory, not predictive — RAND guardrail: all outputs are thought experiments, never forecasts. Label accordingly.
• Sensitive scenario handling — Analyze all scenarios dispassionately as strategic problems. Analytical distance is a feature.
• Depth calibration — Match analysis depth to complexity tier. Do not over-analyze trivial decisions or under-analyze consequential ones.
• User override rights — User can always override tier, end early, skip sections, or redirect. Proceed without resistance.
• Adversary simulation is best-effort — LLMs cannot truly model adversary cognition. Acknowledge this at the start of every Interactive Wargame.
• Force trade-offs — Never present costless options. If an option dominates, search harder for weaknesses.
• LLM bias awareness — Actively mitigate biases per

  references/cognitive-biases.md

  .

references/output-formats-core.md

• Why This Tier: 2-3 sentences explaining which dimensions drove the score
• What Would Change: 1-2 sentences describing what shift would change the tier

optimistic

realistic

adversarial

worst-case

Your scenario scores N/10 — tier X, mode Y, difficulty Z. Override tier or difficulty? [yes/no]

Quick-rank for THIS decision (e.g., "3 1 5 2 4 6") or "skip":
  1. {criterion_1}  2. {criterion_2}  3. {criterion_3}  4. {criterion_4}  5. {criterion_5}  6. {criterion_6}

criteria

• Quick Analysis: Annotate decision tree branches with alignment to top 2 criteria
• Structured Analysis: Use criteria as ranking dimensions in option analysis; criteria become quadrant chart axes
• Interactive Wargame: Annotate decision menu options with criteria alignment (High/Medium/Low per top criteria)

references/output-formats-core.md

1. Restate decision in the user's own terms. Confirm framing.
2. Key Assumptions Check — Surface 2-3 unstated assumptions baked into the scenario framing. Challenge each briefly.
3. Framework application — Select 2-3 frameworks from

   references/frameworks.md

   using the heuristic table. Apply each to the scenario. Show reasoning, not just labels.
4. Analysis — Present findings using a Unicode decision tree (see

   references/output-formats-core.md

   ). Map options to outcomes with probabilities where estimable.
5. Recommendation — State clearly with:
   • Confidence level: high, medium, or low
   • Key assumption that could change this recommendation
   • Watch signal: what to monitor that would trigger reconsideration
6. Bias sweep — Run the Single-Output Mode Sweep per

   references/cognitive-biases.md

   Bias Sweep Protocol. 6b. Proactive bias detection — Suggest relevant commands for overconfidence signals (max one per turn). See

   references/cognitive-biases.md

   Enhanced Debiasing.
7. Action Bridge — See

   references/output-formats-core.md

   Action Bridge template. Each move must reference a specific analysis output.
8. Monte Carlo option — If uncertainty warrants it, offer: "Want to explore N variations? Type

   explore [N]

   ." See

   references/wargame-engine.md

   Monte Carlo Iteration Protocol.
9. Save journal to

   ~/.claude/wargames/{date}-{slug}.md

1. Key Assumptions Check — Surface and challenge all major assumptions. For each assumption, state what changes if it is wrong.
2. Stakeholder mapping — Table format:

   Stakeholder	Interest	Power	Position

   Power: high, medium, low. Position: supportive, neutral, opposed.
3. Framework application — Select 3-5 frameworks from

   references/frameworks.md

   . Include ACH (Analysis of Competing Hypotheses) if the scenario involves competing explanations or theories.
4. Option analysis — For each viable option, present explicit trade-offs. Every option must have at least one significant downside. No free lunches.
5. Ranking with rationale — Rank options. If criteria were set, use them as the primary ranking dimensions. State how each option scored against each criterion. Use granular probability estimates (percentages, not "low/medium/high") per superforecasting methodology (see

   references/frameworks.md

   ).
6. Decision triggers — Define conditions that would change the recommendation. Be specific: thresholds, events, new information.
7. Pre-mortem — For each top-ranked option, imagine it has failed catastrophically. Identify the most likely cause of failure. State what early warning signs would precede that failure.
8. Quadrant chart — Generate a Mermaid quadrant chart plotting options on risk (x-axis) vs. reward (y-axis). Label each quadrant and place options with brief annotations. 8b. Proactive bias detection — Suggest relevant commands for overconfidence signals (max one per turn). See

   references/cognitive-biases.md

   Enhanced Debiasing.
9. Action Bridge — See

   references/output-formats-core.md

   Action Bridge template.
10. Monte Carlo option — Offer: "Want to explore N variations? Type

    explore [N]

    ." See

    references/wargame-engine.md

    Monte Carlo Iteration Protocol.
11. Save journal to

    ~/.claude/wargames/{date}-{slug}.md

1. Define actors — Create 2-8 actors using structured persona templates from

   references/wargame-engine.md

   . Each actor has: name, role, goals, resources, constraints, personality archetype (hawk, dove, pragmatist, ideologue, bureaucrat, opportunist, disruptor, or custom).
2. User role selection — User selects which actor they control. If none fit, create a custom actor for them.
3. Initial conditions — Define the starting state: resources, positions, alliances, constraints, information each actor has access to.
4. Pre-seed injects — Create 3-5 injects (unexpected events). At least one must be a positive opportunity, not just a crisis. Injects remain hidden from the user until deployed.
5. Set turn count — Default: Complex 3-5 turns, Chaotic 3-8 turns. User may request 2-12 turns. Above 8 turns, warn: "Extended games may hit context limits — consider

   export

   +

   resume

   at turn 8." Confirm actor list and turn count with user before proceeding.
6. Present setup summary — Show all actors, initial conditions, and turn count. Confirm with user before proceeding.

1. Timeline — Key decisions and their outcomes in chronological order.
2. What worked and what failed — With evidence from turn records.
3. Biases detected — Both human decision biases and LLM simulation biases observed during play. Name each bias explicitly.
4. Transferable insights — Decision principles extracted from this scenario that apply to the user's real context.
5. Paths not taken — Briefly explore 2-3 alternative decision paths and their likely consequences. For each, identify the turn where the divergence would have occurred and the likely cascade.
6. Actor performance — Evaluate each AI-controlled actor: did they behave consistently with their archetype and goals? Flag any actors that drifted from their persona (LLM consistency check).
7. Visualizations — Generate Mermaid timeline (campaign phases) and decision tree (key branch points) in the journal showing the full arc of the wargame.
8. Final journal save — Write the complete AAR to the journal file.
9. Action Bridge — See

   references/output-formats-core.md

   Action Bridge template. The Probe should target the most uncertain insight from the AAR.

• Path:

  ~/.claude/wargames/

• Create on first use with

  mkdir -p

• Archive path:

  ~/.claude/wargames/archive/

{YYYY-MM-DD}-{domain}-{slug}.md

• {domain}

  : predefined:

  biz

  ,

  career

  ,

  crisis

  ,

  geo

  ,

  personal

  ,

  startup

  ,

  negotiation

  ,

  tech

  . Auto-detected domains use

  custom

  as the slug.

• {slug}

  : 3-5 word semantic summary (e.g.,

  supplier-acquisition-crisis

  )
• Collision handling: append

  -v2

  ,

  -v3

  , etc.

• Quick Analysis / Structured Analysis: Save once at end with

  status: Complete

• Interactive Wargame: Save after EVERY turn with

  status: In Progress

  . After AAR, update to

  status: Complete

1. Before writing: validate target file exists and frontmatter is parseable
2. After writing: verify write completed
3. On resume: if frontmatter missing or malformed, attempt v1 header parsing. If that fails, inform user: "Journal appears corrupted. Start a new analysis of the same scenario?"

1. Label ALL outputs as exploratory, not predictive (RAND guardrail)
2. Always allow the user to override the classification tier
3. Never skip AAR in Interactive Wargame mode
4. Force trade-offs — every option must have explicit downsides
5. Name biases explicitly when detected — both human and LLM
6. Default maximum 8 turns per wargame; user may override up to 12 with context warning
7. Save journal after every turn in Interactive Wargame mode
8. Criteria and Action Bridge are mandatory — when criteria are set they must visibly influence rankings; every recommendation, ranking, or AAR must end with Probe/Position/Commit

references/session-commands.md