Wargame

Domain-agnostic strategic decision analysis. Every output labeled exploratory.

Dispatch

Dispatch guard: If args match an in-session command name (e.g., red team, sensitivity) but no session is active, treat as scenario text or ask for clarification: "Did you mean to start a new scenario about '{input}', or resume an existing session?"

Scenario Gallery (empty args)

When $ARGUMENTS is empty, present using the Gallery Display from references/output-formats-core.md:

Domain tags are extensible. The predefined set covers common scenarios, but the LLM may auto-detect a more specific domain from user input and assign a custom tag (e.g., healthcare, education, supply-chain). Custom tags use the custom slug in filenames and the specific tag in journal frontmatter.

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

Guided Intake

If the user types "guide me", ask three questions sequentially:

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?"

After all three answers, synthesize into a scenario description and proceed to Scenario Classification.

Intelligent Intake (vague inputs)

If the user's input is vague or general (fewer than 10 words AND lacks a specific event or action verb, OR is a general topic/domain without an embedded decision), run the Intelligent Intake Protocol:

Phase 1: Contextual Research — Use WebSearch and WebFetch to gather context (max 2-3 searches). Present a brief research summary using the Context Research Display from references/output-formats-core.md.

Phase 2: Narrowing Interview — Ask 3-5 targeted questions informed by the research:

1. •Anchor: "What specifically prompted you to think about this now?"
2. •Decision: "What is the actual choice you're facing?" (offer concrete options from research)
3. •Stakes: "What happens if you get this wrong? Who else is affected?"
4. •Constraints (if needed): "What options are off the table?"
5. •Timeline (if needed): "When do you need to act by?"

Skip questions the user already answered. Questions adapt to the domain.

Phase 3: Alignment Confirmation — Synthesize research + interview into a concrete scenario using the Scenario Understanding Display from references/output-formats-core.md. If the user confirms, proceed to Classification. If "adjust", let user modify. If "start over", return to gallery.

If web search is unavailable, skip Phase 1 and proceed directly to Phase 2. The "guide me" option from the gallery remains for users who explicitly want the generic intake without research.

When uncertain whether input is vague, default to asking one clarifying question rather than classifying prematurely.

Journal Resume

resume (no args): Read ~/.claude/wargames/, find journals with status: In Progress in YAML frontmatter (or **Status:** In Progress for v1 journals). If exactly one, auto-resume. If multiple, show numbered list.

resume N (number): Resume the Nth journal from list output. Sort is reverse chronological (newest first) — this ordering is canonical for both list and resume N.

resume keyword (text): Search journal YAML frontmatter (scenario, tags fields) for case-insensitive substring match. If exactly one match, auto-resume. If multiple, show filtered list.

Resume flow: Read YAML frontmatter (metadata) + last <!-- STATE ... --> block (game state) for fast resume. Fall back to full-journal reconstruction if no state snapshot found.

Journal List

If $ARGUMENTS starts with list: read ~/.claude/wargames/, extract metadata from YAML frontmatter. For v1 journals without frontmatter, fall back to parsing **Scenario:**, **Tier:**, **Status:**, **Turns:** lines.

Filters (optional, AND-combined):

• •list active — filter to status: In Progress only
• •list biz — filter by domain tag
• •list complex — filter by tier

Present using the list display from references/output-formats-core.md. Sort reverse chronological (newest first).

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

Journal Lifecycle

archive: Move journals older than 90 days from ~/.claude/wargames/ to ~/.claude/wargames/archive/. Only runs when journal count > 20.

delete N: Delete journal N from list. Confirm before deleting: "Delete '{scenario}'? [yes/no]"

Abandon protocol: If the user types end or abandon during an active wargame before the AAR, update journal status to Abandoned and save. Abandoned journals appear in list but are excluded from resume (no arg) auto-detection.

Otherwise, proceed to Scenario Classification with the provided text.

Wargame Principles

Core principles governing all modes. Violations are bugs.

Exploratory, not predictive — RAND guardrail: all outputs are thought experiments, never forecasts. Label every output accordingly. Do not imply certainty where none exists.

Sensitive scenario handling — Treat all scenarios with analytical rigor regardless of domain. Real-world violence, harm, and crisis scenarios are analyzed dispassionately as strategic problems. Analytical distance is a feature, not a defect.

Depth calibration — Match analysis depth to scenario complexity. The classification rubric determines this. Do not over-analyze trivial decisions or under-analyze consequential ones.

User override rights — The user can always override the classification tier, end early, skip sections, or redirect analysis. Acknowledge overrides and proceed without resistance.

Adversary simulation is best-effort — LLMs cannot truly model adversary cognition or maintain hidden information. Acknowledge this limitation explicitly at the start of every Interactive Wargame.

Force trade-offs — Never present costless options. Every choice has downsides. If an option appears to dominate, search harder for its weaknesses.

LLM bias awareness — Actively mitigate known LLM biases. See references/cognitive-biases.md for bias catalog and Bias Sweep Protocol.

Context Management

Multi-turn wargames consume significant context. These rules prevent overflow.

Lazy Loading

Reference files are loaded on demand per the "Read When" column in the Reference File Index — NOT at session start. When a reference file is needed, read only the relevant section if possible (e.g., read only the selected framework's procedure, not all 13 frameworks).

State Compression

After Turn 3, compress earlier turns in the journal state to 2-3 line summaries:

Turn 1: [Decision] → [Outcome]. Key state change: [what shifted].
Turn 2: [Decision] → [Outcome]. Key state change: [what shifted].

Full turn details remain in the saved journal file. Only the compressed summaries are kept in working context.

Context Budget Protocol

At each turn boundary, assess remaining context capacity:

Monte Carlo Budget

Monte Carlo iterations capped at N ≤ 25. See references/wargame-engine.md Monte Carlo Iteration Protocol for cap behavior and diversity requirements.

Output Verbosity

Controls output density per turn. Set during setup or changed mid-game with verbose [level].

Default: standard. Maps to the existing Constraint Priority Tiers in references/wargame-engine.md.

During setup, present: "Output verbosity? [brief / standard / detailed]" — user can skip (defaults to standard).

Scenario Classification

Score the scenario on five dimensions. Show all scores to the user.

Scoring Rubric

Tier Assignment

Score each dimension independently. Present a filled-in rubric table with the user's scenario mapped to each row. Include a Reasoning column explaining each score in one line (see references/output-formats-core.md Classification Display).

After scoring, present:

• •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

Present difficulty level (auto-mapped from tier):

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

If the user overrides, acknowledge and switch without argument. If the user provides additional context that changes scores, rescore and re-announce before proceeding. Proceed to Decision Criteria Elicitation.

Decision Criteria Elicitation

After classification, before entering any analysis mode. All modes.

Present 4-8 criteria relevant to THIS scenario's domain, scaled to complexity: Clear/Complicated: 4-5 criteria, Complex/Chaotic: 6-8 criteria. May include standard criteria (Speed, Cost, Risk, Relationships, Reversibility, Learning) or domain-specific ones the LLM proposes based on the scenario context.

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}

If the user provides a ranking, record it as ranked criteria. If the user skips, proceed without criteria weighting. The user can re-rank anytime with the criteria command.

Swing weighting (Complex/Chaotic only): For Complex or Chaotic tier scenarios, offer swing weighting after the quick-rank: "Your scenario has high complexity — would you like detailed swing weighting for more precise criteria weights? [quick-rank / swing]". Swing weighting procedure: (1) Set all criteria to their worst plausible level. (2) Ask: "Which criterion, improved from worst to best, would make the biggest difference?" — that criterion gets the highest weight. (3) Repeat for remaining criteria. (4) Normalize weights to sum to 1.0. Quick-rank remains the default for Clear/Complicated tiers.

Criteria propagation by mode:

• •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)

Criteria appear in the Decision Criteria Lens display (see references/output-formats-core.md).

Mode A: Quick Analysis

Clear tier (score 0-3). Single output, minimal ceremony.

Steps

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

Keep the total output concise. This mode exists for decisions that do not warrant deep analysis. Resist scope creep. If the analysis reveals the scenario is more complex than initially scored, note this and offer to re-classify upward.

Mode B: Structured Analysis

Complicated tier (score 4-6). Single output, thorough examination.

Steps

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

Mode C: Interactive Wargame

Complex/Chaotic tier (score 7-10). Multi-turn interactive protocol.

Setup Phase

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.

State the adversary simulation limitation explicitly during setup: "AI- controlled actors optimize for their stated goals, but this is best-effort simulation, not genuine adversarial cognition."

Ensure actor goals genuinely conflict. If all actors want the same thing, the wargame degenerates into a coordination exercise. Introduce at least one structural tension between actor objectives.

Turn Loop

Execute turns per references/wargame-engine.md Turn Structure (13 steps). The engine handles choice architecture, belief updating, signal classification, consider-the-opposite, and in-session command dispatch.

Use display templates from references/output-formats-core.md: Turn Header Display for status bar, Intelligence Brief Display for situation, Actor Card Display for each actor, Decision Card Display for options, Inject Alert Display for injects. Target 40-80 lines per turn.

Proactive bias detection: Suggest relevant commands for overconfidence signals (max one per turn). See references/cognitive-biases.md Enhanced Debiasing.

Inject Deployment

Fire pre-seeded injects per references/wargame-engine.md Inject Design. Injects must create dilemmas forcing trade-offs between competing objectives.

End Conditions

The wargame ends when: max turns reached, user explicitly ends early, or a decisive outcome renders continued play moot. If the user says "end", "stop", "done", or "AAR", proceed to AAR immediately. Proceed to AAR regardless of end condition — never end without it.

Mandatory AAR (After Action Review)

Never skip the AAR. This is where learning happens.

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.

State Management

Journal Directory

• •Path: ~/.claude/wargames/
• •Create on first use with mkdir -p
• •Archive path: ~/.claude/wargames/archive/

Journal Format

Journals use YAML frontmatter for machine-parseable metadata:

---
scenario: "{title}"
tier: {Clear | Complicated | Complex | Chaotic}
mode: {Quick Analysis | Structured Analysis | Interactive Wargame}
difficulty: {optimistic | realistic | adversarial | worst-case}
status: {In Progress | Complete | Abandoned}
created: {YYYY-MM-DDTHH:MM:SS}
updated: {YYYY-MM-DDTHH:MM:SS}
turns: {completed}/{total}
criteria: [{ranked criteria list}]
actors: [{actor names}]
tags: [{domain tags}]
---

Migration: If list/resume encounters a journal without --- frontmatter, fall back to v1 markdown header parsing. New journals always use frontmatter.

Filename Convention

Pattern: {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.

Save Protocol

• •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

State Snapshot

Append a YAML state snapshot as an HTML comment at the end of the journal after each turn save:

<!-- STATE
turn_number: {current_turn}
difficulty: {difficulty_level}
verbosity: {brief | standard | detailed}
criteria: [{ranked criteria list}]
branches: [{branch_id, fork_turn, current_turn}]
actors:
  - name: {actor}
    resources: {resource_map}
    stance: {stance}
    beliefs: {what this actor believes about the situation}
    information_state: {knows | thinks-they-know | doesn't-know}
    relationships: {relationship status with other actors}
    risk_posture: {current risk tolerance}
    attention_style: {reactive | adaptive | agile}
active_injects: [{inject_ids}]
inject_history: [{previously deployed inject_ids}]
-->

Resume flow: Read frontmatter + last <!-- STATE ... --> block for fast resume. Fall back to full-journal reconstruction if no snapshot found.

Rewind Protocol

When the user types rewind [N] (default N=1):

1. •Load state snapshot from N turns back by finding the corresponding <!-- STATE ... --> block in the journal
2. •Fork the journal with a branch marker: ## Branch: Turn {T} Alternative header inserted after the original turn's content
3. •Restore the loaded state as the active game state and continue play from that point
4. •Preserve the original path — all content above the branch point remains intact in the journal

If the requested turn does not exist or has no state snapshot, inform the user and show available turn numbers.

Branch limits:

• •Maximum 3 active branches per wargame. If the user tries to create a 4th, prompt: "You have 3 active branches. Prune one with branches prune [N] or continue on the current branch."
• •Active branches are tracked in the state snapshot: branches: [{branch_id, fork_turn, current_turn}]

Branch navigation:

• •branches — List all active branches with their fork points and current turns
• •branches switch [N] — Switch to branch N, loading its latest state snapshot
• •branches prune [N] — Remove branch N (keeps journal content but marks as pruned)

Branches from abandoned sessions (status: Abandoned) are auto-pruned on next resume.

Sort Order

Journals sorted by filename (reverse chronological — newest first). This ordering is canonical for both list and resume N.

Corruption Resilience

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?"

In-Session Commands

Available during any active analysis or wargame. Type ? at any decision point to see the full menu.

All commands handled per protocols in references/wargame-engine.md (except criteria, export, verbose, research, rewind, branches, status, meta, compare, summary, and ? which are defined in this file). Display templates in references/output-formats-core.md and references/output-formats-commands.md.

Export Protocol

When the user types export or dashboard:

1. •Copy templates/dashboard.html to /tmp/wargame-dashboard-{turn}.html using bash cp (do NOT read the HTML template into context — it is 676 lines of static HTML/CSS/JS)
2. •Generate the JSON data block matching the current state and view. For field reference, use references/dashboard-schema.md.
3. •Insert JSON into the <script id="data"> block
4. •Render using the cross-platform flow from references/visualizations.md: Playwright screenshot → browser open → Unicode fallback

Additional export formats (specify after export):

JSON export: Full game state including all turns, actor states, decisions, outcomes, and AAR. Saved to ~/.claude/wargames/{slug}-export.json.

CSV export: One row per turn with columns: Turn, Decision, Outcome, Criteria Scores, Surprise. Saved to ~/.claude/wargames/{slug}-export.csv.

Slide outline: Markdown outline with: title slide, scenario overview, key decision points (1 slide each), findings, action bridge. Saved to ~/.claude/wargames/{slug}-slides.md.

Meta Command

When the user types meta (no active session required):

1. •Read all journals from ~/.claude/wargames/
2. •Analyze across journals for patterns:
   • •Recurring biases: Which human/LLM biases appear most frequently?
   • •Domain performance: Which domains show strongest/weakest decision quality?
   • •Criteria priorities: How do criteria rankings shift across scenarios?
   • •Risk tendency: Is the user consistently risk-seeking, risk-averse, or balanced?
   • •Calibration drift: Are probability estimates improving over time?
3. •Present as a "Decision Fitness Report" using the Meta Analysis Display from references/output-formats-commands.md

Requires 3+ completed journals. If fewer exist, inform user: "Need at least 3 completed journals for cross-session analysis. You have {N}."

Compare Command

When the user types compare [journal1] [journal2] (by number from list or keyword):

1. •Read both journals
2. •Present side-by-side comparison:
   • •Decisions made: Turn-by-turn decision differences
   • •Outcomes: Which strategy produced better results per criterion
   • •Common patterns: Shared decision tendencies across both runs
   • •Divergence points: Where strategies differed most and the consequences
   • •Verdict: Which strategy dominated, with caveats
3. •Use the Compare Display from references/output-formats-commands.md

Both journals must be for the same or similar scenario. If domains differ significantly, warn but proceed.

Summary Command

When the user types summary [N] (journal number from list):

1. •Read journal N
2. •Present a condensed 10-20 line summary:
   • •Scenario and tier (1 line)
   • •Key decisions and outcomes (3-5 lines)
   • •Top insight or transferable principle (2-3 lines)
   • •Action Bridge status: which Probe/Position/Commit actions were taken (2-3 lines)
   • •Final assessment: what went well, what to do differently (2-3 lines)
3. •Does NOT start an active session — read-only review

Difficulty Levels

Auto-mapped from tier (Clear→optimistic, Complicated→realistic, Complex→adversarial, Chaotic→worst-case). User can override during classification. Difficulty affects actor behavior, inject frequency, adjudication thresholds, and analysis tone in all modes.

See references/wargame-engine.md Difficulty Levels for full specification.

Tutorial Mode

When $ARGUMENTS is tutorial:

Run a pre-scripted 2-turn Complicated tier scenario with inline pedagogical annotations. Purpose: teach the wargame system to new users.

Scenario: "Your company's main competitor just poached your VP of Sales. You have 48 hours before the board meeting."

Structure:

1. •Classification — Show the scoring rubric with annotations explaining each dimension: "This scores 1 on Adversary because the competitor is actively hostile. Here's why that matters..."
2. •Criteria elicitation — Walk through the ranking process: "We'll use these criteria to evaluate your options. Ranking them forces you to articulate what matters most."
3. •Turn 1 — Present options with annotations on choice architecture: "Notice the options are randomized and show both success and failure probabilities. This counteracts framing bias."
4. •Turn 2 — Deploy one inject, complete with annotation: "Injects create dilemmas — they force trade-offs between competing objectives, not just add problems."
5. •Mini-AAR — Condensed review with annotations: "The AAR is where learning happens. We look for biases, extract principles, and plan next moves."

Total output: ~200 lines. Ends with: "Ready to try your own scenario? Paste it or type guide me."

Research Command

When the user types research during an active session:

1. •Identify the most relevant research question for the current decision point
2. •Run 1-2 targeted WebSearch queries focused on the specific domain and decision context
3. •Present findings using the Intelligence Research Display from references/output-formats-commands.md
4. •Return to the current decision point — does not advance the turn

If WebSearch is unavailable, inform user: "Web research requires search access. Not available in this session."

Facilitated Mode

When $ARGUMENTS is facilitated:

LLM serves as game master and adjudicator only. All actors are controlled by human players. For tabletop exercises (TTX) and group decision-making workshops.

Setup

1. •Scenario setup — Same as standard Interactive Wargame: classification, criteria, actor definitions
2. •Player assignment — Assign each actor to a human player (or group). The facilitator (LLM) does NOT control any actors.
3. •Rules briefing — Explain the turn structure and available commands to all players

Turn Structure (Facilitated)

1. •Situation brief — LLM presents the current state
2. •Player prompts — Prompt each player for their actor's action: "Player controlling {Actor}: What does {Actor} do this turn?"
3. •Collect all actions — Wait for all players to submit
4. •Adjudicate — LLM adjudicates using the standard Adjudication Protocol
5. •Generate consequences — LLM generates unexpected consequences
6. •Present results — Show outcomes and updated state to all players
7. •Decision point — If an inject fires, present it. Prompt next round of player actions.

Facilitator Guidelines

• •The LLM maintains neutrality — does not favor any player
• •Adjudication follows the standard protocol strictly
• •Injects are pre-seeded but deployment timing adapts to dramatic moments
• •The LLM may call for a "time-out" for group discussion before critical decisions
• •All standard in-session commands are available to any player

Reference File Index

Read reference files as indicated by the "Read When" column above. Do not rely on memory or prior knowledge of their contents. Reference files are the source of truth. If a reference file does not exist, proceed without it but note the gap in the journal.

Critical Rules

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

Canonical terms (use these exactly throughout):

• •Tiers: "Clear", "Complicated", "Complex", "Chaotic"
• •Modes: "Quick Analysis", "Structured Analysis", "Interactive Wargame", "Facilitated Wargame", "Tutorial"
• •Persona archetypes: "hawk", "dove", "pragmatist", "ideologue", "bureaucrat", "opportunist", "disruptor", "custom"
• •Difficulty levels: "optimistic", "realistic", "adversarial", "worst-case"
• •Dispatch commands: "resume", "list", "archive", "delete", "meta", "compare", "summary", "tutorial", "facilitated"
• •In-session commands: "red team", "what if", "criteria", "explore", "sensitivity", "delphi", "forecast", "negotiate", "calibrate", "options", "cause", "morph", "research", "rewind", "branches", "status", "export", "verbose", "meta", "compare", "summary", "?"
• •Verbosity levels: "brief", "standard", "detailed"
• •Action Bridge levels: "Probe", "Position", "Commit"
• •Journal statuses: "In Progress", "Complete", "Abandoned"
• •Domain tags (predefined): "biz", "career", "crisis", "geo", "personal", "startup", "negotiation", "tech", "custom" — plus auto-detected custom tags from scenario context