war-room

Convenes a multi-LLM expert panel to pressure-test hard-to-reverse decisions

Install

openclaw skills install @athola/nm-attune-war-room

Night Market Skill — ported from claude-night-market/attune. For the full experience with agents, hooks, and commands, install the Claude Code plugin.

Overview

The War Room convenes multiple AI experts to analyze problems from diverse perspectives, challenge assumptions through adversarial review, and synthesize optimal approaches under the guidance of a Supreme Commander.

Philosophy

"The trick is that there is no trick. The power of intelligence stems from our vast diversity, not from any single, perfect principle."

Marvin Minsky, Society of Mind

Reversibility-Based Routing

Before deliberation, assess the Reversibility Score (RS) to determine appropriate resource allocation:

text

RS = (Reversal Cost + Time Lock-In + Blast Radius + Information Loss + Reputation Impact) / 25

RS Range	Type	Mode	Resources
0.04 - 0.40	Type 2	Express	1 expert, < 2 min
0.41 - 0.60	Type 1B	Lightweight	3 experts, 5-10 min
0.61 - 0.80	Type 1A	Full Council	7 experts, 15-30 min
0.81 - 1.00	Type 1A+	Delphi	7 experts, 30-60 min

Quick Heuristics:

Can be A/B tested? → Type 2
Requires data migration? → Type 1
Public commitment required? → Type 1A+

See modules/reversibility-assessment.md for full scoring guide.

When To Use

Architectural decisions with major trade-offs
Multi-stakeholder problems requiring diverse perspectives
High-stakes choices with significant consequences (RS > 0.60)
Novel problems without clear precedent
When brainstorming produces multiple strong competing approaches

When NOT To Use

Simple questions with obvious answers
Routine implementation tasks
Well-documented patterns with clear solutions
Time-critical decisions requiring immediate action
Type 2 decisions (RS ≤ 0.40) — use Express mode or skip War Room entirely

Expert Panel

Default (Lightweight Mode)

Role	Model	Purpose
Supreme Commander	Claude Opus	Final synthesis, escalation decisions
Chief Strategist	Claude Sonnet	Approach generation, trade-off analysis
Red Team	Gemini Flash	Adversarial challenge, failure modes

Full Council (Escalated)

Role	Model	Purpose
Supreme Commander	Claude Opus	Final synthesis
Chief Strategist	Claude Sonnet	Approach generation
Intelligence Officer	Gemini 2.5 Pro	Large context analysis (1M+)
Field Tactician	GLM-4.7	Implementation feasibility
Scout	Qwen Turbo	Quick data gathering
Red Team Commander	Gemini Flash	Adversarial challenge
Logistics Officer	Qwen Max	Resource estimation

Deliberation Protocol

Two-Round Default

text

Round 1: Generation
  - Phase 1: Intelligence Gathering (Scout, Intel Officer)
  - Phase 2: Situation Assessment (Chief Strategist)
  - Phase 3: COA Development (Multiple experts, parallel)
  - Commander Escalation Check

Round 2: Pressure Testing
  - Phase 4: Red Team Review (all COAs)
  - Phase 5: Voting + Narrowing (top 2-3)
  - Phase 6: Premortem Analysis (selected COA)
  - Phase 7: Supreme Commander Synthesis
  - Phase 8: Discussion Publishing

Delphi Extension (High-Stakes)

For high-stakes decisions, extend to iterative Delphi convergence:

Multiple rounds until expert consensus
Convergence threshold: 0.85

Integration

With Brainstorm

War Room is AUTOMATICALLY INVOKED from Skill(attune:project-brainstorming) after Phase 3 (Approach Generation).

The brainstorm skill passes all context to War Room:

Problem statement and constraints
Generated approaches with pros/cons
Comparison matrix
Reversibility assessment (automatically calculated)

Bypass conditions (only if ALL true):

RS ≤ 0.40 (Type 2 decision - clearly reversible)
Single obvious approach with no meaningful trade-offs
Low complexity with well-documented pattern
User explicitly declines after seeing RS assessment

bash

# Automatic invocation from brainstorm (do not skip)
/attune:war-room --from-brainstorm

# Direct invocation (standalone)
/attune:war-room "Should we use microservices or monolith for this system?"

With Memory Palace

Sessions persist to the Strategeion (War Palace):

text

~/.claude/memory-palace/strategeion/
  - war-table/      # Active sessions
  - campaign-archive/  # Historical decisions
  - doctrine/       # Learned patterns
  - armory/         # Expert configurations

With Conjure

Experts are invoked via conjure delegation:

conjure:gemini-delegation for Gemini models
conjure:qwen-delegation for Qwen models
Direct CLI for GLM-4.7 (ccgd or claude-glm --dangerously-skip-permissions)

Usage

Basic Invocation

bash

/attune:war-room "What architecture should we use for the new payment system?"

With Context

bash

/attune:war-room "Best approach for API versioning" --files src/api/**/*.py

Reversibility Assessment Only

Quick assessment without full deliberation:

bash

/attune:war-room "Database migration to MongoDB" --assess-only

Output:

text

Reversibility Assessment
========================
Decision: Database migration to MongoDB

Dimensions:
  Reversal Cost:      5/5 (months of rework)
  Time Lock-In:       4/5 (migration path hardens)
  Blast Radius:       5/5 (all services affected)
  Information Loss:   4/5 (query patterns, ACID)
  Reputation Impact:  2/5 (internal unless downtime)

Reversibility Score: 0.80
Decision Type: Type 1A (One-Way Door)
Recommended Mode: Full Council

Proceed with full deliberation? [Y/n]

Force Express Mode (Type 2)

Skip to rapid decision for clearly reversible choices:

bash

/attune:war-room "Which logging library to use" --express

Force Full Council

Override RS assessment for critical decisions:

bash

/attune:war-room "Migration strategy" --full-council

Delphi Mode

For highest-stakes irreversible decisions:

bash

/attune:war-room "Long-term platform decision" --delphi

Resume Session

bash

/attune:war-room --resume war-room-20260120-153022

Output

Decision Document

The War Room produces a Supreme Commander Decision document:

markdown

## SUPREME COMMANDER DECISION: {session_id}

### Reversibility Assessment
| Dimension | Score | Rationale |
|-----------|-------|-----------|
| Reversal Cost | X/5 | ... |
| Time Lock-In | X/5 | ... |
| Blast Radius | X/5 | ... |
| Information Loss | X/5 | ... |
| Reputation Impact | X/5 | ... |

**RS: 0.XX | Type: [1A+/1A/1B/2] | Mode: [delphi/full_council/lightweight/express]**

### Decision
**Selected Approach**: [Name]

### Rationale
[Why this approach was selected]

### Implementation Orders
1. [ ] Immediate actions
2. [ ] Short-term actions

### Watch Points
[From Premortem - what to monitor]

### Reversal Plan (for Type 1 decisions)
[If this decision proves wrong, here's the exit strategy]

### Dissenting Views
[For the record]

Session Artifacts

Saved to Strategeion:

Intelligence reports
Situation assessment
All COAs (with full attribution after unsealing)
Red Team challenges
Premortem analysis
Final decision

Anonymization

Expert contributions are anonymized during deliberation using Merkle-DAG:

Responses labeled as "Response A, B, C..." during review
Attribution revealed only after decision is made
Hash verification ensures integrity

See modules/merkle-dag.md for details.

Escalation

Automatic (Reversibility-Based)

Deliberation mode is automatically selected based on Reversibility Score:

RS Score	Automatic Mode
≤ 0.40	Express (bypass full War Room)
0.41 - 0.60	Lightweight panel
0.61 - 0.80	Full Council
> 0.80	Full Council and Delphi

Manual Override

The Supreme Commander may override automatic classification when:

High complexity detected (multiple architectural trade-offs)
Significant disagreement between initial experts
Novel problem domain requiring specialized analysis
Precedent-setting decision (future decisions will follow pattern)
Political/organizational sensitivity beyond technical scope

Escalation requires written justification with RS assessment.

De-escalation

Equally important: identify decisions being over-deliberated:

If RS ≤ 0.40, recommend Express mode or immediate execution
Challenge "false irreversibility" ("we can't change this later" without evidence)
Track de-escalation rate as team health metric

Configuration

User Settings

json

{
  "war_room": {
    "default_mode": "lightweight",
    "auto_escalate": true,
    "delphi_threshold": 0.85,
    "max_delphi_rounds": 5
  }
}

Hook Auto-Trigger

War Room can be auto-suggested via hook when:

Keywords detected ("strategic decision", "trade-off", etc.)
Complexity score exceeds threshold (0.7)
User has opted in via settings

Agent Teams Execution Mode

Overview

When --agent-teams is specified (or auto-selected for Full Council / Delphi modes), the War Room uses Claude Code Agent Teams instead of sequential conjure delegation. Each expert runs as a persistent teammate with bidirectional messaging, enabling real-time deliberation instead of batch request/response cycles.

Requires: Claude Code 2.1.32+, CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS=1, tmux installed.

When Agent Teams Helps

Mode	Without Agent Teams	With Agent Teams	Benefit
Express	Sonnet direct call	N/A (overkill)	None — skip
Lightweight	3 sequential delegations	N/A (overhead exceeds benefit)	None — skip
Full Council	7 sequential/parallel delegations	7 teammates with live inbox messaging	Experts can react to each other's COAs in real-time
Delphi	Multiple delegation rounds	Persistent team iterates until convergence	No re-invocation cost per round; state preserved across rounds

Rule of thumb: Use agent teams only for Full Council and Delphi modes. Lightweight and Express modes don't generate enough inter-expert traffic to justify the coordination overhead.

Team Configuration

bash

# War Room agent team structure
Team: war-room-{session-id}
  Lead: supreme-commander (Opus) — orchestrates phases, final synthesis
  Teammates:
    chief-strategist (Sonnet) — approach generation
    intel-officer (Sonnet) — deep context analysis
    field-tactician (Sonnet) — implementation feasibility
    scout (Haiku) — rapid reconnaissance
    red-team (Sonnet) — adversarial challenge
    logistics (Haiku) — resource estimation

Note: In agent teams mode, all teammates run as Claude Code instances (Opus/Sonnet/Haiku). External LLM experts (Gemini, Qwen, GLM) are not used because agent teams requires the Claude CLI. The trade-off is losing model diversity but gaining real-time inter-expert messaging.

Deliberation Flow with Agent Teams

Lead creates team → spawns teammates in tmux panes
Phase 1 (Intel): Lead assigns intel tasks to scout and intel-officer via inbox
Phase 3 (COA): Lead broadcasts situation assessment; teammates develop COAs independently; messaging allows clarifying questions mid-development
Phase 4 (Red Team): Red-team teammate receives all COAs, posts challenges; other teammates can respond to challenges in real-time
Phase 5 (Voting): Lead broadcasts ballot; teammates rank via inbox messages
Phase 6 (Premortem): All teammates receive selected COA; can build on each other's failure scenarios
Phase 7 (Synthesis): Lead collects all artifacts, produces decision
Phase 8 (Discussion Publishing): After the Supreme Commander Decision document is finalized, you MUST execute modules/discussion-publishing.md to publish the decision to GitHub Discussions. Publishing is the default. The user can decline with "n". See the "Discussion Publishing (REQUIRED)" section below for the full step-by-step workflow.

Falling Back to Conjure Delegation

If agent teams fails (tmux unavailable, team creation error), the War Room automatically falls back to standard conjure delegation. The deliberation protocol is identical — only the execution backend differs.

Cost Considerations

Agent teams is significantly more token-intensive than conjure delegation (each teammate maintains its own context window). Use only when the coordination value justifies the cost — typically Delphi mode where multiple rounds of revision make persistent teammates worthwhile.

Discussion Publishing (REQUIRED)

After Phase 7 synthesis completes (in any execution mode), you MUST execute the discussion publishing workflow. This is not optional unless the user explicitly declines.

Execute these steps in order:

Read modules/discussion-publishing.md for the full GraphQL workflow
Ask the user: "Publishing this decision to GitHub Discussions. [Y/n]"
If the user says "n", skip to Related Skills. Otherwise proceed with steps 4-6.
Run the gh api graphql commands from the module to create a Discussion in the "Decisions" category
Post phase summaries as threaded comments on the Discussion
Update the local strategeion file with the Discussion URL

If GitHub Discussions are unavailable (non-GitHub platform, Discussions disabled, gh not authenticated), warn the user and skip. Publishing failures never block the war room workflow.

Related Skills

Skill(attune:project-brainstorming) - Pre-War Room ideation
Skill(imbue:scope-guard) - Scope management
Skill(imbue:rigorous-reasoning) - Reasoning methodology
Skill(conjure:delegation-core) - Expert dispatch
Skill(conjure:agent-teams) - Agent teams coordination (Full Council / Delphi)

Related Commands

/attune:war-room - Invoke this skill
/attune:brainstorm - Pre-War Room ideation
/memory-palace:strategeion - Access War Room history

References

Strategic Foundations

Sun Tzu - Art of War (intelligence gathering)
Clausewitz - On War (friction and fog)
Robert Greene - 33 Strategies of War (unity of command)
MDMP - U.S. Army (structured decision process)
Gary Klein - Premortem (failure mode analysis)
Karpathy - LLM Council (anonymized peer review)

Reversibility Framework

Jeff Bezos - Type 1 vs Type 2 Decisions (Amazon shareholder letters)
Farnam Street - Reversible and Irreversible Decisions (STOP-LOP-KNOW framework)
Tapan Desai - One-Way and Two-Way Door Decision-Making (practical application)