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Skillv1.0.0

war-room

Multi-LLM deliberation framework for strategic decisions through pressure-based expert consultation

by David-Li0406(0) 0 installs
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About

Imported from David-Li0406/meta-skill-evloving (skill-flow/data/skills-refined-skillclaw-36k/skillsmp/war-room/SKILL.md). Install upstream with npx skills add David-Li0406/meta-skill-evloving --skill war-room. Copyright stays with the author.

War Room Skill

Orchestrate multi-LLM deliberation for complex strategic decisions.

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:

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

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

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 can be invoked from /attune:brainstorm when:

  • Multiple strong approaches emerge with no clear winner
  • User explicitly requests expert council
  • Problem complexity exceeds threshold
# From brainstorm, escalate to War Room
/attune:war-room --from-brainstorm

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

With Memory Palace

Sessions persist to the Strategeion (War Palace):

~/.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

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

With Context

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

Reversibility Assessment Only

Quick assessment without full deliberation:

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

Output:

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:

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

Force Full Council

Override RS assessment for critical decisions:

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

Delphi Mode

For highest-stakes irreversible decisions:

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

Resume Session

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

Output

Decision Document

The War Room produces a Supreme Commander Decision document:

## 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 + 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

{
  "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

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

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

Use it

Copy one of these into your project. Installing also returns the manifest and these snippets.

yaml
targets:
  - https://api.opensmartroute.ai/api/v1/registry/david-li0406-meta-skill-evloving-war-room/manifest   # or paste the manifest below

Manifest

An Open Capability Manifest: the router reads it to know what this does, what it costs and when to pick it.

david-li0406-meta-skill-evloving-war-room.ocm.jsonjson
{
  "ocm": "1",
  "id": "david-li0406-meta-skill-evloving-war-room",
  "kind": "skill",
  "name": "war-room",
  "description": "Multi-LLM deliberation framework for strategic decisions through pressure-based expert consultation",
  "publisher": "David-Li0406",
  "version": "1.0.0",
  "capabilities": {
    "domains": [
      "general"
    ],
    "tags": [
      "skill-md",
      "deliberation",
      "multi-llm",
      "strategy",
      "decision-making",
      "council",
      "github"
    ],
    "languages": [
      "en"
    ]
  },
  "quality_prior": 0.6,
  "examples": [
    "Multi-LLM deliberation framework for strategic decisions through pressure-based expert consultation"
  ],
  "primary": false,
  "metadata": {
    "source": {
      "provider": "github",
      "repository": "https://github.com/David-Li0406/meta-skill-evloving",
      "path": "skill-flow/data/skills-refined-skillclaw-36k/skillsmp/war-room/SKILL.md",
      "ref": "ca3a335628981df10c36e00cb9850df2c247ab9a",
      "url": "https://github.com/David-Li0406/meta-skill-evloving/blob/ca3a335628981df10c36e00cb9850df2c247ab9a/skill-flow/data/skills-refined-skillclaw-36k/skillsmp/war-room/SKILL.md",
      "key": "David-Li0406/meta-skill-evloving/skill-flow/data/skills-refined-skillclaw-36k/skillsmp/war-room/SKILL.md"
    }
  },
  "instructions": "# War Room Skill\n\nOrchestrate multi-LLM deliberation for complex strategic decisions.\n\n## Overview\n\nThe 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.\n\n### Philosophy\n\n> \"The trick is that there is no trick. The power of intelligence stems from our vast diversity, not from any single, perfect principle.\"\n> - Marvin Minsky, Society of Mind\n\n## Reversibility-Based Routing\n\nBefore deliberation, assess the **Reversibility Score (RS)** ",
  "cost": {
    "context_tokens": 2431
  }
}

Fetch it by URL: GET /api/v1/registry/david-li0406-meta-skill-evloving-war-room/manifest?version=1.0.0

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