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nm-attune-war-roomnm attune 作战室

Agent Skill

nm-attune-war-room 用于辅助前端页面、组件、样式和交互逻辑开发,适合在 OpenClaw 中需要维护前端项目、生成组件或检查界面实现时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

2,064

周安装

86

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下载量

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OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:nm-attune-war-room(nm attune 作战室)
来源仓库:https://github.com/athola/nm-attune-war-room
安装命令:
openclaw skills install nm-attune-war-room
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install nm-attune-war-room

简介

召集多位专家进行高风险决策的压力测试并建立共识。

  • 适合在 OpenClaw 中处理关键设计或架构选择时使用。
  • 核心能力是通过结构化审议降低决策风险。nm-attune-war-room 属于开发类 Skill,可作为该场景下的辅助能力补充。
  • 通过 clawhub 安装,建议限定讨论范围以提高效率。
  • 注意维护状态及是否记录讨论结论至文件系统。

SKILL.md

name
war-room
description
|
version
1.8.2
triggers
metadata
{"openclaw": {"homepage": "https://github.com/athola/claude-night-market/tree/master/plugins/attune", "emoji": "\�\�", "requires": {"config": ["night-market.conjure:delegation-core", "night-market.memory-palace:strategeion", "night-market.leyline:git-platform"]}}}
source
claude-night-market
source_plugin
attune
Night Market Skill — ported from claude-night-market/attune. For the full experience with agents, hooks, and commands, install the Claude Code plugin.

Table of Contents

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 RangeTypeModeResources
0.04 - 0.40Type 2Express1 expert, < 2 min
0.41 - 0.60Type 1BLightweight3 experts, 5-10 min
0.61 - 0.80Type 1AFull Council7 experts, 15-30 min
0.81 - 1.00Type 1A+Delphi7 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)

RoleModelPurpose
Supreme CommanderClaude OpusFinal synthesis, escalation decisions
Chief StrategistClaude SonnetApproach generation, trade-off analysis
Red TeamGemini FlashAdversarial challenge, failure modes

Full Council (Escalated)

RoleModelPurpose
Supreme CommanderClaude OpusFinal synthesis
Chief StrategistClaude SonnetApproach generation
Intelligence OfficerGemini 2.5 ProLarge context analysis (1M+)
Field TacticianGLM-4.7Implementation feasibility
ScoutQwen TurboQuick data gathering
Red Team CommanderGemini FlashAdversarial challenge
Logistics OfficerQwen MaxResource 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
  - 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
# 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):

~/.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 ScoreAutomatic Mode
≤ 0.40Express (bypass full War Room)
0.41 - 0.60Lightweight panel
0.61 - 0.80Full Council
> 0.80Full 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

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

ModeWithout Agent TeamsWith Agent TeamsBenefit
ExpressSonnet direct callN/A (overkill)None — skip
Lightweight3 sequential delegationsN/A (overhead exceeds benefit)None — skip
Full Council7 sequential/parallel delegations7 teammates with live inbox messagingExperts can react to each other's COAs in real-time
DelphiMultiple delegation roundsPersistent team iterates until convergenceNo 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

# 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

  1. Lead creates team → spawns teammates in tmux panes
  2. Phase 1 (Intel): Lead assigns intel tasks to scout + intel-officer via inbox
  3. Phase 3 (COA): Lead broadcasts situation assessment; teammates develop COAs independently; messaging allows clarifying questions mid-development
  4. Phase 4 (Red Team): Red-team teammate receives all COAs, posts challenges; other teammates can respond to challenges in real-time
  5. Phase 5 (Voting): Lead broadcasts ballot; teammates rank via inbox messages
  6. Phase 6 (Premortem): All teammates receive selected COA; can build on each other's failure scenarios
  7. Phase 7 (Synthesis): Lead collects all artifacts, produces decision
  8. 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:

  1. Read modules/discussion-publishing.md for the

full GraphQL workflow

  1. Ask the user: "Publishing this decision to GitHub

Discussions. [Y/n]"

  1. If the user says "n", skip to Related Skills.

Otherwise proceed with steps 4-6.

  1. Run the gh api graphql commands from the module

to create a Discussion in the "Decisions" category

  1. Post phase summaries as threaded comments on the

Discussion

  1. 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

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

97.36%
按下载量换算670

安全审计

VirusTotal

可疑

ClawScan

可疑

Static analysis

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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