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war-room-checkpoint作战室检查站

Agent Skill

war-room-checkpoint 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。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

832

周安装

35

GitHub Stars

264

下载量

291
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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

unknown

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:war-room-checkpoint(作战室检查站)
来源仓库:https://github.com/athola/claude-night-market
仓库路径:skills/war-room-checkpoint
安装命令:
npx skills add https://github.com/athola/claude-night-market --skill war-room-checkpoint
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 npx skills 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。

skills.shnpx skills
npx skills add https://github.com/athola/claude-night-market --skill war-room-checkpoint

简介

用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息。

  • 适合在 Codex、Claude、Cursor、Gemini CLI 中围绕仓库状态、代码变更或协作事项进行整理。
  • 通过 npx skills add 命令从指定仓库安装,需结合原始 README 核验具体用法。
  • 安装前建议确认权限范围、维护状态及是否会触发联网、命令执行或文件读写。
  • war-room-checkpoint 属于待分类类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

War Room Checkpoint Skill

Lightweight inline assessment for determining whether a decision point within a command warrants War Room escalation.

Table of Contents

  1. Purpose
  2. When Commands Should Invoke This
  3. Invocation Pattern
  4. Checkpoint Flow
  5. Confidence Calculation
  6. Profile Thresholds
  7. Output Format
  8. Examples

Verification

Run make test-checkpoint to verify checkpoint logic works correctly after changes.

Purpose

This skill is not invoked directly by users. It is called by other commands (e.g., /do-issue, /pr-review) at critical decision points to:

  1. Calculate Reversibility Score (RS) for the current context
  2. Determine if full War Room deliberation is needed
  3. Return either a quick recommendation (express) or escalate to full War Room

When Commands Should Invoke This

CommandTrigger Conditions
/do-issue3+ issues, dependency conflicts, overlapping files
/pr-review>3 blocking issues, architecture changes, ADR violations
/architecture-reviewADR violations, high coupling, boundary violations
/fix-prMajor scope, conflicting reviewer feedback

Invocation Pattern

Skill(attune:war-room-checkpoint) with context:
  - source_command: "{calling_command}"
  - decision_needed: "{human_readable_question}"
  - files_affected: [{list_of_files}]
  - issues_involved: [{issue_numbers}] (if applicable)
  - blocking_items: [{type, description}] (if applicable)
  - conflict_description: "{summary}" (if applicable)
  - profile: "default" | "startup" | "regulated" | "fast" | "cautious"

Checkpoint Flow

Step 1: Context Analysis

Analyze the provided context to extract:

  • Scope of change (files, modules, services affected)
  • Stakeholders impacted
  • Conflict indicators
  • Time pressure signals

Step 2: Reversibility Assessment

Calculate RS using the 5-dimension framework:

DimensionAssessment Question
Reversal CostHow hard to undo this decision?
Time Lock-InDoes this crystallize immediately?
Blast RadiusHow many components/people affected?
Information LossDoes this close off future options?
Reputation ImpactIs this visible externally?

Score each 1-5, calculate RS = Sum / 25.

Step 3: Mode Selection

Apply profile thresholds to determine mode:

if RS <= profile.express_ceiling:
    mode = "express"
elif RS <= profile.lightweight_ceiling:
    mode = "lightweight"
elif RS <= profile.full_council_ceiling:
    mode = "full_council"
else:
    mode = "delphi"

Step 4: Response Generation

Express Mode (RS <= threshold)

Return immediately with recommendation:

response:
  should_escalate: false
  selected_mode: "express"
  reversibility_score: {rs}
  decision_type: "Type 2"
  recommendation: "{quick_recommendation}"
  rationale: "{brief_explanation}"
  confidence: 0.9
  requires_user_confirmation: false

Escalate Mode (RS > threshold)

Invoke full War Room and return results:

response:
  should_escalate: true
  selected_mode: "{lightweight|full_council|delphi}"
  reversibility_score: {rs}
  decision_type: "{Type 1B|1A|1A+}"
  war_room_session_id: "{session_id}"
  orders: ["{order_1}", "{order_2}"]
  rationale: "{war_room_rationale}"
  confidence: {calculated_confidence}
  requires_user_confirmation: {true_if_confidence_low}

Confidence Calculation

For escalated decisions, calculate confidence for auto-continue:

confidence = 1.0
- 0.10 * dissenting_view_count
- 0.20 if voting_margin < 0.3
- 0.15 if RS > 0.80
- 0.10 if novel_domain
- 0.10 if compound_decision
+ 0.20 if unanimous (cap at 1.0)

requires_user_confirmation = (confidence <= 0.8)

Profile Thresholds

ProfileExpressLightweightFull CouncilUse Case
default0.400.600.80Balanced
startup0.550.750.90Move fast
regulated0.250.450.65Compliance
fast0.500.700.90Speed priority
cautious0.300.500.70Higher stakes

Command-Specific Adjustments

CommandAdjustmentRationale
do-issue (3+ issues)-0.10Higher risk with multiple issues
pr-review (strict mode)-0.15Strict mode = higher scrutiny
architecture-review-0.05Architecture inherently consequential

Output Format

For Calling Command

Return a structured response that the calling command can act on:

## Checkpoint Response

**Source**: {source_command}
**Decision**: {decision_needed}

### Assessment
- **RS**: {reversibility_score} ({decision_type})
- **Mode**: {selected_mode}
- **Escalated**: {yes|no}

### Recommendation
{recommendation_or_orders}

### Control Flow
- **Confidence**: {confidence}
- **Auto-continue**: {yes|no}
{user_prompt_if_needed}

Integration Notes

Calling Commands Should

  1. Check checkpoint response's requires_user_confirmation
  2. If true: present confirmation prompt and wait
  3. If false: continue with orders or recommendation
  4. Log checkpoint to audit trail

Failure Handling

If checkpoint invocation fails:

  • Log warning with context
  • Continue command execution without checkpoint
  • Do NOT block the user's workflow

Audit Trail

Checkpoints are logged to:

~/.claude/memory-palace/strategeion/checkpoints/{date}/{checkpoint-id}.json

Each file contains a CheckpointEntry with: checkpoint_id, session_id, phase, action, reversibility_score, dimensions, confidence, files_affected, and requires_user_confirmation.

After a war room session completes and persist_session() is called, an audit report is written automatically to:

~/.claude/memory-palace/strategeion/war-table/{session-id}/audit-report.json

The report consolidates: all checkpoints for the session, the expert panel, voting summary with unanimity score, escalation history, final decision and rationale, and a Merkle-DAG integrity verification block. The verification recomputes every node hash against the stored values so any tampering with deliberation content is detectable.

Use AuditTrailManager from scripts.war_room.audit_trail to query checkpoints or generate reports programmatically:

from scripts.war_room.audit_trail import AuditTrailManager
manager = AuditTrailManager()
checkpoints = manager.get_checkpoints("war-room-20260303-100000")
audited = manager.list_audited_sessions()

Examples

Example 1: Low RS (Express)

Input:

source_command: "do-issue"
decision_needed: "Execution order for issues #101, #102"
issues_involved: [101, 102]
files_affected: ["src/utils/helper.py", "tests/test_helper.py"]

Assessment:

  • Reversal Cost: 1 (can revert commits)
  • Time Lock-In: 1 (no deadline)
  • Blast Radius: 1 (single utility module)
  • Information Loss: 1 (all options preserved)
  • Reputation Impact: 1 (internal)

RS: 0.20 (Type 2)

Response:

should_escalate: false
selected_mode: "express"
recommendation: "Execute in parallel - no dependencies detected"
confidence: 0.95
requires_user_confirmation: false

Example 2: High RS (Escalate)

Input:

source_command: "pr-review"
decision_needed: "Review verdict for PR #456"
blocking_items:
  - {type: "architecture", description: "New service without ADR"}
  - {type: "breaking", description: "API contract change"}
  - {type: "security", description: "Auth flow modification"}
  - {type: "scope", description: "Unrelated payment refactor"}
files_affected: ["src/auth/", "src/api/", "src/payment/", "src/services/new/"]

Assessment:

  • Reversal Cost: 4 (multi-service impact)
  • Time Lock-In: 3 (PR deadline pressure)
  • Blast Radius: 4 (cross-team impact)
  • Information Loss: 3 (some paths closing)
  • Reputation Impact: 2 (internal review)

RS: 0.64 (Type 1A)

Response:

should_escalate: true
selected_mode: "full_council"
war_room_session_id: "war-room-20260125-143025"
orders:
  - "Split PR: auth changes separate from payment refactor"
  - "Require ADR for new service before merge"
  - "API change: add migration path, not blocking"
confidence: 0.75
requires_user_confirmation: true

Related Skills

  • Skill(attune:war-room) - Full War Room deliberation
  • Skill(attune:war-room)/modules/reversibility-assessment.md - RS framework

Related Commands

  • /attune:war-room - Standalone War Room invocation
  • /do-issue - Issue implementation (uses this checkpoint)
  • /pr-review - PR review (uses this checkpoint)
  • /architecture-review - Architecture review (uses this checkpoint)
  • /fix-pr - PR fix (uses this checkpoint)

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

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

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

Codex

38.02%
按下载量换算111

Claude

30.25%
按下载量换算88

Cursor

18.47%
按下载量换算54

Gemini CLI

9.24%
按下载量换算27

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

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。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。

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