Source profileQuality 94/100

athola/claude-night-market/plugins/attune/skills/war-room-checkpoint/SKILL.md

war-room-checkpoint

Assesses decision reversibility and risk at critical checkpoints. Use when a workflow reaches a high-stakes branch needing escalation check.

Source repository stars
331
Declared platforms
0
Static risk flags
0
Last source update
2026-08-26
Source checked
2026-08-28

Decision brief

What it does: where it fits

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

Best for

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

Not for

  • This skill decides whether deliberation is warranted and returns fast when it is not. A command that checkpoints every decision pays the scoring cost to be told to proceed almost every time, and re-checkpointing a settl…

Compatibility matrix

Platform support, with evidence labels

PlatformStatusEvidenceWhat to check
CodexNot declaredNo explicit evidencePortability before use
Claude CodeNot declaredNo explicit evidencePortability before use
CursorNot declaredNo explicit evidencePortability before use
Gemini CLINot declaredNo explicit evidencePortability before use
Open the compatibility checker

Installation

Inspect first. Install second.

The source command is displayed only when detected. A safe inspection prompt is always available so your agent can explain every action before execution.

Source-detected install commandSource
npx skills add https://github.com/athola/claude-night-market --skill "plugins/attune/skills/war-room-checkpoint"
Safe inspection promptEditorial

Inspect the Agent Skill "war-room-checkpoint" from https://github.com/athola/claude-night-market/blob/6720bb5cdeadeea6de6e4786a449126b3d417536/plugins/attune/skills/war-room-checkpoint/SKILL.md at commit 6720bb5cdeadeea6de6e4786a449126b3d417536. List every install step, command, network request, credential, file read/write, external action, and rollback step. Explain whether it fits my task. Do not install or execute anything until I approve.

Workflow

What the source asks the agent to do

  1. 01

    Verification

    Run make attune-test from the repository root to verify checkpoint logic still works after changes.

    Run make attune-test from the repository root to verify checkpoint logic still works after changes.
  2. 02

    Step 1: Context Analysis

    Analyze the provided context to extract: - Scope of change (files, modules, services affected) - Stakeholders impacted - Conflict indicators - Time pressure signals

    Scope of change (files, modules, services affected)Stakeholders impactedConflict indicators
  3. 03

    Step 2: Reversibility Assessment

    Calculate RS using the 5-dimension framework:

    Calculate RS using the 5-dimension framework:Score each 1-5, calculate RS = Sum / 25.
  4. 04

    Step 3: Mode Selection

    Apply profile thresholds to determine mode:

    Apply profile thresholds to determine mode:
  5. 05

    Step 4: Response Generation

    Return immediately with recommendation:

    Return immediately with recommendation:Invoke full War Room and return results:

Permission review

Static risk signals and limitations

No configured static risk pattern was detected

This is not proof of safety. Runtime behavior, indirect dependencies, and hidden external systems are outside the static scan.

Evidence record

Why each signal appears

EvidenceSourceComputedTestedEditorial
SignalValueEvidence typeMeaning
Quality score94/100ComputedDocumentation, specificity, maintenance, and trust rules
Repository stars331SourceRepository attention, not individual Skill quality
Compatibility0 platformsSourceDeclared in the catalog source record
Usage guideautomated source guideEditorialGenerated or reviewed according to the visible evidence level

Pinned source

Provenance and original SKILL.md

Repository
athola/claude-night-market
Skill path
plugins/attune/skills/war-room-checkpoint/SKILL.md
Commit
6720bb5cdeadeea6de6e4786a449126b3d417536
License
MIT
Collected
2026-08-28
Default branch
master
View the original 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 attune-test from the repository root to verify checkpoint logic still works 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

When NOT To Use

SituationUse instead
A user asks for deliberation directlySkill(attune:war-room)
The decision is cheap to reverse (high RS)Proceed without a checkpoint
A panel already ruled on this decisionThe prior verdict

This skill decides whether deliberation is warranted and returns fast when it is not. A command that checkpoints every decision pays the scoring cost to be told to proceed almost every time, and re-checkpointing a settled call re-litigates it.

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)

Exit Criteria

  • A structured checkpoint response is returned with all required fields: reversibility_score (0.0-1.0), selected_mode (express / lightweight / full_council / delphi), should_escalate (boolean), and recommendation or orders.
  • Any response with reversibility_score > profile threshold has should_escalate: true and triggers the full War Room via Skill(attune:war-room) before returning.
  • Any response with confidence <= 0.8 sets requires_user_confirmation: true and presents a confirmation prompt to the user rather than auto-continuing.
  • The checkpoint is logged to ~/.claude/memory-palace/strategeion/checkpoints/{date}/{checkpoint-id}.json; if this write fails, the calling command proceeds and logs a warning rather than blocking the workflow.

Frequently asked questions

What to verify before installation and use

What does the war-room-checkpoint source document cover?

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

How do I install war-room-checkpoint?

The source record exposes this install command: npx skills add https://github.com/athola/claude-night-market --skill "plugins/attune/skills/war-room-checkpoint". Inspect the command and pinned source before running it.

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