war-room-checkpoint

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](#purpose)
2. [When Commands Should Invoke This](#when-commands-should-invoke-this)
3. [Invocation Pattern](#invocation-pattern)
4. [Checkpoint Flow](#checkpoint-flow)
5. [Confidence Calculation](#confidence-calculation)
6. [Profile Thresholds](#profile-thresholds)
7. [Output Format](#output-format)
8. [Examples](#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

| Command | Trigger Conditions | |---------|-------------------| | /do-issue | 3+ issues, dependency conflicts, overlapping files | | /pr-review | >3 blocking issues, architecture changes, ADR violations | | /architecture-review | ADR violations, high coupling, boundary violations | | /fix-pr | Major 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:

| Dimension | Assessment Question | |-----------|-------------------| | Reversal Cost | How hard to undo this decision? | | Time Lock-In | Does this crystallize immediately? | | Blast Radius | How many components/people affected? | | Information Loss | Does this close off future options? | | Reputation Impact | Is 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

| Profile | Express | Lightweight | Full Council | Use Case | |---------|---------|-------------|--------------|----------| | default | 0.40 | 0.60 | 0.80 | Balanced | | startup | 0.55 | 0.75 | 0.90 | Move fast | | regulated | 0.25 | 0.45 | 0.65 | Compliance | | fast | 0.50 | 0.70 | 0.90 | Speed priority | | cautious | 0.30 | 0.50 | 0.70 | Higher stakes |

Command-Specific Adjustments

| Command | Adjustment | Rationale | |---------|-----------|-----------| | do-issue (3+ issues) | -0.10 | Higher risk with multiple issues | | pr-review (strict mode) | -0.15 | Strict mode = higher scrutiny | | architecture-review | -0.05 | Architecture 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: