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War Room Checkpoint

  • 105 installs
  • 325 repo stars
  • Updated August 2, 2026
  • athola/claude-night-market

Embed a lightweight reversibility and risk checkpoint inside commands so high-stakes branches escalate to full War Room deliberation instead of silent autopilot.

About

War Room Checkpoint is a journey-wide agent skill library that Athola-style commands call when a workflow hits a high-stakes fork—merge conflicts, irreversible deploys, scope cuts, or security-sensitive PR paths. It scores how reversible the pending choice is, computes confidence against profile thresholds, and either returns a quick express recommendation or escalates to the full War Room skill. Solo builders benefit because one-person teams skip formal review boards yet still need a consistent “stop and think” gate before damage is hard to undo. Wire it into `/do-issue`, `/pr-review`, or similar automation so every risky branch gets the same RS math instead of ad-hoc gut feel. After changes to checkpoint logic, run `make test-checkpoint` to keep escalation behavior trustworthy.

  • Library skill invoked by commands—not end users directly—at critical decision points
  • Calculates a Reversibility Score (RS) and confidence to choose express recommendation vs full War Room
  • Profile thresholds tune how aggressive escalation is for your risk appetite
  • Pairs with attune:war-room dependency for deep deliberation when RS is low
  • Documented checkpoint flow, output format, and `make test-checkpoint` verification hook

War Room Checkpoint by the numbers

  • 105 all-time installs (skills.sh)
  • Ranked #1,341 of 3,282 Productivity & Planning skills by installs in the Skillselion catalog
  • Security screen: LOW risk (skills.sh audit)
  • Data as of Aug 5, 2026 (Skillselion catalog sync)
npx skills add https://github.com/athola/claude-night-market --skill war-room-checkpoint

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Listed on Skillselion
Installs105
repo stars325
Security audit3 / 3 scanners passed
Last updatedAugust 2, 2026
Repositoryathola/claude-night-market

What it does

Embed a lightweight reversibility and risk checkpoint inside commands so high-stakes branches escalate to full War Room deliberation instead of silent autopilot.

Files

SKILL.mdMarkdownGitHub ↗

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)

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.

Related skills

FAQ

Is War Room Checkpoint safe to install?

skills.sh reports 3 of 3 security scanners passed. Review the Security Audits panel on this page before installing in production.

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