New: 98,000+ skills indexed from GitHub repos with 100+ stars · every skill safety-checkedBrowse top skillsTop skills
Esc to closeBrowse all skills
Submit a skillBrowse skills
Submit a skillBrowse skills

Agent skill

War Room Checkpoint

by athola in athola/claude-night-market

Assesses decision reversibility and risk at critical checkpoints.

MITAuto-check passedDevelopment

Install War Room Checkpoint

skills CLI
$ npx skills add athola/claude-night-market --skill war-room-checkpoint -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install athola/claude-night-market war-room-checkpoint --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/athola/claude-night-market.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/attune/skills/war-room-checkpoint .claude/skills/war-room-checkpoint && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
war-room-checkpoint
GitHub stars
341
Token cost
~2.6k tokens
SKILL.md length
760 words
Files
1
Skills in repo
152
Repo updated
First seen
Licence
MIT

At a glance

Assesses decision reversibility and risk at critical checkpoints.

  • Works in 4 steps: Context Analysis → Reversibility Assessment → Mode Selection → …
  • A workflow reaches a high-stakes branch needing escalation check
  • SKILL.md covers Verification, Purpose, When Commands Should Invoke This and When NOT To Use, plus 11 more sections
  • Calls make

What it does

War Room Checkpoint is an agent skill from athola/claude-night-market. Assesses decision reversibility and risk at critical checkpoints. Use when a workflow reaches a high-stakes branch needing escalation check.

Its SKILL.md is about 2.6k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Development. The repository describes itself as: 23 Claude Code plugins: TDD enforcement hooks, git/PR workflows, spec-driven development, code review, project lifecycle, fix-from-error, maintenance automation, context… The licence is MIT.

When your agent uses it

  • A workflow reaches a high-stakes branch needing escalation check

Example prompts

  • “Use the war-room-checkpoint skill to assess decision reversibility and risk at critical checkpoints”
  • “/war-room-checkpoint”

Requirements

  • Python 3

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Context Analysis
  2. Reversibility Assessment
  3. Mode Selection
  4. Response Generation

What it can do on your machine

Read from SKILL.md and the folder at commit 9f3eb00. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • make

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

War Room Checkpoint loads about 2.6k tokens when it runs. Until then it costs about 40 tokens; SKILL.md has 760 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~40
When it runs · the whole SKILL.md, loaded when a task matches
~2.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from athola/claude-night-market at commit 9f3eb00, republished under its MIT licence (© athola). 760 words, ~2,610 tokens.

Download SKILL.mdSave it as .claude/skills/war-room-checkpoint/SKILL.md (or your agent's skills folder).
name
war-room-checkpoint
description
Assesses decision reversibility and risk at critical checkpoints. Use when a workflow reaches a high-stakes branch needing escalation check.
alwaysApply
false
model
sonnet
category
strategic-planning
tags
checkpoint, embedded, escalation, reversibility, inline
dependencies
attune:war-room
complexity
lightweight
model_hint
fast
estimated_tokens
400
progressive_loading
false
role
library

War Room Checkpoint Skill

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

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

markdown
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:

yaml
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:

yaml
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:

markdown
## 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
Show full SKILL.md (302 more words)Show less

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:

python
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:

yaml
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:

yaml
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:

yaml
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:

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

© athola, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

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

Open the folder on GitHubat commit 9f3eb00

Compare with similar skills

War Room Checkpoint next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

War Room Checkpoint compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
War Room Checkpoint this skillathola/claude-night-market341—~2.6kAutomated safety check: PassMIT
Vercel Composition Patternssupabase/supabase111k58 repos~726Automated safety check: PassMIT
Finishing a Development Branchobra/superpowers297k5 repos~1.9kAutomated safety check: PassMIT
Typescript Advanced Typesrolling-scopes/rsschool-app10k25 repos~4.2kAutomated safety check: PassMPL-2.0
PR Babysitteropeninterpreter/openinterpreter69k3 repos~4.2kAutomated safety check: PassApache-2.0
Code Review ChecklistshareAI-lab/learn-claude-code78k4 repos~1.1kAutomated safety check: PassMIT

Similar skills

  • Vercel Composition Patterns

    supabase/supabase

    Official

    React composition patterns that scale. An agent skill from supabase/supabase.

    111k GitHub starsUsed in 58 repos~726 tokens
    DevelopmentAuto-check passed

  • Finishing a Development Branch

    obra/superpowers

    Walks the last step of a branch: confirm tests pass, detect the git environment, ask how to integrate, carry out your choice and clean up the worktree.

    297k GitHub starsUsed in 5 repos~1.9k tokens
    DevelopmentAuto-check passed

  • Typescript Advanced Types

    rolling-scopes/rsschool-app

    Master TypeScript's advanced type system including generics, conditional types, mapped types, template literals, and utility types for building type-safe applications.

    10k GitHub starsUsed in 25 repos~4.2k tokens
    DevelopmentAuto-check passed

  • PR Babysitter

    openinterpreter/openinterpreter

    Watches an open GitHub pull request until it merges, handling review comments, diagnosing CI failures and retrying flaky checks along the way.

    69k GitHub starsUsed in 3 repos~4.2k tokens
    DevelopmentAuto-check passed

  • Code Review Checklist

    shareAI-lab/learn-claude-code

    Reviews code against a five-part checklist covering security, correctness, performance, maintainability and testing, and reports findings in a fixed format.

    78k GitHub starsUsed in 4 repos~1.1k tokens
    DevelopmentAuto-check passed

  • Greploop

    onyx-dot-app/onyx

    Iteratively improves a PR (GitHub), MR (GitLab), or shelved changelist (Perforce) until Greptile gives it a 5/5 confidence score with zero unresolved comments.

    32k GitHub starsUsed in 4 repos~3.3k tokens
    DevelopmentAuto-check passed

More from athola/claude-night-market

All 152 skills in this repo

  • Night Market Diagnostics Toolkit

    athola/claude-night-market

    Run and interpret repo diagnostic scripts (ratchets, validators, token stats).

    341 GitHub stars~3.4k tokensUpdated yesterday
    Auto-check passed

  • Skills Eval

    athola/claude-night-market

    Evaluate Claude skill quality through auditing. An agent skill from athola/claude-night-market.

    341 GitHub stars~1.6k tokensUpdated yesterday
    Auto-check passed

  • Agent Teams

    athola/claude-night-market

    Coordinates Claude agent teams via filesystem protocol. An agent skill from athola/claude-night-market.

    341 GitHub stars~2.5k tokensUpdated yesterday
    Auto-check passed

  • Delegation Core

    athola/claude-night-market

    Delegates execution to eight CLIs (Gemini, Qwen, MiniMax, GLM, Muse, Codex, OpenCode, Glimmer).

    341 GitHub stars~2.5k tokensUpdated yesterday
    Auto-check passed

  • Elegant Code

    athola/claude-night-market

    Guide minimal code via a decision ladder with full safety, edge, and negative-case coverage.

    341 GitHub stars~2.1k tokensUpdated yesterday
    Auto-check passed

  • Skill Library Mission

    athola/claude-night-market

    Build a project skill library in .claude/skills/ via discovery, parallel authoring, and review.

    341 GitHub stars~1.6k tokensUpdated yesterday
    Auto-check passed

Categories

Questions about War Room Checkpoint

What does War Room Checkpoint do?

Assesses decision reversibility and risk at critical checkpoints. War Room Checkpoint is an agent skill from athola/claude-night-market. Assesses decision reversibility and risk at critical checkpoints.

When should I use War Room Checkpoint?

War Room Checkpoint fits situations like: A workflow reaches a high-stakes branch needing escalation check.

How do I install War Room Checkpoint in Claude Code?

Run `npx skills add athola/claude-night-market --skill war-room-checkpoint -a claude-code`. Or copy the skill folder (plugins/attune/skills/war-room-checkpoint in athola/claude-night-market) into .claude/skills/war-room-checkpoint in your project. Claude Code loads it when a task matches its description.

How do I install War Room Checkpoint in Codex?

Run `npx skills add athola/claude-night-market --skill war-room-checkpoint -a codex`. Or copy the skill folder (plugins/attune/skills/war-room-checkpoint in athola/claude-night-market) into .agents/skills/war-room-checkpoint in your project. Codex loads it when a task matches its description.

Can I use War Room Checkpoint in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add athola/claude-night-market --skill war-room-checkpoint -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/war-room-checkpoint, .gemini/skills/war-room-checkpoint, .github/skills/war-room-checkpoint and .opencode/skills/war-room-checkpoint in your project.

What does War Room Checkpoint need to run?

Going by SKILL.md and its folder, War Room Checkpoint needs the command-line tools its instructions call (make). Our summary lists: Python 3.

Does War Room Checkpoint access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is War Room Checkpoint safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does War Room Checkpoint use?

War Room Checkpoint is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does War Room Checkpoint use?

About 2.6k tokens (SKILL.md is roughly 10k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to War Room Checkpoint?

Skills that share tags, products or a category with War Room Checkpoint: Vercel Composition Patterns (supabase/supabase, 111k stars), Finishing a Development Branch (obra/superpowers, 297k stars), Typescript Advanced Types (rolling-scopes/rsschool-app, 10k stars) and PR Babysitter (openinterpreter/openinterpreter, 69k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains War Room Checkpoint?

athola (a GitHub user) maintains it in athola/claude-night-market, which has 341 GitHub stars. The repository holds 152 skills in this directory. The repository was last updated on October 9, 2026.

Source: athola/claude-night-market on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.