judgment-day

judgment-day is a skill for Claude Code, Codex from upex-galaxy/agentic-qa-boilerplate. It costs 33 tokens per session (1,241 once invoked), scanned A, original, MIT.

A deliberately adversarial review workflow that runs two independent reviews, fixes confirmed problems, and then reviews the result again. It is meant for a specific target such as files, a feature, a pull request, or part of an architecture.

In plain words
What is it for?
Use it for high-risk code, test, framework, Git, or architecture changes when you want a blind dual review and a final re-check of the fixes.
Why use it?
A second, independent review can expose issues that a normal review misses. The workflow also prevents it from running automatically when the user has not asked for this stricter check.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: installed under .agents/ (shared by several agents); mentions AGENTS.md.

Good fit Use it for high-risk code, test, framework, Git, or architecture changes when you want a blind dual review and a final re-check of the fixes.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/upex-galaxy/agentic-qa-boilerplate/judgment-day
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add upex-galaxy/agentic-qa-boilerplate --skill judgment-day
Clone the repo
git clone --depth 1 https://github.com/upex-galaxy/agentic-qa-boilerplate

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for judgment-day

README.md
[![agentmods](https://agentmods.dev/badge/skills/upex-galaxy/agentic-qa-boilerplate/judgment-day/github.svg)](https://agentmods.dev/skills/upex-galaxy/agentic-qa-boilerplate/judgment-day)
Your own site
<a href="https://agentmods.dev/skills/upex-galaxy/agentic-qa-boilerplate/judgment-day"><img src="https://agentmods.dev/badge/skills/upex-galaxy/agentic-qa-boilerplate/judgment-day/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for judgment-day

Your own site · 80×15
<a href="https://agentmods.dev/skills/upex-galaxy/agentic-qa-boilerplate/judgment-day"><img src="https://agentmods.dev/badge/skills/upex-galaxy/agentic-qa-boilerplate/judgment-day.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 33 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,241 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00033 $0.01241
Opus 5 $0.00016 $0.00620
Sonnet 5 $0.00007 $0.00248
Haiku 4.5 $0.00003 $0.00124

Measured 10d ago against content hash 42e1c9e6d05b, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

judgment-day scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.agents/skills/judgment-day/SKILL.md · 82 lines

How it starts

The opening of the file, as written. The whole thing — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Activation Contract

Load this skill only when the user explicitly asks for Judgment Day, dual/adversarial review, or equivalent Spanish trigger (juzgar, que lo juzguen). Review a specific target: files, feature, PR, or architecture slice.

Within this QA boilerplate, this skill is cited as an OPTIONAL adversarial augmentation by:

  • /test-automation — Review phase for high-risk test changes
  • /git-flow-master — pre-PR gate when the diff is large or touches shared fixtures / base classes
  • /framework-development — pre-archive review of framework evolution diffs

It is never invoked automatically. Users opt in via explicit trigger.

Inputs — read these first, in this order

Canonical reading order for any AI starting cold on a Judgment Day review. Read in order; stop earlier when the target is small enough that later inputs add no signal.

  1. The diff / files / PR / architecture slice under review — the literal target the user named.
  2. AGENTS.md — repo conventions, Critical Rules, behavioral layer (the judges must score against these, not generic best-practice).
  3. .agents/skills/REGISTRY.md — skill registry; resolve which project skills apply to the target's file paths + task type, and inject the same Skills to load before work block into both judge prompts.
  4. The change's spec / PR description / Jira ticket — the stated intent. Judges score against intent, not their imagined intent.
  5. references/prompts-and-formats.md — judge prompts, fix prompts, warning rubric, verdict table format.
  6. Prior judge outputs from earlier rounds (Round 2+ only) — to detect regressions or stale findings vs. new ones.

Hard Rules

  • Resolve project skills before launching agents: read skill registry, match skill paths by target files/task, and inject the same Skills to load before work block into both judge prompts and fix prompts.
  • Launch two blind judges in parallel with identical target and criteria; never review the code yourself.
  • Wait for both judges before synthesis; never accept a partial verdict.
  • Classify warnings as WARNING (real) only if normal intended use can trigger them; otherwise downgrade to INFO as WARNING (theoretical).
  • Ask before fixing Round 1 confirmed issues.
  • After any fix agent runs, immediately re-launch both judges in parallel before commit/push/done/session summary.
  • Terminal states are only JUDGMENT: APPROVED or JUDGMENT: ESCALATED.
  • After 2 fix iterations with remaining issues, ask the user whether to continue.

Read the full file on GitHub · 82 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 10d ago First seen · 82 lines · 33 tokens per session scan A 42e1c9e6d05b

Subscribe to this mod's changes

judgment-day is a skill published in the GitHub repository upex-galaxy/agentic-qa-boilerplate (21 stars, last pushed 4d ago), licensed MIT. It adds 33 tokens to every session and 1,241 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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