Phishnoo Sandbox
Explore synthetic security awareness metrics and file example threat reports.
Description from the maker (original English)
45 judges that evaluate AI-generated code for security, cost, and quality with built-in AST.
These three are never merged into one score: opinions, measurements and popularity are different things.
We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.
Pick the AI app you use, then copy the settings.
You need
Which app do you use?
Pick your app above. We remember it, so next time the button says “Install in …”.
Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json
{
"mcpServers": {
"judges": {
"command": "npx",
"args": [
"-y",
"@kevinrabun/judges"
]
}
}
}Run this in your terminal
claude mcp add judges -- npx -y @kevinrabun/judgesPaste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"judges": {
"command": "npx",
"args": [
"-y",
"@kevinrabun/judges"
]
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
Paste into .vscode/mcp.json in your project
{
"servers": {
"judges": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@kevinrabun/judges"
]
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.
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These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.
Runs on your computer; the tool list appears in your AI app after you install it.
We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 8, 2026
The registry name was verified with the GitHub account KevinRabun, which owns the repo
We have not read this repo from GitHub yet
We have not read this repo from GitHub yet
This one runs on your computer; there is no internet endpoint
Runs on your computer and uses local settings instead
Runs on your computer; we can only read tool lists from internet endpoints
Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.
It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.
The registry does not list a required key, though some servers ask you to sign in on first use.
It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).
The GitHub repo last changed –, and the latest version (3.129.9) reached the registry 7 months ago.
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.
In the same category: Phishnoo Sandbox, Zovo Dependency Audit, TeamOnly
Servers in the same category from other makers, sorted by stars.
Reading a card:↓ Downloads per weeki★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
Explore synthetic security awareness metrics and file example threat reports.
Audit a dependency list for known vulnerabilities, licenses and outdated versions.
Deploy AI-built internal apps behind your company login, with a database, files and secrets.
Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.
An MCP (Model Context Protocol) server that provides a panel of 45 specialized judges to evaluate AI-generated code — acting as an independent quality gate regardless of which project is being reviewed. Combines deterministic pattern matching & AST analysis (instant, offline, zero LLM calls) with LLM-powered deep-review prompts that let your AI assistant perform expert-persona analysis across all 45 domains.
Highlights:
src/patches/index.ts) plus LLM-powered deep review.🧪 Many commands in
printHelpare experimental/roadmap. By default, we show GA commands only. SetJUDGES_SHOW_EXPERIMENTAL=1to reveal stubs; these may not be wired yet.
🔰 Packages
- CLI:
@kevinrabun/judges-cli→ binaryjudges(usenpx @kevinrabun/judges-cli eval --file app.ts).
- MCP/API:
@kevinrabun/judges→ programmatic API + MCP server (npm install @kevinrabun/judges).
- VS Code extension: see
vscode-extension/.
- GitHub Action:
uses: KevinRabun/judges@main(see CI quickstart).
# Using the CLI package (recommended)
npx @kevinrabun/judges-cli eval --file src/app.ts
npx @kevinrabun/judges-cli --help
echo "JUDGES_SHOW_EXPERIMENTAL=1" >> $GITHUB_ENV npx @kevinrabun/judges-cli --help
npx @kevinrabun/judges-cli license-scan --dir .
CLI vs API: If you want to embed Judges in your app (MCP/API), install
@kevinrabun/judges. For the command-line, use@kevinrabun/judges-cli(binaryjudges).
name: Judges
on: [pull_request, push] jobs: judges: runs-on: ubuntu-latest steps:
npm install @kevinrabun/judges
import { evaluateCode } from "@kevinrabun/judges/api";
const verdict = evaluateCode("const password = 'ProdSecret';", "typescript"); console.log(verdict.overallVerdict, verdict.overallScore);
The MCP server runs on stdio and is started by your MCP client (VS Code, Claude Desktop, etc.). Configure it in your MCP settings (e.g. mcp.json):
{
"servers": { "judges": { "type": "stdio", "command": "npx", "args": ["-y", "@kevinrabun/judges"] } } }
Or run the server directly:
npx @kevinrabun/judges
Config file:
.judgesrc.json(supports${ENV_VAR}substitution viaexpandEnvPlaceholders). See Configuration.
AI code generators (Copilot, Cursor, Claude, ChatGPT, etc.) write code fast — but they routinely produce insecure defaults, missing auth, hardcoded secrets, and poor error handling. Human reviewers catch some of this, but nobody reviews 45 dimensions consistently.
| ESLint / Biome | SonarQube | Semgrep / CodeQL | Judges | |
|---|---|---|---|---|
| Scope | Style + some bugs | Bugs + code smells | Security patterns | 45 domains: security, cost, compliance, a11y, API design, cloud, UX, … |
| AI-generated code focus | No | No | Partial | Purpose-built for AI output failure modes |
| Setup | Config per project | Server + scanner | Cloud or local | One command: npx @kevinrabun/judges-cli eval file.ts |
| Auto-fix patches | Some | No | No | 200+ deterministic patches — instant, offline |
| Non-technical output | No | Dashboard | No | Plain-language findings with What/Why/Next |
| MCP native | No | No | No | Yes — works inside Copilot, Claude, Cursor |
| SARIF output | No | Yes | Yes | Yes — upload to GitHub Code Scanning |
| Cost | Free | $$$$ | Free/paid | Free / MIT |
Judges doesn't replace linters — it covers the dimensions linters don't: authentication strategy, data sovereignty, cost patterns, accessibility, framework-specific anti-patterns, and architectural issues across multiple files.
Prereqs: Node.js >=18 (>=20 recommended),
npxavailable. ThejudgesCLI binary ships with @kevinrabun/judges-cli (preferred) and also works vianpx @kevinrabun/judges.
Packages:
- CLI:
npm install -g @kevinrabun/judges-cli(ornpx @kevinrabun/judges-cli ...)
- MCP/API:
npm install @kevinrabun/judges
Use @kevinrabun/judges for the MCP server and programmatic API. Use @kevinrabun/judges-cli when you want the judges terminal command.
# Install the CLI globally
npm install -g @kevinrabun/judges-cli
judges eval src/app.ts
cat api.py | judges eval --language python
judges eval --judge cybersecurity server.ts
judges eval --file app.ts --format sarif > results.sarif
judges eval --file app.ts --format html > report.html
judges eval --fail-on-findings src/api.ts
judges eval --baseline baseline.json src/api.ts
judges eval --preset security-only src/api.ts
judges eval --config .judgesrc.json src/api.ts
judges eval --min-score 80 src/api.ts
judges eval --summary src/api.ts
judges skill ai-code-review --file src/app.ts judges skill security-review --file src/api.ts --format json judges skill release-gate --file src/app.ts judges skills # list available skills
Full catalog:
docs/skills.md
judges list
# Interactive project setup wizard
judges init
judges fix src/app.ts
judges fix src/app.ts --apply
judges license-scan --format json --risk high
judges watch src/
judges report . --format html --output report.html
git diff HEAD~1 | judges diff
judges deps --path . --format json
judges app serve --port 4567
judges review --pr 123 --repo owner/name --diff-only
judges tune --dir . --apply
judges baseline create --file src/api.ts -o baseline.json
judges ci-templates --provider github judges ci-templates --provider gitlab judges ci-templates --provider azure judges ci-templates --provider bitbucket
judges docs judges docs --judge cybersecurity judges docs --output docs/
judges completions bash # eval "$(judges completions bash)" judges completions zsh judges completions fish judges completions powershell
judges hook install
judges hook uninstall
🔎 Tip: The CLI help now defaults to GA commands only. To see experimental/roadmap commands, run:
`bash
JUDGES_SHOW_EXPERIMENTAL=1 judges --help
`
Run a zero-config PR reviewer as a GitHub App:
# Run the webhook server locally
judges app serve --port 4567
Required env vars:
JUDGES_APP_ID – GitHub App IDJUDGES_PRIVATE_KEY or JUDGES_PRIVATE_KEY_PATH – PEM private keyJUDGES_WEBHOOK_SECRET – signature verification secretOptional:
JUDGES_MIN_SEVERITY (default: medium)JUDGES_MAX_COMMENTS (default: 25)JUDGES_TEST_DRY_RUN=1 to avoid live network calls during testsFor local testing, you can expose http://localhost:4567/webhook via ngrok http 4567 and configure the GitHub App webhook URL accordingly.
Add Judges to your CI pipeline with zero configuration:
# .github/workflows/judges.yml
name: Judges Code Review on: [pull_request]
jobs: judges: runs-on: ubuntu-latest permissions: contents: read security-events: write # only if using upload-sarif steps:
Outputs available for downstream steps: verdict, score, findings, critical, high, sarif-file.
# Build the image
docker build -t judges .
docker run --rm -v $(pwd):/code judges eval --file /code/app.ts
cat api.py | docker run --rm -i judges eval --language python
docker run --rm judges list
git clone https://github.com/KevinRabun/judges.git
cd judges npm install npm run build
Run the included demo to see all 45 judges evaluate a purposely flawed API server:
npm run demo
This evaluates examples/sample-vulnerable-api.ts — a file intentionally packed with security holes, performance anti-patterns, and code quality issues — and prints a full verdict with per-judge scores and findings.
The demo now also includes an App Builder Workflow (3-step) section. In a single run, you get both tribunal output and workflow output:
Ship now / Ship with caution / Do not ship)P0/P1 itemsSample workflow output (truncated):
╔══════════════════════════════════════════════════════════════╗
║ App Builder Workflow Demo (3-Step) ║ ╚══════════════════════════════════════════════════════════════╝
Decision : Do not ship Verdict : FAIL (47/100) Risk Counts : Critical
Only the beginning is shown; read the rest in the repo