Judges Panel

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:

Includes an App Builder Workflow (3-step) demo for release decisions, plain-language risk summaries, and prioritized fixes — see Try the Demo.
Includes V2 context-aware evaluation with policy profiles, evidence calibration, specialty feedback, confidence scoring, and uncertainty reporting.
Includes public repository URL reporting to clone a repo, run the full tribunal, and output a consolidated markdown report.
200+ deterministic auto-fix patches (see src/patches/index.ts) plus LLM-powered deep review.

🧪 Many commands in printHelp are experimental/roadmap. By default, we show GA commands only. Set JUDGES_SHOW_EXPERIMENTAL=1 to reveal stubs; these may not be wired yet.

🔰 Packages

CLI: @kevinrabun/judges-cli → binary judges (use npx @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).

Quickstart

CLI (one-off)

bash

# Using the CLI package (recommended)
npx @kevinrabun/judges-cli eval --file src/app.ts

# Show GA commands only (default)
npx @kevinrabun/judges-cli --help

# Show experimental/roadmap commands
echo "JUDGES_SHOW_EXPERIMENTAL=1" >> $GITHUB_ENV
npx @kevinrabun/judges-cli --help

# License scan (supply-chain & license compliance)
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 (binary judges).

GitHub Action

yaml

name: Judges
on: [pull_request, push]
jobs:
  judges:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: KevinRabun/judges@main
        with:
          path: .
          diff-only: true           # evaluate only changed lines in PRs (default true)
          fail-on-findings: true    # fail on critical/high findings
          upload-sarif: true        # upload SARIF to GitHub Code Scanning

Programmatic API (MCP server included)

Terminal

npm install @kevinrabun/judges

server.ts

import { evaluateCode } from "@kevinrabun/judges/api";
const verdict = evaluateCode("const password = 'ProdSecret';", "typescript");
console.log(verdict.overallVerdict, verdict.overallScore);

MCP server

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):

config.json

{
  "servers": {
    "judges": {
      "type": "stdio",
      "command": "npx",
      "args": ["-y", "@kevinrabun/judges"]
    }
  }
}

Or run the server directly:

Terminal

npx @kevinrabun/judges
# Starts the MCP server on stdio

Config file: .judgesrc.json (supports ${ENV_VAR} substitution via expandEnvPlaceholders). See Configuration.

Why Judges?

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.

Judges — Terminal Output

Quick Start

Prereqs: Node.js >=18 (>=20 recommended), npx available. The judges CLI binary ships with @kevinrabun/judges-cli (preferred) and also works via npx @kevinrabun/judges.

Packages:

CLI: npm install -g @kevinrabun/judges-cli (or npx @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.

Try it now (no clone needed)

bash

# Install the CLI globally
npm install -g @kevinrabun/judges-cli

# Evaluate any file
judges eval src/app.ts

# Pipe from stdin
cat api.py | judges eval --language python

# Single judge
judges eval --judge cybersecurity server.ts

# SARIF output for CI
judges eval --file app.ts --format sarif > results.sarif

# HTML report with severity filters and dark/light theme
judges eval --file app.ts --format html > report.html

# Fail CI on findings (exit code 1)
judges eval --fail-on-findings src/api.ts

# Suppress known findings via baseline
judges eval --baseline baseline.json src/api.ts

# Use a named preset
judges eval --preset security-only src/api.ts

# Use a config file
judges eval --config .judgesrc.json src/api.ts

# Set a minimum score threshold (exit 1 if below)
judges eval --min-score 80 src/api.ts

# One-line summary for scripts
judges eval --summary src/api.ts

# Agentic skills (orchestrated judge sets)
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`](docs/skills.md)


# List all 45 judges
judges list

Additional CLI Commands

bash

# Interactive project setup wizard
judges init

# Preview auto-fix patches (dry run)
judges fix src/app.ts

# Apply patches directly
judges fix src/app.ts --apply

# License compliance scan (copyleft/unknown detection)
judges license-scan --format json --risk high

# Watch mode — re-evaluate on file save
judges watch src/

# Project-level report (local directory)
judges report . --format html --output report.html

# Evaluate a unified diff (pipe from git diff)
git diff HEAD~1 | judges diff

# Analyze dependencies for supply-chain risks
judges deps --path . --format json

# Run GitHub App server (zero-config PR reviews)
judges app serve --port 4567

# Run GitHub PR review (gh CLI required)
judges review --pr 123 --repo owner/name --diff-only

# Auto-tune presets and configs
judges tune --dir . --apply

# Create a baseline file to suppress known findings
judges baseline create --file src/api.ts -o baseline.json

# Generate CI template files
judges ci-templates --provider github
judges ci-templates --provider gitlab
judges ci-templates --provider azure
judges ci-templates --provider bitbucket

# Generate per-judge rule documentation
judges docs
judges docs --judge cybersecurity
judges docs --output docs/

# Install shell completions
judges completions bash   # eval "$(judges completions bash)"
judges completions zsh
judges completions fish
judges completions powershell

# Install pre-commit hook
judges hook install

# Uninstall pre-commit hook
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

GitHub App (self-hosted webhook)

Run a zero-config PR reviewer as a GitHub App:

bash

# Run the webhook server locally
judges app serve --port 4567

Required env vars:

JUDGES_APP_ID – GitHub App ID
JUDGES_PRIVATE_KEY or JUDGES_PRIVATE_KEY_PATH – PEM private key
JUDGES_WEBHOOK_SECRET – signature verification secret

Optional:

JUDGES_MIN_SEVERITY (default: medium)
JUDGES_MAX_COMMENTS (default: 25)
JUDGES_TEST_DRY_RUN=1 to avoid live network calls during tests

For local testing, you can expose http://localhost:4567/webhook via ngrok http 4567 and configure the GitHub App webhook URL accordingly.

Use in GitHub Actions

Add Judges to your CI pipeline with zero configuration:

yaml

# .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:
      - uses: actions/checkout@v4
      - uses: KevinRabun/judges@main
        with:
          path: src/api.ts        # file or directory
          format: text             # text | json | sarif | markdown
          upload-sarif: true       # upload to GitHub Code Scanning
          fail-on-findings: true   # fail CI on critical/high findings

Outputs available for downstream steps: verdict, score, findings, critical, high, sarif-file.

Use with Docker (no Node.js required)

bash

# Build the image
docker build -t judges .

# Evaluate a local file
docker run --rm -v $(pwd):/code judges eval --file /code/app.ts

# Pipe from stdin
cat api.py | docker run --rm -i judges eval --language python

# List judges
docker run --rm judges list

Or use as an MCP server

1. Install and Build

bash

git clone https://github.com/KevinRabun/judges.git
cd judges
npm install
npm run build

2. Try the Demo

Run the included demo to see all 45 judges evaluate a purposely flawed API server:

Terminal

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:

Release decision (Ship now / Ship with caution / Do not ship)
Plain-language summaries of top risks
Prioritized remediation tasks and AI-fixable P0/P1 items

Sample workflow output (truncated):

text

╔══════════════════════════════════════════════════════════════╗
║             App Builder Workflow Demo (3-Step)             ║
╚══════════════════════════════════════════════════════════════╝

  Decision       : Do not ship
  Verdict        : FAIL (47/100)
  Risk Counts    : Critical 24 | High 27 | Medium 55

  Step 2 — Plain-Language Findings:
  - [CRITICAL] DATA-001: Hardcoded password detected
      What: ...
      Why : ...
      Next: ...

  Step 3 — Prioritized Tasks:
  - P0 | DEVELOPER | Effort L | DATA-001
      Task: ...
      Done: ...

  AI-Fixable Now (P0/P1):
  - P0 DATA-001: ...

Sample tribunal output (truncated):

Code

╔══════════════════════════════════════════════════════════════╗
║           Judges Panel — Full Tribunal Demo                 ║
╚══════════════════════════════════════════════════════════════╝

  Overall Verdict : FAIL
  Overall Score   : 43/100
  Critical Issues : 15
  High Issues     : 17
  Total Findings  : 83
  Judges Run      : 33

  Per-Judge Breakdown:
  ────────────────────────────────────────────────────────────────
  ❌ Judge Data Security              0/100    7 finding(s)
  ❌ Judge Cybersecurity              0/100    7 finding(s)
  ❌ Judge Cost Effectiveness        52/100    5 finding(s)
  ⚠️  Judge Scalability              65/100    4 finding(s)
  ❌ Judge Cloud Readiness           61/100    4 finding(s)
  ❌ Judge Software Practices        45/100    6 finding(s)
  ❌ Judge Accessibility              0/100    8 finding(s)
  ❌ Judge API Design                 0/100    9 finding(s)
  ❌ Judge Reliability               54/100    3 finding(s)
  ❌ Judge Observability             45/100    5 finding(s)
  ❌ Judge Performance               27/100    5 finding(s)
  ❌ Judge Compliance                 0/100    4 finding(s)
  ⚠️  Judge Testing                  90/100    1 finding(s)
  ⚠️  Judge Documentation            70/100    4 finding(s)
  ⚠️  Judge Internationalization     65/100    4 finding(s)
  ⚠️  Judge Dependency Health        90/100    1 finding(s)
  ❌ Judge Concurrency               44/100    4 finding(s)
  ❌ Judge Ethics & Bias             65/100    2 finding(s)
  ❌ Judge Maintainability           52/100    4 finding(s)
  ❌ Judge Error Handling            27/100    3 finding(s)
  ❌ Judge Authentication             0/100    4 finding(s)
  ❌ Judge Database                   0/100    5 finding(s)
  ❌ Judge Caching                   62/100    3 finding(s)
  ❌ Judge Configuration Mgmt         0/100    3 finding(s)
  ⚠️  Judge Backwards Compat         80/100    2 finding(s)
  ⚠️  Judge Portability              72/100    2 finding(s)
  ❌ Judge UX                        52/100    4 finding(s)
  ❌ Judge Logging Privacy            0/100    4 finding(s)
  ❌ Judge Rate Limiting             27/100    4 finding(s)
  ⚠️  Judge CI/CD                    80/100    2 finding(s)

3. Run the Tests

Terminal

npm test

Runs automated tests covering all judges, AST parsers, markdown formatters, and edge cases.

4. Connect to Your Editor

VS Code (recommended — zero config)

Install the Judges Panel extension from the Marketplace. It provides:

Inline diagnostics & quick-fixes on every file save
@judges chat participant — type @judges in Copilot Chat, or just ask for a "judges panel review" and Copilot routes automatically
Auto-configured MCP server — all 45 expert-persona prompts available to Copilot with zero setup

bash

code --install-extension kevinrabun.judges-panel

VS Code — manual MCP config

If you prefer explicit workspace config (or want teammates without the extension to benefit), create .vscode/mcp.json:

config.json

{
  "servers": {
    "judges": {
      "command": "npx",
      "args": ["-y", "@kevinrabun/judges"]
    }
  }
}

Claude Desktop

Add to claude_desktop_config.json:

config.json

{
  "mcpServers": {
    "judges": {
      "command": "npx",
      "args": ["-y", "@kevinrabun/judges"]
    }
  }
}

Cursor / other MCP clients

Use the same npx command for any MCP-compatible client:

config.json

{
  "command": "npx",
  "args": ["-y", "@kevinrabun/judges"]
}

5. Use Judges in GitHub Copilot PR Reviews

Yes — users can include Judges as part of GitHub-based review workflows, with one important caveat:

The hosted copilot-pull-request-reviewer on GitHub does not currently let you directly attach arbitrary local MCP servers the same way VS Code does.
The practical pattern is to run Judges in CI on each PR, publish a report/check, and have Copilot + human reviewers use that output during review.

Option A (recommended): PR workflow check + report artifact

Create .github/workflows/judges-pr-review.yml:

server.ts

name: Judges PR Review

on:
  pull_request:
    types: [opened, synchronize, reopened]

jobs:
  judges:
    runs-on: ubuntu-latest
    permissions:
      contents: read
      pull-requests: write

    steps:
      - name: Checkout
        uses: actions/checkout@v4

      - name: Setup Node
        uses: actions/setup-node@v4
        with:
          node-version: 20
          cache: npm

      - name: Install
        run: npm ci

      - name: Generate Judges report
        run: |
          npx tsx -e "import { generateRepoReportFromLocalPath } from './src/reports/public-repo-report.ts';
          const result = generateRepoReportFromLocalPath({
            repoPath: process.cwd(),
            outputPath: 'judges-pr-report.md',
            maxFiles: 600,
            maxFindingsInReport: 150,
          });
          console.log('Overall:', result.overallVerdict, result.averageScore);"

      - name: Upload report artifact
        uses: actions/upload-artifact@v4
        with:
          name: judges-pr-report
          path: judges-pr-report.md

This gives every PR a reproducible Judges output your team (and Copilot) can reference.

Option B: Add Copilot custom instructions in-repo

Add .github/instructions/judges.instructions.md with guidance such as:

markdown

When reviewing pull requests:
1. Read the latest Judges report artifact/check output first.
2. Prioritize CRITICAL and HIGH findings in remediation guidance.
3. If findings conflict, defer to security/compliance-related Judges.
4. Include rule IDs (e.g., DATA-001, CYBER-004) in suggested fixes.

This helps keep Copilot feedback aligned with Judges findings.

CLI Reference

All commands support --help for usage details.

`judges eval`

Evaluate a file with all 45 judges or a single judge.

Flag	Description
`--file <path>` / positional	File to evaluate
`--judge <id>` / `-j <id>`	Single judge mode
`--language <lang>` / `-l <lang>`	Language hint (auto-detected from extension)
`--format <fmt>` / `-f <fmt>`	Output format: `text`, `json`, `sarif`, `markdown`, `html`, `pdf`, `junit`, `codeclimate`, `github-actions`
`--output <path>` / `-o <path>`	Write output to file
`--fail-on-findings`	Exit with code 1 if verdict is FAIL
`--baseline <path>` / `-b <path>`	JSON baseline file — suppress known findings
`--summary`	Print a single summary line (ideal for scripts)
`--config <path>`	Load a `.judgesrc` / `.judgesrc.json` config file
`--preset <name>`	Use a named preset (see Named Presets for all 22 options)
`--min-score <n>`	Exit with code 1 if overall score is below this threshold
`--verbose`	Print timing and debug information
`--quiet`	Suppress non-essential output
`--no-color`	Disable ANSI colors

`judges init`

Interactive wizard that generates project configuration:

.judgesrc.json — rule customization, disabled judges, severity thresholds
.github/workflows/judges.yml — GitHub Actions CI workflow
.gitlab-ci.judges.yml — GitLab CI pipeline (optional)
azure-pipelines.judges.yml — Azure Pipelines (optional)

`judges fix`

Preview or apply auto-fix patches from deterministic findings.

Flag	Description
positional	File to fix
`--apply`	Write patches to disk (default: dry run)
`--judge <id>`	Limit to a single judge's findings

`judges watch`

Continuously re-evaluate files on save.

Flag	Description
positional	File or directory to watch (default: `.`)
`--judge <id>`	Single judge mode
`--fail-on-findings`	Exit non-zero if any evaluation fails

`judges report`

Run a full project-level tribunal on a local directory.

Flag	Description
positional	Directory path (default: `.`)
`--format <fmt>`	Output format: `text`, `json`, `html`, `markdown`
`--output <path>`	Write report to file
`--max-files <n>`	Maximum files to analyze (default: 600)
`--max-file-bytes <n>`	Skip files larger than this (default: 300000)

`judges hook`

Manage a Git pre-commit hook that runs Judges on staged files.

bash

judges hook install    # add pre-commit hook
judges hook uninstall  # remove pre-commit hook

Detects Husky (.husky/pre-commit) and falls back to .git/hooks/pre-commit. Uses marker-based injection so it won't clobber existing hooks.

`judges diff`

Evaluate only the changed lines from a unified diff (e.g., git diff output).

Flag	Description
`--file <path>`	Read diff from file instead of stdin
`--format <fmt>`	Output format: `text`, `json`, `sarif`, `junit`, `codeclimate`
`--output <path>`	Write output to file

bash

git diff HEAD~1 | judges diff
judges diff --file changes.patch --format sarif

`judges deps`

Analyze project dependencies for supply-chain risks.

Flag	Description
`--path <dir>`	Project root to scan (default: `.`)
`--format <fmt>`	Output format: `text`, `json`

bash

judges deps --path .
judges deps --path ./backend --format json

`judges baseline`

Create a baseline file to suppress known findings in future evaluations.

bash

judges baseline create --file src/api.ts
judges baseline create --file src/api.ts -o .judges-baseline.json

`judges ci-templates`

Generate CI/CD configuration templates for popular providers.

bash

judges ci-templates --provider github   # .github/workflows/judges.yml
judges ci-templates --provider gitlab   # .gitlab-ci.judges.yml
judges ci-templates --provider azure    # azure-pipelines.judges.yml
judges ci-templates --provider bitbucket # bitbucket-pipelines.yml (snippet)

`judges docs`

Generate per-judge rule documentation in Markdown.

Flag	Description
`--judge <id>`	Generate docs for a single judge
`--output <dir>`	Write individual `.md` files per judge

bash

judges docs                          # all judges to stdout
judges docs --judge cybersecurity    # single judge
judges docs --output docs/judges/    # write files to directory

`judges completions`

Generate shell completion scripts.

bash

eval "$(judges completions bash)"        # Bash
eval "$(judges completions zsh)"         # Zsh
judges completions fish | source         # Fish
judges completions powershell            # PowerShell (Register-ArgumentCompleter)

Named Presets

Use --preset to apply pre-configured evaluation settings:

Preset	Description
`strict`	All severities, all judges — maximum thoroughness
`lenient`	Only high and critical findings — fast and focused
`security-only`	Security-focused — disables non-security judges (cost, scalability, docs, a11y, i18n, UX, etc.)
`startup`	Skip compliance, sovereignty, i18n judges — move fast
`compliance`	Only compliance, data-sovereignty, authentication — regulatory focus
`performance`	Only performance, scalability, caching, cost-effectiveness
`react`	Tuned for React/Next.js apps — enables accessibility, XSS protection
`express`	Tuned for Express.js APIs — middleware security, auth, CORS, rate limiting
`fastapi`	Tuned for Python FastAPI — input validation, async patterns, API security
`django`	Tuned for Django apps — template security, ORM misuse, CSRF
`spring-boot`	Tuned for Java Spring Boot — injection, configuration, actuator security
`rails`	Tuned for Ruby on Rails — mass assignment, CSRF, SQL injection
`nextjs`	Tuned for Next.js — server/client security, API routes, SSR/ISR
`terraform`	Tuned for Terraform/OpenTofu IaC — infrastructure security, compliance
`kubernetes`	Tuned for K8s manifests — security contexts, RBAC, resource limits
`onboarding`	Smart defaults for first-time adoption — suppresses noisy rules
`fintech`	Financial services — PCI DSS, cryptography, authentication, audit
`healthtech`	Healthcare — HIPAA compliance, data sovereignty, encryption, audit trails
`saas`	Multi-tenant SaaS — tenant isolation, rate limiting, scalability
`government`	Government/public sector — compliance, sovereignty, authentication
`open-source`	Open-source projects — documentation, backwards compatibility, security, dependency health
`ai-review`	AI-generated code review — hallucination detection, security, authentication, correctness

bash

judges eval --preset security-only src/api.ts
judges eval --preset strict --format sarif src/app.ts > results.sarif

CI Output Formats

JUnit XML

Generate JUnit XML for Jenkins, Azure DevOps, GitHub Actions, or GitLab test result viewers:

bash

judges eval --format junit src/api.ts > results.xml

Each judge maps to a <testsuite>, each finding becomes a <testcase> with <failure> for critical/high severity.

CodeClimate / GitLab Code Quality

Generate CodeClimate JSON for GitLab Code Quality or similar tools:

bash

judges eval --format codeclimate src/api.ts > codequality.json

Score Badges

Generate SVG or text badges for your README:

server.ts

import { generateBadgeSvg, generateBadgeText } from "@kevinrabun/judges/badge";

const svg = generateBadgeSvg(85);          // shields.io-style SVG
const text = generateBadgeText(85);        // "✓ judges 85/100"
const svg2 = generateBadgeSvg(75, "quality"); // custom label

The Judge Panel

Judge	Domain	Rule Prefix	What It Evaluates
Data Security	Data Security & Privacy	`DATA-`	Encryption, PII handling, secrets management, access controls
Cybersecurity	Cybersecurity & Threat Defense	`CYBER-`	Injection attacks, XSS, CSRF, auth flaws, OWASP Top 10
Cost Effectiveness	Cost Optimization & Resource Efficiency	`COST-`	Algorithm efficiency, N+1 queries, memory waste, caching strategy
Scalability	Scalability & Performance	`SCALE-`	Statelessness, horizontal scaling, concurrency, bottlenecks
Cloud Readiness	Cloud-Native Architecture & DevOps	`CLOUD-`	12-Factor compliance, containerization, graceful shutdown, IaC
Software Practices	Software Engineering Best Practices & Secure SDLC	`SWDEV-`	SOLID principles, type safety, error handling, input validation
Accessibility	Accessibility (a11y)	`A11Y-`	WCAG compliance, screen reader support, keyboard navigation, ARIA
API Design	API Design & Contracts	`API-`	REST conventions, versioning, pagination, error responses
Reliability	Reliability & Resilience	`REL-`	Error handling, timeouts, retries, circuit breakers
Observability	Monitoring & Diagnostics	`OBS-`	Structured logging, health checks, metrics, tracing
Performance	Runtime Performance	`PERF-`	N+1 queries, sync I/O, caching, memory leaks
Compliance	Regulatory & License Compliance	`COMP-`	GDPR/CCPA, PII protection, consent, data retention, audit trails
Data Sovereignty	Data, Technological & Operational Sovereignty	`SOV-`	Data residency, cross-border transfers, vendor key management, AI model portability, identity federation, circuit breakers, audit trails, data export
Testing	Test Quality & Coverage	`TEST-`	Test coverage, assertions, test isolation, naming
Documentation	Documentation & Developer Experience	`DOC-`	JSDoc/docstrings, magic numbers, TODOs, code comments
Internationalization	i18n & Localization	`I18N-`	Hardcoded strings, locale handling, currency formatting
Dependency Health	Supply Chain & Dependencies	`DEPS-`	Version pinning, deprecated packages, supply chain
Concurrency	Concurrency & Thread Safety	`CONC-`	Race conditions, unbounded parallelism, missing await
Ethics & Bias	AI/ML Fairness & Ethics	`ETHICS-`	Demographic logic, dark patterns, inclusive language
Maintainability	Code Maintainability & Technical Debt	`MAINT-`	Any types, magic numbers, deep nesting, dead code, file length
Error Handling	Error Handling & Fault Tolerance	`ERR-`	Empty catch blocks, missing error handlers, swallowed errors
Authentication	Authentication & Authorization	`AUTH-`	Hardcoded creds, missing auth middleware, token in query params
Database	Database Design & Query Efficiency	`DB-`	SQL injection, N+1 queries, connection pooling, transactions
Caching	Caching Strategy & Data Freshness	`CACHE-`	Unbounded caches, missing TTL, no HTTP cache headers
Configuration Management	Configuration & Secrets Management	`CFG-`	Hardcoded secrets, missing env vars, config validation
Backwards Compatibility	Backwards Compatibility & Versioning	`COMPAT-`	API versioning, breaking changes, response consistency
Portability	Platform Portability & Vendor Independence	`PORTA-`	OS-specific paths, vendor lock-in, hardcoded hosts
UX	User Experience & Interface Quality	`UX-`	Loading states, error messages, pagination, destructive actions
Logging Privacy	Logging Privacy & Data Redaction	`LOGPRIV-`	PII in logs, token logging, structured logging, redaction
Rate Limiting	Rate Limiting & Throttling	`RATE-`	Missing rate limits, unbounded queries, backoff strategy
CI/CD	CI/CD Pipeline & Deployment Safety	`CICD-`	Test infrastructure, lint config, Docker tags, build scripts
Code Structure	Structural Analysis	`STRUCT-`	Cyclomatic complexity, nesting depth, function length, dead code, type safety
Agent Instructions	Agent Instruction Markdown Quality & Safety	`AGENT-`	Instruction hierarchy, conflict detection, unsafe overrides, scope, validation, policy guidance
AI Code Safety	AI-Generated Code Quality & Security	`AICS-`	Prompt injection, insecure LLM output handling, debug defaults, missing validation, unsafe deserialization of AI responses
Framework Safety	Framework-Specific Security & Best Practices	`FW-`	React hooks ordering, Express middleware chains, Next.js SSR/SSG pitfalls, Angular/Vue lifecycle patterns, Django/Flask/FastAPI safety, Spring Boot security, ASP.NET Core auth & CORS, Go Gin/Echo/Fiber patterns
IaC Security	Infrastructure as Code	`IAC-`	Terraform, Bicep, ARM template misconfigurations, hardcoded secrets, missing encryption, overly permissive network/IAM rules
Security	General Security Posture	`SEC-`	Holistic security assessment — insecure data flows, weak cryptography, unsafe deserialization
Hallucination Detection	AI-Hallucinated API & Import Validation	`HALLU-`	Detects hallucinated APIs, fabricated imports, and non-existent modules from AI code generators
Intent Alignment	Code–Comment Alignment & Stub Detection	`INTENT-`	Detects mismatches between stated intent and implementation, placeholder stubs, TODO-only functions
API Contract Conformance	API Design & REST Best Practices	`API-`	API endpoint input validation, REST conformance, request/response contract consistency
Multi-Turn Coherence	Code Coherence & Consistency	`COH-`	Self-contradicting patterns, duplicate definitions, dead code, inconsistent naming
Model Fingerprint Detection	AI Code Provenance & Model Attribution	`MFPR-`	Detects stylistic fingerprints characteristic of specific AI code generators
Over-Engineering	Simplicity & Pragmatism	`OVER-`	Unnecessary abstractions, wrapper-mania, premature generalization, over-complex patterns
Logic Review	Semantic Correctness & Logic Integrity	`LOGIC-`	Inverted conditions, dead code, name-body mismatch, off-by-one, incomplete control flow
False-Positive Review	False Positive Detection & Finding Accuracy	`FPR-`	Meta-judge reviewing pattern-based findings for false positives: string literal context, comment/docstring matches, test scaffolding, IaC template gating

How It Works

The tribunal operates in three layers:

Pattern-Based Analysis — All tools (evaluate_code, evaluate_code_single_judge, evaluate_project, evaluate_diff) perform heuristic analysis using regex pattern matching to catch common anti-patterns. This layer is instant, deterministic, and runs entirely offline with zero external API calls.
AST-Based Structural Analysis — The Code Structure judge (STRUCT-* rules) uses real Abstract Syntax Tree parsing to measure cyclomatic complexity, nesting depth, function length, parameter count, dead code, and type safety with precision that regex cannot achieve. All supported languages — TypeScript, JavaScript, Python, Rust, Go, Java, C#, and C++ — are parsed via tree-sitter WASM grammars (real syntax trees compiled to WebAssembly, in-process, zero native dependencies). A scope-tracking structural parser is kept as a fallback when WASM grammars are unavailable. No external AST server required.
LLM-Powered Deep Analysis (Prompts) — The server exposes MCP prompts (e.g., judge-data-security, judge-cybersecurity) that provide each judge's expert persona as a system prompt. When used by an LLM-based client (Copilot, Claude, Cursor, etc.), the host LLM performs deeper, context-aware probabilistic analysis beyond what static patterns can detect. This is where the systemPrompt on each judge comes alive — Judges itself makes no LLM calls, but it provides the expert criteria so your AI assistant can act as 45 specialized reviewers.

Composable by Design

Judges Panel is a dual-layer review system: instant deterministic tools (offline, no API keys) for pattern and AST analysis, plus 45 expert-persona MCP prompts that unlock LLM-powered deep analysis when connected to an AI client. It does not try to be a CVE scanner or a linter. Those capabilities belong in dedicated MCP servers that an AI agent can orchestrate alongside Judges.

Built-in AST Analysis

Unlike earlier versions that recommended a separate AST MCP server, Judges Panel now includes real AST-based structural analysis out of the box:

TypeScript, JavaScript, Python, Rust, Go, Java, C#, C++ — All parsed with a unified tree-sitter WASM engine for full syntax-tree analysis (functions, complexity, nesting, dead code, type safety). Falls back to a scope-tracking structural parser when WASM grammars are unavailable

The Code Structure judge (STRUCT-*) uses these parsers to accurately measure:

Rule	Metric	Threshold
`STRUCT-001`	Cyclomatic complexity	> 10 per function (high)
`STRUCT-002`	Nesting depth	> 4 levels (medium)
`STRUCT-003`	Function length	> 50 lines (medium)
`STRUCT-004`	Parameter count	> 5 parameters (medium)
`STRUCT-005`	Dead code	Unreachable statements (low)
`STRUCT-006`	Weak types	`any`, `dynamic`, `Object`, `interface{}`, `unsafe` (medium)
`STRUCT-007`	File complexity	> 40 total cyclomatic complexity (high)
`STRUCT-008`	Extreme complexity	> 20 per function (critical)
`STRUCT-009`	Extreme parameters	> 8 parameters (high)
`STRUCT-010`	Extreme function length	> 150 lines (high)

Recommended MCP Stack

When your AI coding assistant connects to multiple MCP servers, each one contributes its specialty:

Code

┌─────────────────────────────────────────────────────────┐
│                   AI Coding Assistant                   │
│              (Claude, Copilot, Cursor, etc.)            │
└──────┬──────────────────┬──────────┬───────────────────┘
       │                  │          │
       ▼                  ▼          ▼
  ┌──────────────┐  ┌────────┐  ┌────────┐
  │   Judges     │  │  CVE / │  │ Linter │
  │   Panel      │  │  SBOM  │  │ Server │
  │ ─────────────│  └────────┘  └────────┘
  │ 44 Heuristic │   Vuln DB     Style &
  │   judges     │   scanning    correctness
  │ + AST judge  │
  └──────────────┘
   Patterns +
   structural
   analysis

Layer	What It Does	Example Servers
Judges Panel	45-judge quality gate — security patterns, AST analysis, cost, scalability, a11y, compliance, sovereignty, ethics, dependency health, agent instruction governance, AI code safety, framework safety	This server
CVE / SBOM	Vulnerability scanning against live databases — known CVEs, license risks, supply chain	OSV, Snyk, Trivy, Grype MCP servers
Linting	Language-specific style and correctness rules	ESLint, Ruff, Clippy MCP servers
Runtime Profiling	Memory, CPU, latency measurement on running code	Custom profiling MCP servers

What This Means in Practice

When you ask your AI assistant "Is this code production-ready?", the agent can:

Judges Panel → Scan for hardcoded secrets, missing error handling, N+1 queries, accessibility gaps, compliance issues, plus analyze cyclomatic complexity, detect dead code, and flag deeply nested functions via AST
CVE Server → Check every dependency in package.json against known vulnerabilities
Linter Server → Enforce team style rules, catch language-specific gotchas

Each server returns structured findings. The AI synthesizes everything into a single, actionable review — no single server needs to do it all.

MCP Tools

`evaluate_v2`

Run a V2 context-aware tribunal evaluation designed to raise feedback quality toward lead engineer/architect-level review:

Policy profile calibration (default, startup, regulated, healthcare, fintech, public-sector)
Context ingestion (architecture notes, constraints, standards, known risks, data-boundary model)
Runtime evidence hooks (tests, coverage, latency, error rate, vulnerability counts)
Specialty feedback aggregation by judge/domain
Confidence scoring and explicit uncertainty reporting

Supports:

Code mode: code + language
Project mode: files[]

Parameter	Type	Required	Description
`code`	string	conditional	Source code for single-file mode
`language`	string	conditional	Programming language for single-file mode
`files`	array	conditional	`{ path, content, language }[]` for project mode
`context`	string	no	High-level review context
`includeAstFindings`	boolean	no	Include AST/code-structure findings (default: true)
`minConfidence`	number	no	Minimum finding confidence to include (0-1, default: 0)
`policyProfile`	enum	no	`default`, `startup`, `regulated`, `healthcare`, `fintech`, `public-sector`
`evaluationContext`	object	no	Structured architecture/constraint context
`evidence`	object	no	Runtime/operational evidence for confidence calibration

`evaluate_app_builder_flow`

Run a 3-step app-builder workflow for technical and non-technical stakeholders:

Tribunal review (code/project/diff)
Plain-language translation of top risks
Prioritized remediation tasks with AI-fixable P0/P1 extraction

Supports:

Code mode: code + language
Project mode: files[]
Diff mode: code + language + changedLines[]

Parameter	Type	Required	Description
`code`	string	conditional	Full source content (code/diff mode)
`language`	string	conditional	Programming language (code/diff mode)
`files`	array	conditional	`{ path, content, language }[]` for project mode
`changedLines`	number[]	no	1-based changed lines for diff mode
`context`	string	no	Optional business/technical context
`maxFindings`	number	no	Max translated top findings (default: 10)
`maxTasks`	number	no	Max generated tasks (default: 20)
`includeAstFindings`	boolean	no	Include AST/code-structure findings (default: true)
`minConfidence`	number	no	Minimum finding confidence to include (0-1, default: 0)

`evaluate_public_repo_report`

Clone a public repository URL, run the full judges panel across eligible source files, and generate a consolidated markdown report.

Parameter	Type	Required	Description
`repoUrl`	string	yes	Public repository URL (`https://...`)
`branch`	string	no	Optional branch name
`outputPath`	string	no	Optional path to write report markdown
`maxFiles`	number	no	Max files analyzed (default: 600)
`maxFileBytes`	number	no	Max file size in bytes (default: 300000)
`maxFindingsInReport`	number	no	Max detailed findings in output (default: 150)
`credentialMode`	string	no	Credential detection mode: `standard` (default) or `strict`
`includeAstFindings`	boolean	no	Include AST/code-structure findings (default: true)
`minConfidence`	number	no	Minimum finding confidence to include (0-1, default: 0)
`enableMustFixGate`	boolean	no	Enable must-fix gate summary for high-confidence dangerous findings (default: false)
`mustFixMinConfidence`	number	no	Confidence threshold for must-fix gate triggers (0-1, default: 0.85)
`mustFixDangerousRulePrefixes`	string[]	no	Optional dangerous rule prefixes for gate matching (e.g., `AUTH`, `CYBER`, `DATA`)
`keepClone`	boolean	no	Keep cloned repo on disk for inspection

Quick examples

Generate a report from CLI:

Terminal

npm run report:public-repo -- --repoUrl https://github.com/microsoft/vscode --output reports/vscode-judges-report.md

# stricter credential-signal mode (optional)
npm run report:public-repo -- --repoUrl https://github.com/openclaw/openclaw --credentialMode strict --output reports/openclaw-judges-report-strict.md

# judge findings only (exclude AST/code-structure findings)
npm run report:public-repo -- --repoUrl https://github.com/openclaw/openclaw --includeAstFindings false --output reports/openclaw-judges-report-no-ast.md

# show only findings at 80%+ confidence
npm run report:public-repo -- --repoUrl https://github.com/openclaw/openclaw --minConfidence 0.8 --output reports/openclaw-judges-report-high-confidence.md

# include must-fix gate summary in the generated report
npm run report:public-repo -- --repoUrl https://github.com/openclaw/openclaw --enableMustFixGate true --mustFixMinConfidence 0.9 --mustFixDangerousPrefix AUTH --mustFixDangerousPrefix CYBER --output reports/openclaw-judges-report-mustfix.md

# opinionated quick-start mode (recommended first run)
npm run report:quickstart -- --repoUrl https://github.com/openclaw/openclaw --output reports/openclaw-quickstart.md

Call from MCP client:

config.json

{
  "tool": "evaluate_public_repo_report",
  "arguments": {
    "repoUrl": "https://github.com/microsoft/vscode",
    "branch": "main",
    "maxFiles": 400,
    "maxFindingsInReport": 120,
    "credentialMode": "strict",
    "includeAstFindings": false,
    "minConfidence": 0.8,
    "enableMustFixGate": true,
    "mustFixMinConfidence": 0.9,
    "mustFixDangerousRulePrefixes": ["AUTH", "CYBER", "DATA"],
    "outputPath": "reports/vscode-judges-report.md"
  }
}

Typical response summary includes:

overall verdict and average score
analyzed file count and total findings
per-judge score table
highest-risk findings and lowest-scoring files

Sample report snippet:

text

# Public Repository Full Judges Report

Generated from https://github.com/microsoft/vscode on 2026-02-21T12:00:00.000Z.

## Executive Summary
- Overall verdict: WARNING
- Average file score: 78/100
- Total findings: 412 (critical 3, high 29, medium 114, low 185, info 81)

`get_judges`

List all available judges with their domains and descriptions.

`evaluate_code`

Submit code to the full judges panel. all 45 judges evaluate independently and return a combined verdict.

Parameter	Type	Required	Description
`code`	string	yes	The source code to evaluate
`language`	string	yes	Programming language (e.g., `typescript`, `python`)
`context`	string	no	Additional context about the code
`includeAstFindings`	boolean	no	Include AST/code-structure findings (default: true)
`minConfidence`	number	no	Minimum finding confidence to include (0-1, default: 0)
`config`	object	no	Inline configuration (see Configuration)

`evaluate_code_single_judge`

Submit code to a specific judge for targeted review.

Parameter	Type	Required	Description
`code`	string	yes	The source code to evaluate
`language`	string	yes	Programming language
`judgeId`	string	yes	See judge IDs below
`context`	string	no	Additional context
`minConfidence`	number	no	Minimum finding confidence to include (0-1, default: 0)
`config`	object	no	Inline configuration (see Configuration)

`evaluate_project`

Submit multiple files for project-level analysis. all 45 judges evaluate each file, plus cross-file architectural analysis detects code duplication, inconsistent error handling, and dependency cycles.

Parameter	Type	Required	Description
`files`	array	yes	Array of `{ path, content, language }` objects
`context`	string	no	Optional project context
`includeAstFindings`	boolean	no	Include AST/code-structure findings (default: true)
`minConfidence`	number	no	Minimum finding confidence to include (0-1, default: 0)
`config`	object	no	Inline configuration (see Configuration)

`evaluate_diff`

Evaluate only the changed lines in a code diff. Runs all 45 judges on the full file but filters findings to lines you specify. Ideal for PR reviews and incremental analysis.

Parameter	Type	Required	Description
`code`	string	yes	The full file content (post-change)
`language`	string	yes	Programming language
`changedLines`	number[]	yes	1-based line numbers that were changed
`context`	string	no	Optional context about the change
`includeAstFindings`	boolean	no	Include AST/code-structure findings (default: true)
`minConfidence`	number	no	Minimum finding confidence to include (0-1, default: 0)
`config`	object	no	Inline configuration (see Configuration)

`analyze_dependencies`

Analyze a dependency manifest file for supply-chain risks, version pinning issues, typosquatting indicators, and dependency hygiene. Supports package.json, requirements.txt, Cargo.toml, go.mod, pom.xml, and .csproj files.

Parameter	Type	Required	Description
`manifest`	string	yes	Contents of the dependency manifest file
`manifestType`	string	yes	File type: `package.json`, `requirements.txt`, etc.
`context`	string	no	Optional context

`evaluate_git_diff`

Evaluate only changed lines from a git diff. Provide either repoPath for a live git diff or diffText for a pre-computed unified diff.

Parameter	Type	Required	Description
`repoPath`	string	conditional	Absolute path to the git repository
`base`	string	no	Git ref to diff against (default: `HEAD~1`)
`diffText`	string	conditional	Pre-computed unified diff text
`confidenceFilter`	number	no	Minimum confidence threshold for findings (0–1)
`autoTune`	boolean	no	Apply feedback-driven auto-tuning (default: false)
`maxPromptChars`	number	no	Max character budget for LLM prompts (default: 100000, 0 = unlimited)
`config`	object	no	Inline configuration

`re_evaluate_with_context`

Re-run the tribunal with prior findings as context for iterative refinement. Supports dispute resolution, developer context injection, and focus-area filtering.

Parameter	Type	Required	Description
`code`	string	yes	Source code to re-evaluate
`language`	string	yes	Programming language
`disputedRuleIds`	string[]	no	Rule IDs the developer disputes as false positives
`acceptedRuleIds`	string[]	no	Rule IDs the developer accepts
`developerContext`	string	no	Free-form explanation of developer intent
`focusAreas`	string[]	no	Specific areas to focus on (e.g., `["security"]`)
`confidenceFilter`	number	no	Minimum confidence threshold (default: 0.5)
`filePath`	string	no	File path for context-aware evaluation
`deepReview`	boolean	no	Include LLM deep-review prompt section
`relatedFiles`	array	no	Cross-file context `{ path, snippet, relationship? }[]`
`maxPromptChars`	number	no	Max character budget for LLM prompts (default: 100000, 0 = unlimited)

Additional MCP Tools

Tool	Description
`evaluate_file`	Read a file from disk and submit it to the full panel. Auto-detects language from extension.
`evaluate_code_streaming`	Streaming evaluation — returns per-judge results as each judge completes with running aggregates.
`evaluate_focused`	Run only specified judges. Use after an initial full evaluation to re-check specific areas.
`evaluate_batch`	Evaluate multiple code files in a single call. Returns per-file verdicts plus aggregate statistics.
`evaluate_then_fix`	Evaluate code and automatically generate fix patches for all findings with auto-fix support.
`evaluate_with_progress`	Evaluate with progress callbacks for long-running evaluations.
`evaluate_policy_aware`	Policy-aware evaluation with named profiles (startup, regulated, healthcare, fintech, public-sector).
`fix_code`	Evaluate code and apply all available auto-fix patches. Returns fixed code with applied/remaining summary.
`explain_finding`	Explain a finding in plain language with OWASP/CWE references, risk context, and remediation guidance.
`triage_finding`	Set triage status of a finding (accepted-risk, deferred, wont-fix, false-positive) with attribution.
`record_feedback`	Record user feedback (true-positive, false-positive, wont-fix) to calibrate confidence scores.
`get_finding_stats`	Finding lifecycle statistics: open, fixed, recurring, and triaged counts plus trends.
`get_suppression_analytics`	Analyze suppression patterns: FP rates by rule, suppression rates, auto-suppress candidates.
`list_triaged_findings`	List triaged findings, optionally filtered by triage status.
`benchmark_gate`	Run benchmarks against quality thresholds. Returns pass/fail with F1, precision, recall metrics.
`run_benchmark`	Run the full benchmark suite with per-judge, per-category, per-difficulty breakdowns.
`scaffold_judge`	Generate boilerplate files to add a new judge: definition, evaluator skeleton, and registration.
`scaffold_plugin`	Generate a starter plugin template with custom rules, judges, and lifecycle hooks.
`session_status`	Current evaluation session state: evaluation count, frameworks, verdict history, stability.
`list_files`	List files and directories in the workspace for project exploration.
`read_file`	Read file contents from the workspace.

Judge IDs

data-security · cybersecurity · security · cost-effectiveness · scalability · cloud-readiness · software-practices · accessibility · api-design · api-contract · reliability · observability · performance · compliance · data-sovereignty · testing · documentation · internationalization · dependency-health · concurrency · ethics-bias · maintainability · error-handling · authentication · database · caching · configuration-management · backwards-compatibility · portability · ux · logging-privacy · rate-limiting · ci-cd · code-structure · agent-instructions · ai-code-safety · framework-safety · iac-security · hallucination-detection · intent-alignment · multi-turn-coherence · model-fingerprint · over-engineering · logic-review · false-positive-review

MCP Prompts

Each judge has a corresponding prompt for LLM-powered deep analysis:

Prompt	Description
`judge-data-security`	Deep data security review
`judge-cybersecurity`	Deep cybersecurity review
`judge-cost-effectiveness`	Deep cost optimization review
`judge-scalability`	Deep scalability review
`judge-cloud-readiness`	Deep cloud readiness review
`judge-software-practices`	Deep software practices review
`judge-accessibility`	Deep accessibility/WCAG review
`judge-api-design`	Deep API design review
`judge-reliability`	Deep reliability & resilience review
`judge-observability`	Deep observability & monitoring review
`judge-performance`	Deep performance optimization review
`judge-compliance`	Deep regulatory compliance review
`judge-data-sovereignty`	Deep data, technological & operational sovereignty review
`judge-testing`	Deep testing quality review
`judge-documentation`	Deep documentation quality review
`judge-internationalization`	Deep i18n review
`judge-dependency-health`	Deep dependency health review
`judge-concurrency`	Deep concurrency & async safety review
`judge-ethics-bias`	Deep ethics & bias review
`judge-maintainability`	Deep maintainability & tech debt review
`judge-error-handling`	Deep error handling review
`judge-authentication`	Deep authentication & authorization review
`judge-database`	Deep database design & query review
`judge-caching`	Deep caching strategy review
`judge-configuration-management`	Deep configuration & secrets review
`judge-backwards-compatibility`	Deep backwards compatibility review
`judge-portability`	Deep platform portability review
`judge-ux`	Deep user experience review
`judge-logging-privacy`	Deep logging privacy review
`judge-rate-limiting`	Deep rate limiting review
`judge-ci-cd`	Deep CI/CD pipeline review
`judge-code-structure`	Deep AST-based structural analysis review
`judge-agent-instructions`	Deep review of agent instruction markdown quality and safety
`judge-ai-code-safety`	Deep review of AI-generated code risks: prompt injection, insecure LLM output handling, debug defaults, missing validation
`judge-framework-safety`	Deep review of framework-specific safety: React hooks, Express middleware, Next.js SSR/SSG, Angular/Vue, Django, Spring Boot, ASP.NET Core, Flask, FastAPI, Go frameworks
`judge-iac-security`	Deep review of infrastructure-as-code security: Terraform, Bicep, ARM template misconfigurations
`judge-security`	Deep holistic security posture review: insecure data flows, weak cryptography, unsafe deserialization
`judge-hallucination-detection`	Deep review of AI-hallucinated APIs, fabricated imports, non-existent modules
`judge-intent-alignment`	Deep review of code–comment alignment, stub detection, placeholder functions
`judge-api-contract`	Deep review of API contract conformance, input validation, REST best practices
`judge-multi-turn-coherence`	Deep review of code coherence: self-contradictions, duplicate definitions, dead code
`judge-model-fingerprint`	Deep review of AI code provenance and model attribution fingerprints
`judge-over-engineering`	Deep review of unnecessary abstractions, wrapper-mania, premature generalization
`judge-logic-review`	Deep review of logic correctness, semantic mismatches, and dead code in AI-generated code
`judge-false-positive-review`	Meta-judge review of pattern-based findings for false positive detection and accuracy

Configuration

Create a .judgesrc.json (or .judgesrc) file in your project root to customize evaluation behavior. See .judgesrc.example.json for a copy-paste-ready template, or reference the JSON Schema for full IDE autocompletion.

config.json

{
  "$schema": "https://github.com/KevinRabun/judges/blob/main/judgesrc.schema.json",
  "preset": "strict",
  "minSeverity": "medium",
  "disabledRules": ["COST-*", "I18N-001"],
  "disabledJudges": ["accessibility", "ethics-bias"],
  "ruleOverrides": {
    "SEC-003": { "severity": "critical" },
    "DOC-*": { "disabled": true }
  },
  "languages": ["typescript", "python"],
  "format": "text",
  "failOnFindings": false,
  "baseline": "",
  "regulatoryScope": ["GDPR", "PCI-DSS", "SOC2"],
  "consensusThreshold": 0.7
}

Field	Type	Default	Description
`$schema`	`string`	—	JSON Schema URL for IDE validation
`preset`	`string`	—	Named preset (see Named Presets for all 22 options)
`minSeverity`	`string`	`"info"`	Minimum severity to report: `critical` · `high` · `medium` · `low` · `info`
`disabledRules`	`string[]`	`[]`	Rule IDs or prefix wildcards to suppress (e.g. `"COST-*"`, `"SEC-003"`)
`disabledJudges`	`string[]`	`[]`	Judge IDs to skip entirely (e.g. `"cost-effectiveness"`)
`ruleOverrides`	`object`	`{}`	Per-rule overrides keyed by rule ID or wildcard — `{ disabled?: boolean, severity?: string }`
`languages`	`string[]`	`[]`	Restrict analysis to specific languages (empty = all)
`format`	`string`	`"text"`	Default output format: `text` · `json` · `sarif` · `markdown` · `html` · `pdf` · `junit` · `codeclimate` · `github-actions`
`failOnFindings`	`boolean`	`false`	Exit code 1 when verdict is `fail` — useful for CI gates
`baseline`	`string`	`""`	Path to a baseline JSON file — matching findings are suppressed
`plugins`	`string[]`	`[]`	Plugin module specifiers (npm packages or relative paths) that export custom judges
`judgeWeights`	`object`	`{}`	Weighted importance per judge for aggregated scoring (e.g. `{ "cybersecurity": 2.0 }`)
`failOnScoreBelow`	`number`	—	Minimum score (0–100) for the run to pass; complements `failOnFindings`
`regulatoryScope`	`string[]`	—	Regulatory frameworks in scope (e.g. `["GDPR", "PCI-DSS"]`). Findings citing ONLY out-of-scope frameworks are suppressed. Run `judges list --frameworks` for supported values.
`consensusThreshold`	`number`	—	Consensus suppression (0–1). If this fraction of judges report zero findings, minority findings are suppressed. Recommended: `0.7` for CI.
`escalationThreshold`	`number`	—	Confidence threshold (0–1) below which findings are flagged for human review
`overrides`	`array`	`[]`	Path-scoped config overrides (e.g. `[{ "files": "*/.test.ts", "disabledJudges": ["documentation"] }]`)
`customRules`	`array`	`[]`	User-defined regex-based rules for business logic validation

All evaluation tools (CLI and MCP) accept the same configuration fields via --config <path> or inline config parameter.

Advanced Features

Inline Suppressions

Suppress specific findings directly in source code using comment directives:

server.ts

const x = eval(input); // judges-ignore SEC-001
// judges-ignore-next-line CYBER-002
const y = dangerousOperation();
// judges-file-ignore DOC-*    ← suppress globally for this file

Supported comment styles: //, #, /* */. Supports comma-separated rule IDs and wildcards (*, SEC-*).

Auto-Fix Patches

Certain findings include machine-applicable patches in the patch field:

Pattern	Auto-Fix
`new Buffer(x)`	→ `Buffer.from(x)`
`http://` URLs (non-localhost)	→ `https://`
`Math.random()`	→ `crypto.randomUUID()`

Patches include oldText, newText, startLine, and endLine for automated application.

Cross-Evaluator Deduplication

When multiple judges flag the same issue (e.g., both Data Security and Cybersecurity detect SQL injection on line 15), findings are automatically deduplicated. The highest-severity finding wins, and the description is annotated with cross-references (e.g., "Also identified by: CYBER-003").

Human Focus Guide

Every tribunal evaluation includes a humanFocusGuide that categorizes findings into three buckets for human reviewers:

Bucket	Description	When to use
✅ Trust	High-confidence (≥80%), evidence-backed findings with AST/taint confirmation	Act directly — these have strong automated evidence
🔍 Verify	Lower-confidence or absence-based findings	Use your judgment — the issue may exist elsewhere in the project
🔦 Blind Spots	Areas automated analysis cannot evaluate	Focus your manual review time here

Blind spots are detected from code characteristics: complex branching logic, external service calls, financial calculations, PII handling, state machines, and complex regex. The guide appears in CLI text/markdown output, JSON/SARIF output, and GitHub Action step summaries.

Regulatory Scope

Configure which regulatory frameworks apply to your project in .judgesrc:

config.json

{ "regulatoryScope": ["GDPR", "PCI-DSS", "SOC2"] }

Findings that cite ONLY out-of-scope frameworks are suppressed. Findings with no regulatory reference (general code quality) are always kept. Run judges list --frameworks to see all 17 supported frameworks (GDPR, CCPA, HIPAA, PCI-DSS, SOC2, SOX, COPPA, FedRAMP, NIST, ISO27001, ePrivacy, DORA, NIS2, EU-AI-Act, and more).

Self-Teaching Amendments

The LLM benchmark system auto-generates precision amendments for judges with high false-positive rates. Amendments are data-driven corrections injected into prompts that improve accuracy over successive benchmark runs.

The self-teaching loop:

Run benchmark → analyzer identifies judges below 70% precision
Generates targeted amendments (e.g., "Judge ERR: do not flag clean Express code with framework error middleware")
Next benchmark run loads amendments → precision improves
Run judges codify-amendments to bake amendments permanently into the distributed package

Taint Flow Analysis

The engine performs inter-procedural taint tracking to trace data from user-controlled sources (e.g., req.body, process.env) through transformations to security-sensitive sinks (e.g., eval(), exec(), SQL queries). Taint flows are used to boost confidence on true-positive findings and suppress false positives where sanitization is detected.

Positive Signal Detection

Code that demonstrates good practices receives score bonuses (capped at +15):

Signal	Bonus
Parameterized queries	+3
Security headers (helmet)	+3
Auth middleware (passport, etc.)	+3
Proper error handling	+2
Input validation libs (zod, joi, etc.)	+2
Rate limiting	+2
Structured logging (pino, winston)	+2
CORS configuration	+1
Strict mode / strictNullChecks	+1
Test patterns (describe/it/expect)	+1

Framework-Aware Rules

Judges include framework-specific detection for Express, Django, Flask, FastAPI, Spring, ASP.NET, Rails, and more. Framework middleware (e.g., helmet(), express-rate-limit, passport.authenticate()) is recognized as mitigation, reducing false positives.

Cross-File Import Resolution

In project-level analysis, imports are resolved across files. If one file imports a security middleware module from another file in the project, findings about missing security controls are automatically adjusted with reduced confidence.

Scoring

Each judge scores the code from 0 to 100:

Severity	Score Deduction
Critical	−30 points
High	−18 points
Medium	−10 points
Low	−5 points
Info	−2 points

Verdict logic:

FAIL — Any critical finding, or score < 60
WARNING — Any high finding, any medium finding, or score < 80
PASS — Score ≥ 80 with no critical, high, or medium findings

The overall tribunal score is the average of all 45 judges. The overall verdict fails if any judge fails.

Project Structure

Code

judges/
├── src/
│   ├── index.ts              # MCP server entry point — tools, prompts, transport
│   ├── api.ts                # Programmatic API entry point
│   ├── cli.ts                # CLI argument parser and command router
│   ├── types.ts              # TypeScript interfaces (Finding, JudgeEvaluation, etc.)
│   ├── config.ts             # .judgesrc configuration parser and validation
│   ├── errors.ts             # Custom error types (ConfigError, EvaluationError, ParseError)
│   ├── language-patterns.ts  # Multi-language regex pattern constants and helpers
│   ├── judge-registry.ts     # Unified JudgeRegistry — single source of truth for all judges
│   ├── plugins.ts            # Plugin API façade (delegates to JudgeRegistry)
│   ├── scoring.ts            # Confidence scoring and calibration
│   ├── dedup.ts              # Finding deduplication engine
│   ├── fingerprint.ts        # Finding fingerprint generation
│   ├── comparison.ts         # Tool comparison benchmark data
│   ├── cache.ts              # Evaluation result caching
│   ├── calibration.ts        # Confidence calibration from feedback data
│   ├── fix-history.ts        # Auto-fix application history tracking
│   ├── ast/                  # AST analysis engine (built-in, no external deps)
│   │   ├── index.ts          # analyzeStructure() — routes to correct parser
│   │   ├── types.ts          # FunctionInfo, CodeStructure interfaces
│   │   ├── tree-sitter-ast.ts    # Tree-sitter WASM parser (all 8 languages)
│   │   ├── structural-parser.ts  # Fallback scope-tracking parser
│   │   ├── cross-file-taint.ts   # Cross-file taint propagation analysis
│   │   └── taint-tracker.ts      # Single-file taint flow tracking
│   ├── evaluators/           # Analysis engine for each judge
│   │   ├── index.ts          # evaluateWithJudge(), evaluateWithTribunal(), evaluateProject(), etc.
│   │   ├── shared.ts         # Scoring, verdict logic, markdown formatters
│   │   └── *.ts              # One analyzer per judge (45 files)
│   ├── formatters/           # Output formatters
│   │   ├── sarif.ts              # SARIF 2.1.0 output
│   │   ├── html.ts               # Self-contained HTML report (dark/light theme, filters)
│   │   ├── junit.ts              # JUnit XML output (Jenkins, Azure DevOps, GitHub Actions)
│   │   ├── codeclimate.ts        # CodeClimate/GitLab Code Quality JSON
│   │   ├── diagnostics.ts        # Diagnostics formatter
│   │   └── badge.ts              # SVG and text badge generator
│   ├── commands/             # CLI subcommands
│   │   ├── init.ts               # Interactive project setup wizard
│   │   ├── fix.ts                # Auto-fix patch preview and application
│   │   ├── watch.ts              # Watch mode — re-evaluate on save
│   │   ├── report.ts             # Project-level local report
│   │   ├── hook.ts               # Pre-commit hook install/uninstall
│   │   ├── ci-templates.ts       # GitLab, Azure, Bitbucket CI templates
│   │   ├── diff.ts               # Evaluate unified diff (git diff)
│   │   ├── deps.ts               # Dependency supply-chain analysis
│   │   ├── baseline.ts           # Create baseline for finding suppression
│   │   ├── completions.ts        # Shell completions (bash/zsh/fish/PowerShell)
│   │   ├── docs.ts               # Per-judge rule documentation generator
│   │   ├── feedback.ts           # False-positive tracking & finding feedback
│   │   ├── benchmark.ts          # Detection accuracy benchmark suite
│   │   ├── rule.ts               # Custom rule authoring wizard
│   │   ├── language-packs.ts     # Language-specific rule pack presets
│   │   └── config-share.ts       # Shareable team/org configuration
│   ├── presets.ts            # Named evaluation presets (strict, lenient, security-only, …)
│   ├── patches/
│   │   └── index.ts              # 201 deterministic auto-fix patch rules
│   ├── tools/                # MCP tool registrations
│   │   ├── register.ts           # Tool registration orchestrator
│   │   ├── register-evaluation.ts    # Evaluation tools (evaluate_code, etc.)
│   │   ├── register-workflow.ts      # Workflow tools (app builder, reports, etc.)
│   │   ├── prompts.ts            # MCP prompt registrations (per-judge prompts)
│   │   └── schemas.ts            # Zod schemas for tool parameters
│   ├── reports/
│   │   └── public-repo-report.ts   # Public repo clone + full tribunal report generation
│   └── judges/               # Judge definitions (id, name, domain, system prompt)
│       ├── index.ts          # Side-effect imports + re-exports (JUDGES, getJudge, getJudgeSummaries)
│       └── *.ts              # One self-registering definition per judge (45 files)
├── scripts/
│   ├── generate-public-repo-report.ts  # Run: npm run report:public-repo -- --repoUrl <url>
│   ├── daily-popular-repo-autofix.ts   # Run: npm run automation:daily-popular
│   └── debug-fp.ts                     # Debug false-positive findings
├── examples/
│   ├── sample-vulnerable-api.ts  # Intentionally flawed code (triggers all judges)
│   ├── demo.ts                   # Run: npm run demo
│   └── quickstart.ts             # Quick-start evaluation example
├── tests/
│   ├── judges.test.ts            # Core judge evaluation tests
│   ├── negative.test.ts          # Negative / FP-avoidance tests
│   ├── subsystems.test.ts        # Subsystem integration tests
│   ├── extension-logic.test.ts   # VS Code extension logic tests
│   └── tool-routing.test.ts      # MCP tool routing tests
├── grammars/                 # Tree-sitter WASM grammar files
│   ├── tree-sitter-typescript.wasm
│   ├── tree-sitter-cpp.wasm
│   ├── tree-sitter-python.wasm
│   ├── tree-sitter-go.wasm
│   ├── tree-sitter-rust.wasm
│   ├── tree-sitter-java.wasm
│   └── tree-sitter-c_sharp.wasm
├── judgesrc.schema.json      # JSON Schema for .judgesrc config files
├── server.json               # MCP Registry manifest
├── package.json
├── tsconfig.json
└── README.md

Scripts

Command	Description
`npm run build`	Compile TypeScript to `dist/`
`npm run dev`	Watch mode — recompile on save
`npm test`	Run the full test suite
`npm run demo`	Run the sample tribunal demo
`npm run report:public-repo -- --repoUrl <url>`	Generate a full tribunal report for a public repository URL
`npm run report:quickstart -- --repoUrl <url>`	Run opinionated high-signal report defaults for fast adoption
`npm run automation:daily-popular`	Analyze up to 10 rotating popular repos/day and open up to 5 remediation PRs per repo
`npm start`	Start the MCP server
`npm run clean`	Remove `dist/`
`judges init`	Interactive project setup wizard
`judges fix <file>`	Preview auto-fix patches (add `--apply` to write)
`judges watch <dir>`	Watch mode — re-evaluate on file save
`judges report <dir>`	Full tribunal report on a local directory
`judges hook install`	Install a Git pre-commit hook
`judges diff`	Evaluate changed lines from unified diff
`judges deps`	Analyze dependencies for supply-chain risks
`judges baseline create`	Create baseline for finding suppression
`judges ci-templates`	Generate CI pipeline templates
`judges docs`	Generate per-judge rule documentation
`judges completions <shell>`	Shell completion scripts
`judges feedback submit`	Mark findings as true positive, false positive, or won't fix
`judges feedback stats`	Show false-positive rate statistics
`judges benchmark run`	Run detection accuracy benchmark suite
`judges rule create`	Interactive custom rule creation wizard
`judges rule list`	List custom evaluation rules
`judges pack list`	List available language packs
`judges config export`	Export config as shareable package
`judges config import <src>`	Import a shared configuration
`judges compare`	Compare judges against other code review tools
`judges list`	List all 45 judges with domains and descriptions
`judges list --frameworks`	List supported regulatory frameworks and `.judgesrc` usage
`judges codify-amendments`	Bake self-teaching amendments into judge source files

Daily Popular Repo Automation

This repo includes a scheduled workflow at .github/workflows/daily-popular-repo-autofix.yml that:

selects up to 10 repositories per day from a default pool of 100+ popular repos (or a manually supplied target),
runs the full Judges evaluation across supported source languages,
applies only conservative, single-line remediations that reduce matching finding counts,
opens up to 5 PRs per repository with attribution to both Judges and the target repository,
skips repositories unless they are public and PR creation is possible with existing GitHub auth (no additional auth flow).
enforces hard runtime caps of 10 repositories/day and 5 PRs/repository.

Each run writes daily-autofix-summary.json (or SUMMARY_PATH) with per-repository telemetry, including:

runAggregate — compact run-level totals and cross-repo top prioritized rules,
runAggregate.totalCandidatesDiscovered and runAggregate.totalCandidatesAfterLocationDedupe — signal how much overlap was removed before attempting fixes,
runAggregate.totalCandidatesAfterPriorityThreshold — candidates that remain after applying minimum priority score,
runAggregate.dedupeReductionPercent — percent reduction from location dedupe for quick runtime-efficiency tracking,
runAggregate.priorityThresholdReductionPercent — percent reduction from minimum-priority filtering after dedupe,
priorityRulePrefixesUsed — dangerous rule prefixes used during prioritization,
minPriorityScoreUsed — minimum candidatePriorityScore applied for candidate inclusion,
candidatesDiscovered, candidatesAfterLocationDedupe, and candidatesAfterPriorityThreshold — per-repo candidate counts after each filter stage,
topPrioritizedRuleCounts — most common rule IDs among ranked candidates,
topPrioritizedCandidates — top ranked candidate samples (rule, severity, confidence, file, line, priority score).

Optional runtime control:

AUTOFIX_MIN_PRIORITY_SCORE — minimum candidate priority score required after dedupe (default: 0, disabled).

Required secret:

JUDGES_AUTOFIX_GH_TOKEN — GitHub token with permission to fork/push/create PRs for target repositories.

Manual run:

bash

gh workflow run "Judges Daily Full-Run Autofix PRs" -f targetRepoUrl=https://github.com/owner/repo

Programmatic API

Judges can be consumed as a library (not just via MCP). Import from @kevinrabun/judges/api:

server.ts

import {
  evaluateCode,
  evaluateProject,
  evaluateCodeSingleJudge,
  getJudge,
  JUDGES,
  findingsToSarif,
} from "@kevinrabun/judges/api";

// Full tribunal evaluation
const verdict = evaluateCode("const x = eval(input);", "typescript");
console.log(verdict.overallScore, verdict.overallVerdict);

// Single judge
const result = evaluateCodeSingleJudge("cybersecurity", code, "typescript");

// SARIF output for CI integration
const sarif = findingsToSarif(verdict.evaluations.flatMap(e => e.findings));

Package Exports

Entry Point	Description
`@kevinrabun/judges/api`	Programmatic API (default)
`@kevinrabun/judges/server`	MCP server entry point
`@kevinrabun/judges/sarif`	SARIF 2.1.0 formatter
`@kevinrabun/judges/junit`	JUnit XML formatter
`@kevinrabun/judges/codeclimate`	CodeClimate/GitLab Code Quality JSON
`@kevinrabun/judges/badge`	SVG and text badge generator
`@kevinrabun/judges/diagnostics`	Diagnostics formatter
`@kevinrabun/judges/plugins`	Plugin system API (see Plugin Guide)
`@kevinrabun/judges/fingerprint`	Finding fingerprint utilities
`@kevinrabun/judges/comparison`	Tool comparison benchmarks

SARIF Output

Convert findings to SARIF 2.1.0 for GitHub Code Scanning, Azure DevOps, and other CI/CD tools:

server.ts

import { findingsToSarif, evaluationToSarif, verdictToSarif } from "@kevinrabun/judges/sarif";

const sarif = verdictToSarif(verdict, "src/app.ts");
fs.writeFileSync("results.sarif", JSON.stringify(sarif, null, 2));

Custom Error Types

All thrown errors extend JudgesError with a machine-readable code property:

Error Class	Code	When
`ConfigError`	`JUDGES_CONFIG_INVALID`	Malformed `.judgesrc` or invalid inline config
`EvaluationError`	`JUDGES_EVALUATION_FAILED`	Unknown judge, analyzer crash
`ParseError`	`JUDGES_PARSE_FAILED`	Unparseable source code or input data

server.ts

import { ConfigError, EvaluationError } from "@kevinrabun/judges/api";
try {
  evaluateCode(code, "typescript");
} catch (e) {
  if (e instanceof ConfigError) console.error("Config issue:", e.code);
}

License

MIT