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)

# 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

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)

npm install @kevinrabun/judges

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):

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

Or run the server directly:

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.

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.

<p align="center"> <img src="docs/terminal-output.svg" alt="Judges — Terminal Output" width="680" /> </p>

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)

# 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

# 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:

# 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 <code>http://localhost:4567/webhook</code> via <code>ngrok http 4567</code> and configure the GitHub App webhook URL accordingly.

Use in GitHub Actions

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:
      - 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)

# 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

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:

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):

╔══════════════════════════════════════════════════════════════╗
║             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):

╔══════════════════════════════════════════════════════════════╗
║           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

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](https://marketplace.visualstudio.com/items?itemName=kevinrabun.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

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:

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

#### Claude Desktop

Add to claude_desktop_config.json:

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

#### Cursor / other MCP clients

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

{
  "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:

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:

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.

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.

judges watch

Continuously re-evaluate files on save.

judges report

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

judges hook

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

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).

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

judges deps

Analyze project dependencies for supply-chain risks.

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

judges baseline

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

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.

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.

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.

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:

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:

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:

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

#### Score Badges

Generate SVG or text badges for your README:

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

<!-- JUDGES_TABLE_START -->

<!-- JUDGES_TABLE_END -->

How It Works

The tribunal operates in three layers:

1. 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.

1. 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.

1. 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:

Recommended MCP Stack

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

┌─────────────────────────────────────────────────────────┐
│                   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

What This Means in Practice

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

1. 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
2. CVE Server → Check every dependency in package.json against known vulnerabilities
3. 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[]

evaluate_app_builder_flow

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

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

Supports:

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

evaluate_public_repo_report

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

Quick examples

Generate a report from CLI:

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:

{
  "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:

# 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.

evaluate_code_single_judge

Submit code to a specific judge for targeted review.

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.

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.

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.

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.

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.

Additional MCP Tools

#### 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:

<!-- PROMPTS_TABLE_START -->

<!-- PROMPTS_TABLE_END -->

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.

{
  "$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
}

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:

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:

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:

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:

{ "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:

1. Run benchmark → analyzer identifies judges below 70% precision
2. Generates targeted amendments (e.g., "Judge ERR: do not flag clean Express code with framework error middleware")
3. Next benchmark run loads amendments → precision improves
4. 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):