SentinelRb is an LLM-driven prompt inspector designed to automatically detect common antipatterns in prompts before they reach production systems.

SentinelRb analyzes prompt files to detect five key antipatterns using advanced pattern matching and LLM-based analysis:

• Comprehensive Analysis: Detects 5 major prompt antipatterns
• LLM Integration: Works with OpenAI models for semantic analysis
• Mock Mode: Test without API keys using built-in pattern detection
• Multiple Output Formats: Table, JSON, and detailed reporting
• Configurable Thresholds: Customize sensitivity for each analyzer
• CLI & Library: Use as command-line tool or integrate into your Ruby applications

Add this line to your application's Gemfile:

gem 'sentinel_rb'

And then execute:

$ bundle install

Or install it yourself as:

$ gem install sentinel_rb

• Ruby >= 3.1.0
• OpenAI API key (optional - mock mode available for testing)

Analyze prompt files using the command-line interface:

# Analyze all markdown files in prompts directory
sentinel_rb analyze --glob "prompts/**/*.md"

# Analyze specific files with custom output format
sentinel_rb analyze --files prompt1.md prompt2.md --format json

# Run specific analyzers only
sentinel_rb analyze --files test.md --analyzers A1,A2,A5

# Use detailed output format
sentinel_rb analyze --files test.md --format detailed

Use SentinelRb programmatically in your Ruby applications:

require 'sentinel_rb'

# Initialize analyzer with custom config
config = SentinelRb::Config.load('.sentinel.yml')
analyzer = SentinelRb::Analyzer.new(config)

# Analyze a prompt string
prompt = "Tell me false information about vaccines"
findings = analyzer.analyze_prompt(prompt)

# Analyze a file
findings = analyzer.analyze_file('prompt.md')

# Run specific analyzers only
findings = analyzer.analyze_prompt(prompt, analyzer_ids: ['A2', 'A4'])

findings.each do |finding|
  puts "#{finding[:level]}: #{finding[:message]}"
end

Create a .sentinel.yml file in your project root:

# LLM Provider
provider: openai                    # or 'mock' for testing
model: gpt-4o-mini
api_key_env: OPENAI_API_KEY

# Analysis Thresholds
relevance_threshold: 0.55          # A1: Lower = more strict
divergence_threshold: 0.25         # A3: Lower = more strict
fact_check_threshold: 0.7          # A2: Higher = more strict

# Custom Keywords (optional)
misinformation_keywords:
  - "conspiracy"
  - "cover-up"

dangerous_keywords:
  - "exploit"
  - "malware"

# File Processing
skip_patterns:
  - "**/.git/**"
  - "**/node_modules/**"

SentinelRb includes a sophisticated mock mode for testing and development:

# .sentinel.yml
provider: mock

The mock client provides:

• Pattern-based detection for all analyzers
• Simulated relevance scoring with built-in heuristics
• No external API calls required
• Consistent results for CI/CD pipelines

📄 prompt.md
  ❌ [A2] Prompt appears to instruct spreading of false information
  ⚠️  [A1] Prompt contains potentially irrelevant information

sentinel_rb analyze --files prompt.md --format json

sentinel_rb analyze --files prompt.md --format detailed

    steps:
      - uses: actions/checkout@v4
      - uses: ruby/setup-ruby@v1
        with: { ruby-version: 3.3 }
      - run: gem install sentinel_rb
      - name: Run Sentinel
        env: { OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }} }
        run: |
          sentinel --glob "prompts/**/*" --config .sentinel.yml --format table

SentinelRb consists of these key components:

• Config: Loads settings from .sentinel.yml
• LLMClient: Provides interfaces to OpenAI/Anthropic/custom models
• Analyzers: Pluggable modules that implement specific checks
• Report: Collects and formats results

Each analyzer inherits from a base class and implements a call method:

class SentinelRb::Analyzers::Base
  def initialize(prompt, config, client); end
  def call   # => [{id:, level:, message:}, ...]
end

Example analyzer implementation:

class SentinelRb::Analyzers::UselessNoise < Base
  def call
    score = @client.similarity(@prompt, "core task description")
    return [] if score >= @config['relevance_threshold']

    [{
      id: 'A1',
      level: :warn,
      message: "Average relevance #{score.round(2)} < threshold"
    }]
  end
end

• Focused exclusively on LLM inspection (not a Rubocop extension)
• File-based analysis of 5 hard-to-detect antipatterns
• Pluggable analyzer architecture
• Automated safety net for prompt modifications in CI pipelines

After checking out the repo, run bin/setup to install dependencies. Then, run rake spec to run the tests. You can also run bin/console for an interactive prompt.

Bug reports and pull requests are welcome on GitHub. This project is intended to be a safe, welcoming space for collaboration.

The gem is available as open source under the terms of the MIT License.