prlyn 0.1.1

A powerful, extensible tool for analyzing LLM prompts.

prlyn (Prompt Linter)

prlyn is a professional-grade static analysis tool for LLM prompts. Beyond basic linting, it provides a comprehensive suite of linguistic, security, and model-specific metrics to ensure your prompts are robust, efficient, and safe for production.

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🌟 Why prlyn?

Developing effective prompts is often trial-and-error. prlyn brings Engineering Rigor to Prompt Engineering by:

• Quantifying Quality: Move from "it feels slow" to "Actionable Ratio is 0.45".
• Detecting Vulnerabilities: Identify design-time security flaws before they become runtime liabilities.
• Ensuring Model Fit: Adjust scores based on whether you are targeting GPT-4, Claude, or legacy models.
• Preventing Regression: Track metrics over time as you iterate on complex system instructions.

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🚀 Key Features

1. Design-time Security (Prompt Shield)

prlyn doesn't just detect attacks; it flags vulnerability patterns in your system prompts:

• Delimiter Analysis: Verifies strong separation between instructions and user data.
• Defensive Anchors: Checks for mandatory safety instructions and negative constraints.
• Reflexive Leakage: Identifies instructions that might inadvertently reveal your prompt's inner workings.

2. Advanced Linguistics

• Flow Cohesion: Uses semantic embeddings to detect "logical jumps" or disjointed instructions that confuse models.
• Instructional Strength: Quantifies the assertiveness of your commands using weighted verb analysis (e.g., Must vs. Try).
• Position Scoring: Detects the "lost middle" phenomenon by measuring instructional density at the context window's edges.

3. Model-Aware Intelligence

Use the --model flag to tailor analysis to specific LLM quirks:

• GPT-4: Penalizes "buried" instructions more heavily due to known recency bias.
• Claude 3.5: Adjusts thresholds for excellent long-context retrieval capabilities.

4. Prompt Shadowing (Iterative Development)

• Automatic History: Every scan is stored in a hidden .prlyn/ directory.
• Regression Detection: Use prlyn --diff to compare your current draft against the previous version and see how your scores changed.

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🚀 Quick Start (CLI)

For human users who want to manually scan a prompt. No installation required.

# Basic analysis
uvx --from git+https://github.com/Mishtert/prlyn prlyn "Your prompt here..."

# Show all options
uvx --from git+https://github.com/Mishtert/prlyn prlyn --help

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🛠 Usage

CLI Usage

# Basic analysis (if already running via uvx as shown above)
uvx --from git+https://github.com/Mishtert/prlyn prlyn "Your prompt here..."

Model-specific analysis

prlyn "..." --model gpt-4

1. Run analysis (saved automatically)

prlyn "Initial draft..."

2. Iterate and compare against your last run

prlyn "Improved draft..." --diff

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🤖 For AI Agents (MCP Server)

For AI Assistants (Claude, Cursor, Antigravity) to self-correct and optimize prompts.

1. Claude Desktop

Add this to your claude_desktop_config.json:

{
  "mcpServers": {
    "prlyn": {
      "command": "uvx",
      "args": [
        "--from", "git+https://github.com/Mishtert/prlyn",
        "prlyn"
      ]
    }
  }
}

2. Cursor

1. Go to Settings > Cursor Settings > Features > MCP.
2. Click + Add New MCP Server.
3. Name: prlyn
4. Type: command
5. Command: uvx --from git+https://github.com/Mishtert/prlyn prlyn

3. Antigravity

Add the following to your MCP configuration:

{
  "mcpServers": {
    "prlyn": {
      "command": "uvx",
      "args": [
        "--from", "git+https://github.com/Mishtert/prlyn",
        "prlyn"
      ]
    }
  }
}

4. Automated Rewriting

prlyn supports self-correction workflows. Your agent can:

1. Call prlyn.analyze_prompt(prompt) to get a quality score.
2. If the score is low, call prlyn.get_improvement_template(prompt).
3. Use the returned Actionable Template to rewrite its own prompt.

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🔭 Technical Details

• NLP Engine: Spacy (en_core_web_sm) for linguistic structure and POS tagging.
• Embeddings: SentenceTransformers (all-MiniLM-L6-v2) for semantic similarity and flow analysis.
• Tokenizer: tiktoken for accurate token budget estimation.

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💻 For Developers

If you want to contribute to prlyn:

# Clone the repository
git clone https://github.com/Mishtert/prlyn.git
cd prlyn

# Install dependencies and setup environment
uv sync

# Run pipeline locally
uv run pytest
uv run pre-commit run --all-files

CI/CD Pipeline

Every PR triggers a 4-stage pipeline:

1. Quality: Linting (ruff) and Type Checking (mypy).
2. Security: Vulnerability scanning (bandit).
3. Test: Unit tests (pytest).
4. Build: Package verification (uv build).

Refer to CONTRIBUTING.md for more details.

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🔒 Privacy & Telemetry

prlyn respects your privacy.

• No Home: prlyn does not send any data to external servers.
• Local Analysis: All NLP and scanning happens locally on your machine.
• Usage Tracking: We rely solely on public PyPI download stats to gauge interest.