How to Integrate Ponytail with Existing Applications: A Complete Guide

Ponytail integrates with existing applications by injecting a minimal ruleset (the "ladder") into every LLM turn through either a Node-based plugin or simple markdown rule files, exposing slash commands like /ponytail and /ponytail-review across supported AI coding environments.

Ponytail is a "lazy senior dev" skill-set designed to guide AI-assisted development toward minimal, safe code. According to the DietrichGebert/ponytail repository, you can integrate Ponytail into any existing application—whether Claude Code, Cursor, OpenCode, or Gemini CLI—by either installing a lightweight plugin or copying rule files into your agent's configuration directory.

Core Architecture Components

Ponytail's integration relies on four core components that work together to enforce coding standards across AI interactions.

Rule Files

Rule files provide the always-on instruction set that guides the LLM to write minimal, safe code. The primary rule file, AGENTS.md, lives in the repository root, while platform-specific variants reside in directories like .windsurf/rules/ponytail.md. These files contain the "ladder" philosophy that Ponytail enforces on every prompt.

Runtime Hooks

The hooks/ponytail-runtime.js file loads the rule set on each turn, activates the selected intensity (lite, full, ultra, or off), and injects the rules into sub-agents. This hook ensures that every LLM interaction automatically includes Ponytail's constraints without manual prompting.

Skills Module

skills/ponytail.mjs implements the slash commands (/ponytail, /ponytail-review, /ponytail-gain) that users invoke from the host UI. This module registers six distinct skills that allow developers to toggle intensity levels and request code reviews directly within their AI-assisted workflow.

Plugin Manifests

Declarative configuration files (plugin.json, opencode.json) expose the hooks and skills to the host environment. These manifests tell Node-based agents like Codex, Claude Code, and Gemini CLI where to find the runtime logic and command implementations.

Integration Methods

You can integrate Ponytail through two primary approaches depending on your host application's capabilities.

Node-Based Plugin Installation

Best for: Claude Code, Codex, OpenCode, Gemini CLI, Pi, and Qoder.

Install the Ponytail package via npm and reference it in your host configuration:

# Install the plugin

npm install @dietrichgebert/ponytail

# Configure OpenCode to load the plugin

echo '{ "plugin": ["@dietrichgebert/ponytail"] }' > opencode.json

The plugin automatically registers hooks/ponytail-runtime.js and the six skills from skills/ponytail.mjs. No additional configuration is required after installation.

Rule-File Method for Non-Plugin Hosts

Best for: Cursor, Windsurf, Cline, Copilot, Kiro, and Antigravity.

Copy the markdown rule files directly into your host's rules directory:

# For Cursor

cp .cursor/rules/ponytail.md ~/.cursor/rules/

# For Windsurf

cp .windsurf/rules/ponytail.md ~/.windsurf/rules/

Because the rules are pure text, they can be copied directly into any agent's "guidelines" directory without installing the full plugin—perfect for legacy setups or environments that don't support Node plugins.

Step-by-Step Integration Guide

Follow these steps to activate Ponytail in your existing development environment.

1. Choose Your Integration Path

Evaluate whether your AI host supports Node plugins. Refer to docs/agent-portability.md in the repository for a complete mapping of Ponytail files to various AI agents.

2. Install According to Host Type

For Node-based hosts, run the installation command and create the configuration file as shown in the previous section. For file-based hosts, simply copy AGENTS.md or the platform-specific rule files to the appropriate configuration directory.

3. Configure Default Intensity

Set your preferred default mode using either environment variables or configuration files:

# Environment variable

export PONYTAIL_DEFAULT_MODE=full

Or create ~/.config/ponytail/config.json:

{
  "defaultMode": "lite"
}

Available modes include lite (basic constraints), full (standard minimal-code guidance), ultra (aggressive optimization), and off (disabled).

4. Use the Slash Commands

In a supported UI, type the slash commands to control Ponytail:

/ponytail ultra          # Activate aggressive minimal-code mode

/ponytail-review        # Request pruning of current diff

/ponytail-gain          # View benchmark impact numbers

The commands are implemented in skills/ponytail.mjs, which the host calls automatically when it detects the slash syntax.

5. Validate the Integration

Run the repository's test suite to ensure proper hook and skill loading:

npm test

Open an example project like examples/react-countdown.md and invoke /ponytail-review to verify the before/after code size reductions are working correctly.

Practical Integration Examples

Adding Ponytail to an OpenCode Session

Create opencode.json in your project root:

{
  "plugin": ["@dietrichgebert/ponytail"]
}

This single-line configuration tells OpenCode to load the Ponytail plugin on every turn, activating the runtime hook and all slash commands.

Using Rule Files with Cursor

Copy the rule file into Cursor's configuration directory:

cp .cursor/rules/ponytail.md ~/.cursor/rules/

No further installation is required—the next prompt will automatically include the ladder instructions loaded from the markdown file.

Switching Intensity from Terminal

Within a terminal connected to Claude Code, switch modes dynamically:

/ponytail ultra   # Activates ultra mode for the remainder of the session

This command interfaces with the runtime hook to immediately change the constraint level for subsequent LLM calls.

Running Performance Benchmarks

Validate Ponytail's impact on your codebase using the built-in benchmark suite:

npx promptfoo@latest eval -c benchmarks/promptfooconfig.yaml

The output shows LOC, token, cost, and time reductions, with detailed results available in benchmarks/results/2026-06-18-agentic.md.

Summary

• Ponytail integrates via two methods: Node-based plugins for modern AI hosts or simple markdown rule files for any agent that reads configuration directories.
• Core files to know: AGENTS.md (rules), hooks/ponytail-runtime.js (injection logic), and skills/ponytail.mjs (commands).
• Configuration is minimal: Either one line in opencode.json or a single file copy to ~/.cursor/rules/.
• Four intensity modes (lite, full, ultra, off) control the strictness of code minimization.
• Six slash commands provide interactive control over the skill-set without leaving your coding session.

Frequently Asked Questions

Does Ponytail work with self-hosted or offline LLM setups?

Yes. Because Ponytail operates through text-based rule files and local JavaScript hooks, it functions entirely offline once installed. The AGENTS.md rules work with any LLM that reads system prompts, and the Node-based plugin runs locally without requiring external API calls beyond your existing LLM provider.

Can I customize the Ponytail rules for my specific codebase?

Absolutely. The rule files in AGENTS.md and platform-specific directories like .windsurf/rules/ponytail.md are standard markdown. You can append project-specific constraints to these files, and the hooks/ponytail-runtime.js will inject your custom rules alongside the default ladder instructions.

Which AI agents support the /ponytail slash commands?

The slash commands require a host that can execute the skills defined in skills/ponytail.mjs. This includes Claude Code, Codex, OpenCode, Gemini CLI, Pi, and Qoder when using the Node plugin. For hosts like Cursor or Windsurf that only consume rule files, you won't have interactive commands but will still receive the always-on guidance from the ladder rules.

How do I completely disable Ponytail without uninstalling?

Set the intensity to off using either the environment variable PONYTAIL_DEFAULT_MODE=off or the command /ponytail off. The hooks/ponytail-runtime.js checks this setting before injecting rules, effectively passing through standard LLM behavior while keeping the plugin installed for easy reactivation later.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}