# Where to Find the Full Ponytail Skill Definition: Complete File Guide > Find the complete Ponytail skill definition in the GitHub repository. Access command templates, prompt rules, and usage examples for `/ponytail` commands. - Repository: [DietrichGebert/ponytail](https://github.com/DietrichGebert/ponytail) - Tags: api-reference - Published: 2026-09-06 --- **The complete Ponytail skill definition resides in [`skills/ponytail/SKILL.md`](https://github.com/DietrichGebert/ponytail/blob/main/skills/ponytail/SKILL.md), which contains the command templates, prompt rules, and usage examples that agents invoke via `/ponytail` commands.** The DietrichGebert/ponytail repository provides a structured plugin architecture for LLM agents. Understanding exactly where the **Ponytail skill definition** lives is essential for customizing behavior, debugging agent responses, or manually registering the skill in compatible environments. ## Primary Location of the Ponytail Skill Definition The authoritative source of truth for Ponytail's capabilities is located at [`skills/ponytail/SKILL.md`](https://github.com/DietrichGebert/ponytail/blob/main/skills/ponytail/SKILL.md). This markdown file houses the complete command-prompt template, textual injection rules, and usage examples that govern how agents interpret the `/ponytail` interface. ### Inside the SKILL.md File Structure According to the repository source code, [`SKILL.md`](https://github.com/DietrichGebert/ponytail/blob/main/SKILL.md) defines the "ponytail" skill that agents recognize and invoke. When you issue commands such as `/ponytail` or `ponytail:`, the agent references the definitions stored in this specific file to determine available actions, parameter formats, and response constraints. ## How the Skill Definition Is Registered The mechanism that exposes the skill to agents is implemented in [`__init__.py`](https://github.com/DietrichGebert/ponytail/blob/main/__init__.py). This initialization file automatically discovers skill definitions within the `skills/` directory hierarchy and registers them with the host context. The registration uses the context's `register_skill` method: ```python ctx.register_skill("ponytail", Path("skills/ponytail/SKILL.md")) ``` This call binds the string identifier `"ponytail"` to the markdown definition file, enabling the plugin to inject the skill's prompt rules into every LLM turn. ## Alternative Entry Points and Hooks Beyond the primary [`SKILL.md`](https://github.com/DietrichGebert/ponytail/blob/main/SKILL.md) file, the repository provides additional access patterns for different agent architectures and initialization scenarios. ### Lifecycle Hooks for Automatic Loading For agents that support automated hook loading, the `hooks/` directory contains configuration files such as [`hooks/qoder-hooks.json`](https://github.com/DietrichGebert/ponytail/blob/main/hooks/qoder-hooks.json). These hooks automatically load the **Ponytail skill definition** during agent initialization without requiring explicit manual registration calls in the host code. ### AGENTS.md Fallback Instructions The repository includes [`AGENTS.md`](https://github.com/DietrichGebert/ponytail/blob/main/AGENTS.md) as a fallback instruction set. This file provides a condensed, static version of the skill rules for host environments that only read textual rule files rather than registering dynamic skills through the plugin interface. ## Programmatic Access to the Skill Definition You can load and inspect the skill definition directly using standard Python file operations. The following pattern demonstrates how the plugin internally accesses the markdown content: ```python from pathlib import Path ROOT = Path(__file__).parent.parent skill_md = (ROOT / "skills" / "ponytail" / "SKILL.md").read_text(encoding="utf-8") print(skill_md) # prints the full skill definition ``` When working within an LLM-enabled environment such as Claude Code, you activate specific skill modes using the command syntax defined in [`SKILL.md`](https://github.com/DietrichGebert/ponytail/blob/main/SKILL.md): ```bash /ponytail ultra # activates the "ultra" mode using the definition in SKILL.md ``` ## Summary - The complete **Ponytail skill definition** lives in [`skills/ponytail/SKILL.md`](https://github.com/DietrichGebert/ponytail/blob/main/skills/ponytail/SKILL.md) - Registration occurs in [`__init__.py`](https://github.com/DietrichGebert/ponytail/blob/main/__init__.py) via `ctx.register_skill("ponytail", Path(...))` - Lifecycle hooks in `hooks/` enable automatic loading for compatible agents - [`AGENTS.md`](https://github.com/DietrichGebert/ponytail/blob/main/AGENTS.md) serves as a static fallback for rule-only host environments ## Frequently Asked Questions ### What file contains the complete Ponytail skill definition? The file [`skills/ponytail/SKILL.md`](https://github.com/DietrichGebert/ponytail/blob/main/skills/ponytail/SKILL.md) contains the complete definition, including command templates, prompt rules, and usage examples that agents use when processing `/ponytail` commands. ### How do I register the Ponytail skill manually in my agent? Import the skill file path and call `ctx.register_skill("ponytail", Path("skills/ponytail/SKILL.md"))` as implemented in [`__init__.py`](https://github.com/DietrichGebert/ponytail/blob/main/__init__.py). This binds the skill identifier to the markdown definition for the host context. ### What is the difference between SKILL.md and AGENTS.md? [`SKILL.md`](https://github.com/DietrichGebert/ponytail/blob/main/SKILL.md) contains the full dynamic skill definition used for plugin registration and runtime prompt injection, while [`AGENTS.md`](https://github.com/DietrichGebert/ponytail/blob/main/AGENTS.md) provides a static fallback version for hosts that consume rules as plain text without supporting dynamic skill loading. ### Where are the lifecycle hooks for automatic skill loading located? Lifecycle hooks are stored in the `hooks/` directory, with files such as [`qoder-hooks.json`](https://github.com/DietrichGebert/ponytail/blob/main/qoder-hooks.json) configuring automatic skill initialization for agents that support hook-based plugin discovery.