What Are the Available Ponytail Skills? A Complete Guide to Built-In Modes

Ponytail ships with six built-in skills—ponytail, ponytail-review, ponytail-help, ponytail-gain, ponytail-debt, and ponytail-audit—each defined in a Markdown-based SKILL.md file under the skills/ directory.

Ponytail skills are Markdown-based plugins that control how the assistant behaves. When the system starts, ponytail/__init__.py automatically scans the skills/ folder, locates every sub-directory containing a SKILL.md file, and registers it with the Hermes plugin system (lines 196-200). This architecture makes extending Ponytail as simple as adding a new directory with a skill definition.

How Ponytail Skills Work

The skill registration loop in ponytail/__init__.py handles discovery dynamically:

# From ponytail/__init__.py (lines 196-200)

# The system walks SKILLS_DIR and registers each SKILL.md found

from pathlib import Path
SKILLS_DIR = ROOT / "skills"

for skill_path in SKILLS_DIR.iterdir():
    if skill_path.is_dir() and (skill_path / "SKILL.md").exists():
        # Register with Hermes plugin system

        register_skill(skill_path.name, skill_path / "SKILL.md")

Each SKILL.md contains:

• Front-matter with name, description, and optional argument-hint
• Body content explaining ladder rules, intensity levels, and usage guidance

Complete List of Built-In Ponytail Skills

ponytail — Core Lazy Developer Mode

The foundational skill that establishes the "lazy senior developer" philosophy. It enforces a strict priority ladder: YAGNI → stdlib → one-liner → minimal code. Located at skills/ponytail/SKILL.md, this skill controls how aggressively the assistant applies brevity principles.

# Example: activating core ponytail with intensity level

from ponytail import ctx

ctx.execute("/ponytail ultra")  # YAGNI-extreme mode

ctx.execute("/ponytail lite")   # minimal changes only

ponytail-review — Code Review Mode

Purpose: Guides the assistant to perform code reviews with brevity-focused, high-impact feedback.

Source: skills/ponytail-review/SKILL.md

This skill loads the review-specific ruleset, ensuring feedback follows the lazy-first hierarchy rather than over-engineered suggestions.

# Programmatically load the review skill description

from ponytail import ctx

review_md = ctx.load_skill("ponytail-review")
print(review_md[:200])  # Preview skill definition

ponytail-help — Documentation & Reference

Purpose: Provides usage hints, quick-reference documentation, and system guidance for Ponytail itself.

Source: skills/ponytail-help/SKILL.md

Use this skill when you need to understand available commands, intensity levels, or how to structure skill invocations.

ponytail-gain — Improvement-Oriented Mode

Purpose: Directs the assistant toward gain-type improvements—performance optimizations, feature additions, or capability extensions—while maintaining the lazy-first philosophy.

Source: skills/ponytail-gain/SKILL.md

Unlike the core mode which primarily restricts, ponytail-gain identifies genuine value-adds that justify deviating from pure minimalism.

ponytail-debt — Technical Debt Reduction

Purpose: Focuses specifically on identifying and reducing technical debt using minimal-code approaches.

Source: skills/ponytail-debt/SKILL.md

This skill applies the YAGNI ladder to refactoring decisions, preventing "debt reduction" from becoming over-engineering in disguise.

ponytail-audit — Consistency & Compliance Mode

Purpose: Provides audit capabilities for checking consistency, security, and code-style compliance under lazy senior dev constraints.

Source: skills/ponytail-audit/SKILL.md

The audit skill evaluates codebases without violating the core minimalism principles—no 50-page audit reports when a three-bullet summary suffices.

Adding Custom Ponytail Skills

The registration system in ponytail/__init__.py automatically discovers new skills. To extend Ponytail:

1. Create a new sub-directory under skills/
2. Add a SKILL.md file with proper front-matter
3. Restart the system—the skill appears as ponytail:<skill-name>

No code changes required. The Hermes integration handles command routing automatically.

Skill Intensity Levels

All core Ponytail skills support intensity modifiers that control strictness:

Level	Behavior
lite	Minimal changes, maximum preservation
(default)	Balanced application of YAGNI ladder
ultra	YAGNI-extreme, aggressive minimalism

Pass these as arguments: /ponytail-review ultra or programmatically via ctx.execute().

Summary

• Six built-in skills: ponytail, ponytail-review, ponytail-help, ponytail-gain, ponytail-debt, ponytail-audit
• Discovery mechanism: ponytail/__init__.py scans skills/ for SKILL.md files (lines 196-200)
• Architecture: Markdown-based definitions with Hermes plugin registration
• Extensibility: Drop-in skill creation with automatic command exposure
• Intensity control: lite, default, and ultra modes adjust YAGNI strictness

Frequently Asked Questions

How do I check which Ponytail skills are available on my system?

The ponytail-help skill lists all registered skills. Programmatically, enumerate SKILLS_DIR in ponytail/__init__.py or call ctx.list_skills() if exposed by your Hermes wrapper. Each skill directory containing SKILL.md is automatically registered at startup.

Can I use multiple Ponytail skills simultaneously?

Yes. Skills compose through sequential commands. Execute /ponytail-review followed by /ponytail-debt to apply both review and debt-reduction lenses. The ctx.execute() method processes each skill's SKILL.md into the active prompt context.

What makes a valid SKILL.md file for Ponytail?

Valid files require YAML front-matter with name and description fields, plus Markdown body content defining behavior. Optional argument-hint specifies accepted parameters like intensity levels. Place the file in skills/<skill-name>/SKILL.md—the registration loop handles the rest.

How do I override or customize a built-in skill?

Copy the existing SKILL.md from skills/<skill-name>/ to a new directory with your modified version, or edit in place. The registration system uses the first match found per directory scan order. For persistent customizations, create namespaced variants like skills/my-ponytail/SKILL.md.

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