How to Use the ponytail-audit Skill for Repository-Wide Over-Engineering Detection

The ponytail-audit skill is a built-in Ponytail command that performs a repository-wide scan to identify and rank over-engineered code patterns, suggesting deletions, standard library replacements, and simplifications without automatically modifying files.

The ponytail-audit skill provides a one-shot audit capability for the Ponytail framework, allowing developers to analyze entire codebases for complexity that violates the YAGNI (You Aren't Gonna Need It) principle. Unlike standard code reviews that focus on bugs or security vulnerabilities, this skill specifically targets unnecessary abstractions, redundant dependencies, and opportunities to leverage native platform features according to the DietrichGebert/ponytail source code.

Architecture of the ponytail-audit Skill

The ponytail-audit skill consists of three integrated components that handle definition, registration, and execution.

Skill Definition in SKILL.md

The skill manifest at skills/ponytail-audit/SKILL.md defines the audit's purpose, tags, hunt criteria, output format, and operational boundaries. This markdown file declares the five classification tags used during analysis: delete, stdlib, native, yagni, and shrink.

Command Registration in pi-extension/index.js

In pi-extension/index.js, the Ponytail extension system registers the command name ponytail-audit (and the alias ponytail-audit) and maps it to the generic skill handler via the /skill:ponytail-audit route. The registration logic forwards user invocations to the core audit engine while preserving command context.

Core Audit Logic via ponytail-review

The actual analysis executes through the ponytail-review module with the "repo-wide" flag enabled. As implemented in the core source, the engine traverses the file tree, applies tag-based heuristics, ranks findings by impact, and calculates potential line and dependency reductions.

How to Run the ponytail-audit Skill

You can invoke the skill through chat interfaces, programmatically, or via command line.

Chat Interface Invocation

In supported chat environments (Hermes, Qoder, etc.), trigger the audit with the forward-slash command or its alias:

User: /ponytail-audit
Bot:  delete unused_logger. Remove. [src/logger.js]
      stdlib json_parse. Use JSON.parse(). [src/util.js]
      net: -12 lines, -2 deps possible.

Command Line Usage

Use the Ponytail launcher to execute the audit directly from your terminal:

# The `ponytail` launcher forwards the sub-command to the skill

ponytail audit

Programmatic Execution in Node.js

For custom integrations, register and call the skill through the Ponytail extension object following the pattern in pi-extension/index.js:

// Register the command
pi.registerCommand('ponytail-audit', {
  description: 'Run /skill:ponytail-audit',
  handler: (_args, ctx) => sendAlias('/skill:ponytail-audit', '', ctx)
});

// Execute the audit
await pi.runCommand('ponytail-audit', '', ctx);

Understanding the Audit Output Format

The ponytail-audit skill returns findings in a standardized, machine-readable format:

<tag> <what to cut>. <replacement>. [path]
net: -<N> lines, -<M> deps possible.

Each finding follows this structure:

• Tag: Classifies the issue as delete, stdlib, native, yagni, or shrink
• What to cut: Specific component or pattern identified for removal
• Replacement: Recommended alternative or "Remove" if deletion suffices
• Path: File location in brackets [src/file.js]
• Net impact: Summary of line count and dependency reductions possible

Scope and Boundaries of ponytail-audit

The skill operates strictly as an over-engineering detector. It does not identify bugs, security vulnerabilities, or performance bottlenecks—those require a standard /ponytail-review pass. The audit runs as a one-shot operation: it generates the report without applying fixes automatically.

To terminate the skill's active mode, issue the command stop ponytail-audit or switch back to normal mode in your interface.

Summary

• The ponytail-audit skill performs repo-wide scans for over-engineered code patterns in the DietrichGebert/ponytail framework
• Three components power the skill: the SKILL.md manifest, registration logic in pi-extension/index.js, and the core ponytail-review engine
• Invoke via /ponytail-audit (or the alias ponytail-audit) in chat, ponytail audit in CLI, or pi.runCommand() programmatically
• Output uses five tags (delete, stdlib, native, yagni, shrink) to categorize simplification opportunities with line and dependency impact metrics
• The skill is read-only and excludes security, bug, and performance analysis

Frequently Asked Questions

How do I stop the ponytail-audit skill once started?

To exit the skill's dedicated mode, type stop ponytail-audit in your chat interface or explicitly switch back to normal mode. The skill runs as a one-shot audit, so it will automatically complete after generating the report, but the mode persists until you issue the stop command or change contexts.

What is the difference between ponytail-audit and ponytail-review?

While ponytail-review handles general code reviews including bugs and security issues, ponytail-audit specifically targets over-engineering through a repository-wide lens. The audit skill uses the same core engine as the review skill but activates the "repo-wide" flag and filters for YAGNI violations, unnecessary abstractions, and standard library replacement opportunities only.

Can ponytail-audit automatically fix the issues it finds?

No, the ponytail-audit skill is strictly read-only and generates reports without modifying source files. As implemented in the DietrichGebert/ponytail source code, the skill produces ranked findings for developer review but requires manual implementation of the suggested deletions, replacements, or simplifications.

What do the five audit tags mean?

The classification tags defined in skills/ponytail-audit/SKILL.md represent specific over-engineering categories: delete identifies removable dead code; stdlib suggests replacing custom implementations with standard library functions; native recommends platform-specific native features over abstractions; yagni flags speculative complexity violating the "You Aren't Gonna Need It" principle; and shrink targets code that can be reduced in scope or size.

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