Ponytail Gain: AI Skill for Efficient Coding Benchmarks

Ponytail Gain: AI Skill for Efficient Coding Benchmarks

Ponytail Gain is an AI skill designed to display a scoreboard showing the impact of AI agents on coding efficiency. It presents benchmark medians for five common tasks across three models, illustrating potential reductions in code volume, cost savings, and speed improvements compared to a non-optimized baseline. The display is a one-shot view and does not alter any existing settings or store persistent data.

What this skill does

• Renders a plain ASCII scoreboard that compares lines of code, cost, and speed with benchmarks. The maximum impact is displayed as measured medians, providing clarity on AI efficiency impacts.
• Focuses on five everyday tasks: email validation, debounce, CSV sum, countdown timer, and rate limiter.
• Benchmarks these tasks using three models: Haiku, Sonnet, and Opus.
• Directs users to specific repository paths for additional context: /ponytail-debt for unfinished aspects and /ponytail-audit for potential improvements.
• Operates without changing modes or writing persistent data files.

Who it is for

• Developers working with AI coding agents like Claude Code, Cursor, and Codex.
• Engineering teams seeking to track and measure coding efficiency improvements when integrating AI assistance.

Use cases

• Development teams wishing to assess the quantitative impact of using AI agents in their coding processes.
• Software engineers comparing productivity metrics before and after AI integration.
• Technical leads and project managers evaluating the cost-effectiveness of adopting AI agent skills in their coding environments.

Technical details

• Utilizes the agent-skills framework to interface with AI applications.
• Integrates seamlessly with AI-capable coding environments such as Claude Code, Cursor, and Codex.
• Accesses source data from the benchmarks/ directory and README file for accuracy.
• Ensures compliance with operational boundaries by delivering a one-shot, non-invasive display.

Source & Licence

This package is built on open-source work published by 0xwilliamortiz (0xwilliamortiz/ponytail-improved) and distributed under MIT. The original licence text and copyright notice are included in your download.

Personal and commercial use, modification and redistribution are permitted, provided the original copyright and licence notice are retained.

Your purchase covers curation, licence verification, packaging, documentation and instant delivery. It does not grant exclusive rights to the underlying open-source code, which remains available under its original licence.

Delivery & Support

• Delivery: instant — a secure download link is emailed to you as soon as payment is confirmed.
• Format: ZIP archive containing the skill files, documentation and the original licence.
• Support: support@mcpcart.com — we aim to reply within 2 business days.
• Updates: updates are included only where stated on this page.

Refunds

This is a digital product delivered immediately after purchase. By completing your order you request immediate delivery and acknowledge that, once the download has been accessed, the statutory right to cancel no longer applies to the extent permitted by law. Refund requests are handled in accordance with our published Refund Policy.

Claude, Codex, Gemini and Cursor are trademarks of their respective owners. MCP Cart is an independent marketplace and is not affiliated with, endorsed by, or sponsored by any of them. Compatibility references describe interoperability only.