# How to View the Ponytail Benchmark Impact Scoreboard Using `/ponytail-gain`

> Easily view the Ponytail benchmark impact scoreboard with the `/ponytail-gain` command. See pre-computed median savings for five workloads and three Claude models instantly.

- Repository: [DietrichGebert/ponytail](https://github.com/DietrichGebert/ponytail)
- Tags: how-to-guide
- Published: 2026-09-07

---

**To view Ponytail's benchmark impact scoreboard, type `/ponytail-gain` in your chat interface—this displays pre-computed median savings across five typical workloads and three Claude models without analyzing your current repository.**

The `/ponytail-gain` command is one of six built-in **Ponytail skills** in the DietrichGebert/ponytail repository. Unlike repo-specific diagnostics, this command serves a static scoreboard showing aggregate performance data collected from Ponytail's standardized benchmark suite.

## What `/ponytail-gain` Displays

The scoreboard presents **median results** from five representative programming tasks:

- Email validator
- Debounce function
- CSV sum utility
- Countdown timer
- Rate limiter

These benchmarks ran against three Anthropic models: **Haiku**, **Sonnet**, and **Opus**.

The output format renders as an ASCII bar chart comparing **no-skill** (baseline) versus **ponytail** (optimized) approaches:

```

pony tail gain                     benchmark median · 5 tasks · 3 models

Lines of code   no‑skill  ████████████████████  100%
                ponytail  ██▌·················    6–20%   ▼ 80–94%
Cost            no‑skill  ████████████████████  100%
                ponytail  █████▌··············   23–53%  ▼ 47–77%
Speed           ponytail  ▸ 3–6× faster

```

These figures represent **aggregate medians**, not predictions for your specific codebase. For repository-specific analysis, use `/ponytail-debt` or `/ponytail-audit` instead.

## Technical Architecture

The `/ponytail-gain` command flows through four coordinated components:

| Component | File Path | Purpose |
|-----------|-----------|---------|
| **Skill definition** | [`skills/ponytail-gain/SKILL.md`](https://github.com/DietrichGebert/ponytail/blob/main/skills/ponytail-gain/SKILL.md) | Markdown file containing scoreboard content and ASCII rendering (lines 29-34) |
| **Command metadata** | [`commands/ponytail-gain.toml`](https://github.com/DietrichGebert/ponytail/blob/main/commands/ponytail-gain.toml) | TOML configuration with one-shot LLM prompt |
| **Command registration** | [`pi-extension/index.js`](https://github.com/DietrichGebert/ponytail/blob/main/pi-extension/index.js) (lines 59-62) | JavaScript bridge registering the slash command |
| **Plugin manifest** | [`plugin.yaml`](https://github.com/DietrichGebert/ponytail/blob/main/plugin.yaml) (line 13) | Skill directory declaration for host discovery |

### Execution Flow

1. User types `/ponytail-gain` (or synonym "ponytail gain")
2. **Hermes/Qoder** host receives the slash command
3. **pi-extension** handler invokes `sendAlias("/skill:ponytail-gain", "", ctx)`
4. Skill engine loads [`SKILL.md`](https://github.com/DietrichGebert/ponytail/blob/main/SKILL.md) and returns the static scoreboard

Because the data is **pre-computed** and stored in [`SKILL.md`](https://github.com/DietrichGebert/ponytail/blob/main/SKILL.md), the response requires no repository analysis, ensuring sub-second, side-effect-free operation.

## Usage Examples

### Chat Interface Invocation

```text
User: /ponytail-gain
Bot:  ponytail gain ... (ASCII scoreboard rendered)

```

### Programmatic API Call

```javascript
// Register command (already done in pi-extension/index.js)
pi.registerCommand("ponytail-gain", {
  description: "Run /skill:ponytail-gain",
  handler: (_args, ctx) => sendAlias("/skill:ponytail-gain", "", ctx),
});

// Execute programmatically
await pi.runCommand("ponytail-gain", {}, context);

```

### Custom Alias Creation

Add shorthand triggers in [`pi-extension/index.js`](https://github.com/DietrichGebert/ponytail/blob/main/pi-extension/index.js):

```javascript
pi.aliasCommand("pgain", "ponytail-gain");   // Now "/pgain" works identically

```

## Key Files Reference

- **[`skills/ponytail-gain/SKILL.md`](https://github.com/DietrichGebert/ponytail/blob/main/skills/ponytail-gain/SKILL.md)** — Complete skill description and static scoreboard markup
- **[`commands/ponytail-gain.toml`](https://github.com/DietrichGebert/ponytail/blob/main/commands/ponytail-gain.toml)** — LLM one-shot prompt preventing mode changes
- **[`pi-extension/index.js`](https://github.com/DietrichGebert/ponytail/blob/main/pi-extension/index.js)** — Command registration and alias handling
- **[`plugin.yaml`](https://github.com/DietrichGebert/ponytail/blob/main/plugin.yaml)** — Skill discovery metadata
- **[`README.md`](https://github.com/DietrichGebert/ponytail/blob/main/README.md)** (around line 316) — End-user quick reference

## Summary

- **`/ponytail-gain`** displays **pre-computed benchmark medians**, not repo-specific data
- The scoreboard covers **5 tasks × 3 models** with metrics for code volume, cost, and speed
- Response time is near-instant because no repository analysis occurs
- For actual codebase measurements, use **`/ponytail-debt`** or **`/ponytail-audit`** instead

## Frequently Asked Questions

### Does `/ponytail-gain` analyze my current codebase?

No. The command only renders static data from [`skills/ponytail-gain/SKILL.md`](https://github.com/DietrichGebert/ponytail/blob/main/skills/ponytail-gain/SKILL.md). It never reads your repository files. For actual analysis of your code, use `/ponytail-debt` or `/ponytail-audit`.

### Where do the benchmark numbers come from?

The medians originate from Ponytail's standardized benchmark suite in the `benchmarks/` folder, as documented in the repository README. These runs tested consistent prompts across Haiku, Sonnet, and Opus models.

### Can I customize which models or tasks appear in the scoreboard?

Not without modifying the skill definition. The scoreboard is hardcoded in [`SKILL.md`](https://github.com/DietrichGebert/ponytail/blob/main/SKILL.md) lines 29-34. To create variant views, you would need to fork the skill or build a custom `/ponytail-gain` variant.

### Why is `/ponytail-gain` faster than `/ponytail-debt`?

`/ponytail-gain` serves cached markdown content directly from [`SKILL.md`](https://github.com/DietrichGebert/ponytail/blob/main/SKILL.md). `/ponytail-debt` performs live repository analysis, token counting, and LLM inference—operations that require significantly more processing time.