ponytail

1. Project Overview

ponytail is a lightweight skill/ruleset for AI coding agents (Claude Code, Codex, Cursor, and 20+ other platforms) that trains the agent to write the minimum code necessary — reusing what already exists before generating anything new — cutting AI-generated code volume by roughly half without sacrificing safety.

2. Background & Positioning

• Core mission: Modern coding agents tend to over-produce — wrapping simple tasks in extra abstractions, reinventing utilities that already exist, or ignoring native platform features. ponytail encodes a deliberate "decision ladder" the agent must climb before writing any code, so the default behavior becomes restraint rather than elaboration. The philosophy is summarized by its tagline: "He says nothing. He writes one line. It works."
• How it differs from similar projects: Rather than being a code generator, linter, or style guide, ponytail is a pre-generation ruleset — it changes what the agent decides to write in the first place, not how the output is formatted afterward. It is explicitly complementary to tools like "caveman" (which shrinks agent conversation, while ponytail shrinks agent code), and it ships as portable rule files/hooks rather than a standalone application, so it can be installed into almost any AI coding tool.

3. Feature Categories

🪜 Decision Ladder

7 sequential checks the agent runs before writing code — deciding whether a feature is needed at all, already exists, or can be solved with stdlib, native platform features, or an installed dependency.

• Does this need to exist? (YAGNI check)
• Already in this codebase?
• Does the standard library do it?
• Is there a native platform feature (e.g. <input type="date">)?
• Is there an installed dependency that already does it?
• Can it be a one-liner?
• Only if all else fails: write minimum viable custom code

🧰 Skills / Commands

5 slash-style commands that let developers inspect and enforce minimalism directly from the agent session.

• /ponytail-review — flags over-engineering in the current diff
• /ponytail-audit — scans an entire repository for unnecessary code
• /ponytail-debt — collects deferred ponytail: shortcuts into a ledger
• /ponytail-gain — shows benchmark metrics for the current session
• /ponytail-help — quick reference

🎚️ Intensity Levels

4 configurable modes controlling how aggressively the agent trims code, switchable per-project or per-session.

• lite — gentle nudge, preserves verbosity
• full — balanced reduction (default)
• ultra — aggressive minimalism
• off — disables the ruleset

🔌 Platform Integrations

20+ supported AI coding agents/editors, each with a native install path (plugin manager, extension, or copied rule file).

• Claude Code, Codex, GitHub Copilot CLI
• Cursor, Windsurf, Cline
• Gemini CLI, Devin CLI, OpenCode, Aider, Zed, and more

4. Key Highlights

• ~54% less code, same safety — measured on real feature tickets against a FastAPI + React template, ponytail cut lines of code by 54%, tokens by 22%, cost by 20%, and time by 27%, while keeping safety (validation, error handling, security, accessibility) at 100%.
• Portable across the agent ecosystem — ships as AGENTS.md plus platform-specific rule copies (.cursor/rules/, .windsurf/rules/, .github/copilot-instructions.md, etc.), so the same discipline applies whichever coding agent a team uses.
• Deliberate order of operations — the decision ladder only runs after the agent understands the problem, so minimalism never comes at the expense of correctness.
• Built-in review tooling — /ponytail-review and /ponytail-audit turn "less code" from a one-time habit into an ongoing, auditable practice across a codebase.
• Zero mandatory configuration — works out of the box at full intensity; teams that want more or less aggressiveness can tune via an environment variable or config file.
• Clean uninstall path — a dedicated uninstall.js script clears mode flags, config files, and status-line integrations, so adopting it is low-risk to reverse.

5. Use Cases by Role

• General developers: Get agent-generated code that reads closer to what a careful human engineer would write — fewer unnecessary wrapper components, dependencies, or abstractions to review and maintain.
• Project managers / tech leads: Use /ponytail-gain and the published benchmark methodology to quantify the cost and time savings an AI-assisted workflow is actually delivering.
• DevOps/SRE, security engineers, data/research scientists: Not directly targeted — ponytail is agent-agnostic to domain, but its safety guarantees (validation, error handling, security, accessibility are "never minimized") mean it does not trade safety for brevity when applied in any of these contexts.

6. Getting Started

Find what you need — start with the core ruleset and command reference:

/ponytail-help

Install / integrate — for Claude Code:

/plugin marketplace add DietrichGebert/ponytail
/plugin install ponytail@ponytail

Other platforms (Codex, Gemini CLI, Cursor, Windsurf, etc.) each have an equivalent one-line install command, or the rule files can be copied directly from the repository (.cursor/rules/, .windsurf/rules/, AGENTS.md).

Contribute — validate rule consistency and run tests before submitting changes:

node scripts/check-rule-copies.js
npm test

7. Project Structure

ponytail/
├── AGENTS.md                 # Core ruleset (auto-loaded by many platforms)
├── skills/                   # Executable skills (review, audit, debt, gain, help)
├── hooks/                    # Lifecycle hooks (platform-specific)
│   ├── hooks.json
│   └── qoder-hooks.json
├── .cursor/rules/            # Cursor/Windsurf/Cline rule copies
├── .windsurf/rules/
├── .clinerules/
├── .github/copilot-instructions.md
├── scripts/
│   ├── check-rule-copies.js  # Validate consistency across agent copies
│   ├── build-openclaw-skills.js
│   └── uninstall.js          # Clean up local state files
├── benchmarks/                # Measurement methodology and results
├── examples/                  # Before/after code samples
└── LICENSE                    # MIT

Key files: AGENTS.md holds the canonical decision-ladder ruleset that every platform-specific rule file mirrors; scripts/check-rule-copies.js guards against those copies drifting out of sync.

8. Related Ecosystem

• Depends on: The AI coding agent it is installed into (Claude Code, Codex, Cursor, Gemini CLI, etc.) — ponytail itself is a ruleset/skill, not a standalone runtime. Node.js is required on the PATH for lifecycle hooks on platforms like Claude Code and Codex.
• Complementary projects: "caveman," a companion ruleset that reduces agent conversational verbosity, pairs with ponytail (which reduces agent code verbosity) without overlapping functionality.

9. License

MIT License.

• ✅ Free to use, copy, modify, and distribute, including for commercial purposes.
• ✅ Can be integrated into proprietary or closed-source AI tooling.
• ❌ Comes with no warranty; the author is not liable for damages from use.
• ℹ️ The original copyright notice and license text must be retained in copies or substantial portions of the software.

10. FAQ

Q: Does ponytail conflict with other agent-behavior tools like "caveman"?
A: No — caveman reduces agent conversation length, ponytail reduces the code the agent writes. They are designed to be used together.

Q: Do the benchmark numbers come from synthetic tests?
A: No — the primary benchmark was measured on real feature tickets against tiangolo's full-stack FastAPI + React template using Claude Haiku 4.5, not isolated single-shot generations.

Q: Does minimalism come at the cost of safety or accessibility?
A: No — validation, error handling, security, and accessibility are explicitly excluded from what the decision ladder is allowed to trim.

Q: How do I change how aggressive ponytail is?
A: Run /ponytail [lite | full | ultra | off], or set PONYTAIL_DEFAULT_MODE via environment variable or ~/.config/ponytail/config.json.

Q: How do I remove ponytail cleanly?
A: Run node scripts/uninstall.js before using your platform's plugin-removal command, to clear mode flags, config files, and status-line entries.

11. Quick Links

• Repository: https://github.com/DietrichGebert/ponytail
• Core ruleset: AGENTS.md in the repository root
• Contributing: node scripts/check-rule-copies.js and npm test (see repository root)
• License: MIT (LICENSE file in the repository)

12. Summary

ponytail turns "write less code" from an aspiration into an enforceable, measurable habit for AI coding agents, cutting generated code volume by roughly half while holding safety and accessibility guarantees fixed. It is best suited for teams that rely heavily on AI coding agents across their workflow and want the output to stay lean and maintainable — install it once across your preferred agent platform and its decision ladder runs automatically on every task.