Review a diff for over-engineering. Finds what to delete: reinvented stdlib, needless deps, speculative abstractions. One line per finding.
npx skills add https://github.com/DietrichGebert/ponytail --skill ponytail-review
Review diffs for unnecessary complexity. One line per finding: location, what
to cut, what replaces it. The diff's best outcome is getting shorter.
L<line>: <tag> <what>. <replacement>., or <file>:L<line>: ... for
multi-file diffs.
Tags:
delete: dead code, unused flexibility, speculative feature. Replacement: nothing.stdlib: hand-rolled thing the standard library ships. Name the function.native: dependency or code doing what the platform already does. Name the feature.yagni: abstraction with one implementation, config nobody sets, layer with one caller.shrink: same logic, fewer lines. Show the shorter form.❌ "This EmailValidator class might be more complex than necessary, have you
considered whether all these validation rules are needed at this stage?"
✅ L12-38: stdlib: 27-line validator class. "@" in email, 1 line, real validation is the confirmation mail.
✅ L4: native: moment.js imported for one format call. Intl.DateTimeFormat, 0 deps.
✅ repo.py:L88: yagni: AbstractRepository with one implementation. Inline it until a second one exists.
✅ L52-71: delete: retry wrapper around an idempotent local call. Nothing replaces it.
✅ L30-44: shrink: manual loop builds dict. dict(zip(keys, values)), 1 line.
End with the only metric that matters: net: -<N> lines possible.
If there is nothing to cut, say Lean already. Ship. and stop.
Scope: over-engineering and complexity only. Correctness bugs, security holes,
and performance are explicitly out of scope. Route them to a normal review
pass, not this one. A single smoke test or assert-based
self-check is the ponytail minimum, not bloat, never flag it for deletion.
Does not apply the fixes, only lists them.
"stop ponytail-review" or "normal mode": revert to verbose review style.
Use when receiving code review feedback, before implementing suggestions, especially if feedback seems unclear or technically questionable - requires technical rigor and verification, not performative agreement or blind implementation
Use when completing tasks, implementing major features, or before merging to verify work meets requirements
Execute git commit with conventional commit message analysis, intelligent staging, and message generation. Use when user asks to commit changes, create a git commit, or mentions "/commit". Supports: (1) Auto-detecting type and scope from changes, (2) Generating conventional commit messages from diff, (3) Interactive commit with optional type/scope/description overrides, (4) Intelligent file staging for logical grouping
Comprehensive GitHub code review with AI-powered swarm coordination
Behavioral guidelines to reduce common LLM coding mistakes. Use when writing, reviewing, or refactoring code to avoid overcomplication, make surgical changes, surface assumptions, and define verifiable success criteria.
Use this skill to review code. It supports both local changes (staged or working tree) and remote Pull Requests (by ID or URL). It focuses on correctness, maintainability, and adherence to project standards.
Refactor bloated AGENTS.md, CLAUDE.md, or similar agent instruction files to follow progressive disclosure principles. Splits monolithic files into organized, linked documentation.
Create high-quality git commits: review/stage intended changes, split into logical commits, and write clear commit messages (including Conventional Commits). Use when the user asks to commit, craft a commit message, stage changes, or split work into multiple commits.
Take dietrichgebert/ponytail-review from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.