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Jatinkrmahato993203
Jatinkrmahato993203COMMUNITY
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ponytail-review

Reviews code diffs for over-engineering and lists what to delete or simplify.

Updated Jul 20, 2026
Search Tags:#diff analysis#code review#refactoring#yagni#dead code#simplification#over-engineering
One-click install
npx skills add https://github.com/Jatinkrmahato993203/crime --skill ponytail-review-jatinkrmahato993203
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ponytail-review
Source: https://github.com/Jatinkrmahato993203/crime/tree/main/skills/ponytail-review
Command: npx skills add https://github.com/Jatinkrmahato993203/crime --skill ponytail-review-jatinkrmahato993203

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Codebases accumulate unnecessary complexity: reinvented standard library functions, speculative abstractions, unused dependencies, and dead flexibility. This Skill performs a focused review pass that finds only what can be cut, keeping diffs lean. ## Core Features & Use Cases - Over-Engineering Detection: Flags hand-rolled utilities the standard library already provides, dependencies duplicating native platform features, and abstractions with a single implementation. - One-Line Findings: Each finding is a single line with location, a tag (delete, stdlib, native, yagni, shrink), what to cut, and what replaces it. - Net-Line Scoring: Ends every review with the total lines that could be removed, or confirms the code is already lean. - Use Case: Before merging a pull request, run this review to catch a 27-line email validator that could be one line, or a moment.js import replaceable by Intl.DateTimeFormat. ## Quick Start Ask the AI to review this diff for over-engineering and tell me what can be deleted.

Frequently Asked Questions about ponytail-review

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I review code for over-engineering?▼

Run this review pass on a diff to get one-line findings tagged as delete, stdlib, native, yagni, or shrink. Each finding states the location, what to cut, and what replaces it, ending with a net line-reduction score.

What kinds of complexity does this code review detect?▼

It detects reinvented standard library functions, dependencies duplicating native platform features, abstractions with a single implementation, dead code, unused configuration, and logic that can be expressed in fewer lines.

Does this review catch bugs or security issues?▼

No. Correctness bugs, security holes, and performance problems are explicitly out of scope and should be routed to a normal review pass. This review only hunts unnecessary complexity.

Will this skill apply the simplifications it suggests?▼

No. It only lists findings with locations and suggested replacements; it does not modify the code. You decide which cuts to apply.

When should I not use an over-engineering review?▼

Skip it when you need correctness, security, or performance feedback, since those are out of scope. It also never flags minimal smoke tests or assert-based self-checks, which it treats as the acceptable minimum.

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