m15-anti-pattern

Identify and resolve common Rust code anti-patterns during review.

zhanghandong/rust-skillsUpdated Apr 8, 2026

Works with

Claude CodeCursorClineWindsurfCodexGooseGitHub CopilotZed

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Install Skill

Run in your terminal

$npx skills add https://github.com/zhanghandong/rust-skills --skill m15-anti-pattern

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What it does

  • Covers eight major anti-patterns with explanations and idiomatic alternatives, including excessive cloning, unwrap in production, and fighting the borrow checker

  • Provides a thinking framework to distinguish symptoms from root causes and trace issues to underlying design problems

  • Includes quick reference tables for beginner mistakes, code smells, common error patterns, and deprecated approaches with fixes

  • Links anti

Category

Productivity

Last updated

Apr 8, 2026

Installation Guide

How to use m15-anti-pattern on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your machine
  • Node.js 16+ with npm — verify with node --version
  • Active project directory where you want to add m15-anti-pattern
2

Run the install command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/zhanghandong/rust-skills --skill m15-anti-pattern

Fetches m15-anti-pattern from zhanghandong/rust-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI shows a list of agents. Use arrow keys and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ────────────────
│ · Cline · Codex · Goose · Windsurf
│ ●Cursor(selected)
│ · Cursor · Aider · Continue
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/m15-anti-pattern

Restart Cursor to activate m15-anti-pattern. Access via /m15-anti-pattern in your agent's command palette.

Security Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.

Documentation

Anti-Patterns

Layer 2: Design Choices

Core Question

Is this pattern hiding a design problem?

When reviewing code:

  • Is this solving the symptom or the cause?
  • Is there a more idiomatic approach?
  • Does this fight or flow with Rust?

Anti-Pattern → Better Pattern

Anti-Pattern Why Bad Better
.clone() everywhere Hides ownership issues Proper references or ownership
.unwrap() in production Runtime panics ?, expect, or handling
Rc when single owner Unnecessary overhead Simple ownership
unsafe for convenience UB risk Find safe pattern
OOP via Deref Misleading API Composition, traits
Giant match arms Unmaintainable Extract to methods
String everywhere Allocation waste &str, Cow<str>
Ignoring #[must_use] Lost errors Handle or let _ =

Thinking Prompt

When seeing suspicious code:

  1. Is this symptom or cause?

    • Clone to avoid borrow? → Ownership design issue
    • Unwrap "because it won't fail"? → Unhandled case
  2. What would idiomatic code look like?

    • References instead of clones
    • Iterators instead of index loops
    • Pattern matching instead of flags
  3. Does this fight Rust?

    • Fighting borrow checker → restructure
    • Excessive unsafe → find safe pattern

Trace Up ↑

To design understanding:

"Why does my code have so many clones?"
    ↑ Ask: Is the ownership model correct?
    ↑ Check: m09-domain (data flow design)
    ↑ Check: m01-ownership (reference patterns)
Anti-Pattern Trace To Question
Clone everywhere m01-ownership Who should own this data?
Unwrap everywhere m06-error-handling What's the error strategy?
Rc everywhere m09-domain Is ownership clear?
Fighting lifetimes m09-domain Should data structure change?

Trace Down ↓

To implementation (Layer 1):

"Replace clone with proper ownership"
    ↓ m01-ownership: Reference patterns
    ↓ m02-resource: Smart pointer if needed

"Replace unwrap with proper handling"
    ↓ m06-error-handling: ? operator
    ↓ m06-error-handling: expect with message

Top 5 Beginner Mistakes

Rank Mistake Fix
1 Clone to escape borrow checker Use references
2 Unwrap in production Propagate with ?
3 String for everything Use &str
4 Index loops Use iterators
5 Fighting lifetimes Restructure to own data

Code Smell → Refactoring

Smell Indicates Refactoring
Many .clone() Ownership unclear Clarify data flow
Many .unwrap() Error handling missing Add proper handling
Many pub fields Encapsulation broken Private + accessors
Deep nesting Complex logic Extract methods
Long functions Multiple responsibilities Split
Giant enums Missing abstraction Trait + types

Common Error Patterns

Error Anti-Pattern Cause Fix
E0382 use after move Cloning vs ownership Proper references
Panic in production Unwrap everywhere ?, matching
Slow performance String for all text &str, Cow
Borrow checker fights Wrong structure Restructure
Memory bloat Rc/Arc everywhere Simple ownership

Deprecated → Better

Deprecated Better
Index-based loops .iter(), .enumerate()
collect::<Vec<_>>() then iterate Chain iterators
Manual unsafe cell Cell, RefCell
mem::transmute for casts as or TryFrom
Custom linked list Vec, VecDeque
lazy_static! std::sync::OnceLock

Quick Review Checklist

  • No .clone() without justification
  • No .unwrap() in library code
  • No pub fields with invariants
  • No index loops when iterator works
  • No String where &str suffices
  • No ignored #[must_use] warnings
  • No unsafe without SAFETY comment
  • No giant functions (>50 lines)

Related Skills

When See
Ownership patterns m01-ownership
Error handling m06-error-handling
Mental models m14-mental-model
Performance m10-performance

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Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Steps

  1. 1Install product management skill
  2. 2Start with user story generation for known feature
  3. 3Progress to competitive analysis: research 2-3 competitors
  4. 4Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5Draft stakeholder communications and refine based on feedback
  6. 6Build template library for recurring PM tasks
  7. 7Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use when

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid when

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Related Skills

Reviews

4.755 reviews
  • L
    Layla WhiteDec 20, 2024

    m15-anti-pattern is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • L
    Layla TandonDec 8, 2024

    We added m15-anti-pattern from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • D
    Diego TandonDec 8, 2024

    Useful defaults in m15-anti-pattern — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • L
    Liam AbebeDec 8, 2024

    I recommend m15-anti-pattern for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • P
    Pratham WareDec 4, 2024

    I recommend m15-anti-pattern for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Y
    Yuki SrinivasanNov 27, 2024

    Useful defaults in m15-anti-pattern — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • N
    Neel BansalNov 27, 2024

    We added m15-anti-pattern from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • L
    Layla SrinivasanNov 11, 2024

    Keeps context tight: m15-anti-pattern is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • L
    Layla WangOct 18, 2024

    m15-anti-pattern has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • L
    Lucas DialloOct 18, 2024

    Solid pick for teams standardizing on skills: m15-anti-pattern is focused, and the summary matches what you get after install.

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