m04-zero-cost

zhanghandong/rust-skills · updated Apr 8, 2026

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$npx skills add https://github.com/zhanghandong/rust-skills --skill m04-zero-cost
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summary

Compile-time versus runtime polymorphism: generics, trait objects, and zero-cost abstraction patterns.

  • Distinguishes static dispatch (generics, impl Trait ) from dynamic dispatch ( dyn Trait ), with decision guidance on when each is appropriate based on type knowledge, performance priorities, and collection heterogeneity
  • Maps common type system errors (E0277, E0308, E0599, E0038) to underlying design questions rather than mechanical fixes
  • Covers object safety constraints, monomorphiz
skill.md

Zero-Cost Abstraction

Layer 1: Language Mechanics

Core Question

Do we need compile-time or runtime polymorphism?

Before choosing between generics and trait objects:

  • Is the type known at compile time?
  • Is a heterogeneous collection needed?
  • What's the performance priority?

Error → Design Question

Error Don't Just Say Ask Instead
E0277 "Add trait bound" Is this abstraction at the right level?
E0308 "Fix the type" Should types be unified or distinct?
E0599 "Import the trait" Is the trait the right abstraction?
E0038 "Make object-safe" Do we really need dynamic dispatch?

Thinking Prompt

Before adding trait bounds:

  1. What abstraction is needed?

    • Same behavior, different types → trait
    • Different behavior, same type → enum
    • No abstraction needed → concrete type
  2. When is type known?

    • Compile time → generics (static dispatch)
    • Runtime → trait objects (dynamic dispatch)
  3. What's the trade-off priority?

    • Performance → generics
    • Compile time → trait objects
    • Flexibility → depends

Trace Up ↑

When type system fights back:

E0277 (trait bound not satisfied)
    ↑ Ask: Is the abstraction level correct?
    ↑ Check: m09-domain (what behavior is being abstracted?)
    ↑ Check: m05-type-driven (should use newtype?)
Persistent Error Trace To Question
Complex trait bounds m09-domain Is the abstraction right?
Object safety issues m05-type-driven Can typestate help?
Type explosion m10-performance Accept dyn overhead?

Trace Down ↓

From design to implementation:

"Need to abstract over types with same behavior"
    ↓ Types known at compile time → impl Trait or generics
    ↓ Types determined at runtime → dyn Trait

"Need collection of different types"
    ↓ Closed set → enum
    ↓ Open set → Vec<Box<dyn Trait>>

"Need to return different types"
    ↓ Same type → impl Trait
    ↓ Different types → Box<dyn Trait>

Quick Reference

Pattern Dispatch Code Size Runtime Cost
fn foo<T: Trait>() Static +bloat Zero
fn foo(x: &dyn Trait) Dynamic Minimal vtable lookup
impl Trait return Static +bloat Zero
Box<dyn Trait> Dynamic Minimal Allocation + vtable

Syntax Comparison

// Static dispatch - type known at compile time
fn process(x: impl Display) { }      // argument position
fn process<T: Display>(x: T) { }     // explicit generic
fn get() -> impl Display { }         // return position

// Dynamic dispatch - type determined at runtime
fn process(x: &dyn Display) { }      // reference
fn process(x: Box<dyn Display>) { }  // owned

Error Code Reference

Error Cause Quick Fix
E0277 Type doesn't impl trait Add impl or change bound
E0308 Type mismatch Check generic params
E0599 No method found Import trait with use
E0038 Trait not object-safe Use generics or redesign

Decision Guide

Scenario Choose Why
Performance critical Generics Zero runtime cost
Heterogeneous collection dyn Trait Different types at runtime
Plugin architecture dyn Trait Unknown types at compile
Reduce compile time dyn Trait Less monomorphization
Small, known type set enum No indirection

Object Safety

A trait is object-safe if it:

  • Doesn't have Self: Sized bound
  • Doesn't return Self
  • Doesn't have generic methods
  • Uses where Self: Sized for non-object-safe methods

Anti-Patterns

Anti-Pattern Why Bad Better
Over-generic everything Compile time, complexity Concrete types when possible
dyn for known types Unnecessary indirection Generics
Complex trait hierarchies Hard to understand Simpler design
Ignore object safety Limits flexibility Plan for dyn if needed

Related Skills

When See
Type-driven design m05-type-driven
Domain abstraction m09-domain
Performance concerns m10-performance
Send/Sync bounds m07-concurrency
how to use m04-zero-cost

How to use m04-zero-cost 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 development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add m04-zero-cost
2

Execute installation 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 m04-zero-cost

The skills CLI fetches m04-zero-cost from GitHub repository zhanghandong/rust-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/m04-zero-cost

Reload or restart Cursor to activate m04-zero-cost. Access the skill through slash commands (e.g., /m04-zero-cost) or your agent's skill management interface.

Security & Verification 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 development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

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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

Installation Steps

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

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.771 reviews
  • Dhruvi Jain· Dec 24, 2024

    Solid pick for teams standardizing on skills: m04-zero-cost is focused, and the summary matches what you get after install.

  • Isabella Singh· Dec 24, 2024

    We added m04-zero-cost from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Isabella Khanna· Dec 24, 2024

    Keeps context tight: m04-zero-cost is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Daniel Khan· Dec 24, 2024

    Solid pick for teams standardizing on skills: m04-zero-cost is focused, and the summary matches what you get after install.

  • Maya Robinson· Dec 20, 2024

    m04-zero-cost has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Benjamin Johnson· Dec 16, 2024

    m04-zero-cost has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Zaid Diallo· Dec 4, 2024

    m04-zero-cost fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Rahul Santra· Nov 23, 2024

    m04-zero-cost is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Zaid Ghosh· Nov 23, 2024

    m04-zero-cost has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Oshnikdeep· Nov 15, 2024

    We added m04-zero-cost from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

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