m10-performance▌
actionbook/rust-skills · updated Apr 8, 2026
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Layer 2: Design Choices
Performance Optimization
Layer 2: Design Choices
Core Question
What's the bottleneck, and is optimization worth it?
Before optimizing:
- Have you measured? (Don't guess)
- What's the acceptable performance?
- Will optimization add complexity?
Performance Decision → Implementation
| Goal | Design Choice | Implementation |
|---|---|---|
| Reduce allocations | Pre-allocate, reuse | with_capacity, object pools |
| Improve cache | Contiguous data | Vec, SmallVec |
| Parallelize | Data parallelism | rayon, threads |
| Avoid copies | Zero-copy | References, Cow<T> |
| Reduce indirection | Inline data | smallvec, arrays |
Thinking Prompt
Before optimizing:
-
Have you measured?
- Profile first → flamegraph, perf
- Benchmark → criterion, cargo bench
- Identify actual hotspots
-
What's the priority?
- Algorithm (10x-1000x improvement)
- Data structure (2x-10x)
- Allocation (2x-5x)
- Cache (1.5x-3x)
-
What's the trade-off?
- Complexity vs speed
- Memory vs CPU
- Latency vs throughput
Trace Up ↑
To domain constraints (Layer 3):
"How fast does this need to be?"
↑ Ask: What's the performance SLA?
↑ Check: domain-* (latency requirements)
↑ Check: Business requirements (acceptable response time)
| Question | Trace To | Ask |
|---|---|---|
| Latency requirements | domain-* | What's acceptable response time? |
| Throughput needs | domain-* | How many requests per second? |
| Memory constraints | domain-* | What's the memory budget? |
Trace Down ↓
To implementation (Layer 1):
"Need to reduce allocations"
↓ m01-ownership: Use references, avoid clone
↓ m02-resource: Pre-allocate with_capacity
"Need to parallelize"
↓ m07-concurrency: Choose rayon or threads
↓ m07-concurrency: Consider async for I/O-bound
"Need cache efficiency"
↓ Data layout: Prefer Vec over HashMap when possible
↓ Access patterns: Sequential over random access
Quick Reference
| Tool | Purpose |
|---|---|
cargo bench |
Micro-benchmarks |
criterion |
Statistical benchmarks |
perf / flamegraph |
CPU profiling |
heaptrack |
Allocation tracking |
valgrind / cachegrind |
Cache analysis |
Optimization Priority
1. Algorithm choice (10x - 1000x)
2. Data structure (2x - 10x)
3. Allocation reduction (2x - 5x)
4. Cache optimization (1.5x - 3x)
5. SIMD/Parallelism (2x - 8x)
Common Techniques
| Technique | When | How |
|---|---|---|
| Pre-allocation | Known size | Vec::with_capacity(n) |
| Avoid cloning | Hot paths | Use references or Cow<T> |
| Batch operations | Many small ops | Collect then process |
| SmallVec | Usually small | smallvec::SmallVec<[T; N]> |
| Inline buffers | Fixed-size data | Arrays over Vec |
Common Mistakes
| Mistake | Why Wrong | Better |
|---|---|---|
| Optimize without profiling | Wrong target | Profile first |
| Benchmark in debug mode | Meaningless | Always --release |
| Use LinkedList | Cache unfriendly | Vec or VecDeque |
Hidden .clone() |
Unnecessary allocs | Use references |
| Premature optimization | Wasted effort | Make it work first |
Anti-Patterns
| Anti-Pattern | Why Bad | Better |
|---|---|---|
| Clone to avoid lifetimes | Performance cost | Proper ownership |
| Box everything | Indirection cost | Stack when possible |
| HashMap for small sets | Overhead | Vec with linear search |
| String concat in loop | O(n^2) | String::with_capacity or format! |
Related Skills
| When | See |
|---|---|
| Reducing clones | m01-ownership |
| Concurrency options | m07-concurrency |
| Smart pointer choice | m02-resource |
| Domain requirements | domain-* |
How to use m10-performance on Cursor
AI-first code editor with Composer
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 m10-performance
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches m10-performance from GitHub repository actionbook/rust-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate m10-performance. Access the skill through slash commands (e.g., /m10-performance) 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
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.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 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▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.5★★★★★38 reviews- ★★★★★Charlotte Gupta· Dec 8, 2024
We added m10-performance from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★William Gill· Dec 8, 2024
m10-performance reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Dhruvi Jain· Dec 4, 2024
m10-performance reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Advait Tandon· Nov 27, 2024
Keeps context tight: m10-performance is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Sophia Yang· Nov 27, 2024
I recommend m10-performance for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Oshnikdeep· Nov 23, 2024
I recommend m10-performance for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Kiara Haddad· Nov 19, 2024
Solid pick for teams standardizing on skills: m10-performance is focused, and the summary matches what you get after install.
- ★★★★★Henry Nasser· Oct 18, 2024
m10-performance is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★William Srinivasan· Oct 18, 2024
Useful defaults in m10-performance — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Ganesh Mohane· Oct 14, 2024
Useful defaults in m10-performance — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
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