m10-performance

m10-performance is a skill for Claude Code from moeru-ai/auv. It costs 49 tokens per session (1,035 once invoked), scanned A, original, Apache-2.0.

A guide to making Rust programs use less time or memory. It explains how to measure slow code and choose changes such as fewer allocations, better data layout, parallel work, or fewer copies.

In plain words
What is it for?
Use it for profiling, benchmarking, reducing allocations, improving cache use, parallelizing work, avoiding copies, or choosing faster data structures.
Why use it?
It helps you find the actual bottleneck before changing code, while making the trade-off between speed, memory use, and added complexity explicit.

Skill for Claude Code

Written for Claude Code: user-invocable in frontmatter. Also seen: installed under .agents/ (shared by several agents).

Good fit Use it for profiling, benchmarking, reducing allocations, improving cache use, parallelizing work, avoiding copies, or choosing faster data structures.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/moeru-ai/auv/m10-performance
View source ↗ moeru-ai/auv
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add moeru-ai/auv --skill m10-performance
Clone the repo
git clone --depth 1 https://github.com/moeru-ai/auv

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for m10-performance

README.md
[![agentmods](https://agentmods.dev/badge/skills/moeru-ai/auv/m10-performance.svg)](https://agentmods.dev/skills/moeru-ai/auv/m10-performance)
Your own site
<a href="https://agentmods.dev/skills/moeru-ai/auv/m10-performance"><img src="https://agentmods.dev/badge/skills/moeru-ai/auv/m10-performance.svg" alt="Measured on agentmods" height="20"></a>
Per session 49 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,035 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00049 $0.01035
Opus 5 $0.00024 $0.00517
Sonnet 5 $0.00010 $0.00207
Haiku 4.5 $0.00005 $0.00103

Measured 8d ago against content hash 6661cd16c27d, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

m10-performance scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.agents/skills/m10-performance/SKILL.md · 158 lines

How it starts

The opening of the file, as written. The whole thing — 158 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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:

  1. Have you measured?

    • Profile first → flamegraph, perf
    • Benchmark → criterion, cargo bench
    • Identify actual hotspots
  2. What's the priority?

    • Algorithm (10x-1000x improvement)
    • Data structure (2x-10x)
    • Allocation (2x-5x)
    • Cache (1.5x-3x)
  3. 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

Read the full file on GitHub · 158 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 8d ago First seen · 158 lines · 49 tokens per session scan A 6661cd16c27d

Subscribe to this mod's changes

m10-performance is a skill published in the GitHub repository moeru-ai/auv (27 stars, last pushed today), licensed Apache-2.0. It adds 49 tokens to every session and 1,035 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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