optimize-julia-code

optimize-julia-code is a skill for Codex from Krastanov/JuliaLLMAgentSkills. It costs 61 tokens per session (330 once invoked), scanned A, original, Unlicense.

A guide to measuring and improving the runtime performance of Julia programs. It uses benchmarks, profiling, allocation checks, and type-inference analysis to find and verify real bottlenecks.

In plain words
What is it for?
Use it when Julia code is slow, allocates too much memory, has type-instability, or needs a repeatable performance baseline and regression check.
Why use it?
It keeps optimization tied to measured workloads, reducing the risk of making code more complicated without making it faster.

Skill for Codex

Written for Codex: agents/openai.yaml present.

Good fit Use it when Julia code is slow, allocates too much memory, has type-instability, or needs a repeatable performance baseline and regression check.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/krastanov/juliallmagentskills/optimize-julia-code
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 Krastanov/JuliaLLMAgentSkills --skill optimize-julia-code
Clone the repo
git clone --depth 1 https://github.com/Krastanov/JuliaLLMAgentSkills

Made for: Codex.

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 optimize-julia-code

README.md
[![agentmods](https://agentmods.dev/badge/skills/krastanov/juliallmagentskills/optimize-julia-code.svg)](https://agentmods.dev/skills/krastanov/juliallmagentskills/optimize-julia-code)
Your own site
<a href="https://agentmods.dev/skills/krastanov/juliallmagentskills/optimize-julia-code"><img src="https://agentmods.dev/badge/skills/krastanov/juliallmagentskills/optimize-julia-code.svg" alt="Measured on agentmods" height="20"></a>
Per session 61 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 330 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.
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 $0.00061 $0.00330
Opus 5 $0.00030 $0.00165
Sonnet 5 $0.00012 $0.00066
Haiku 4.5 $0.00006 $0.00033

Measured 8d ago against content hash 40258e7410a4, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

optimize-julia-code 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.

optimize-julia-code/SKILL.md · 26 lines

What it actually says

Optimize Julia Code

Optimize an observed workload, preserve its behavior, and remeasure every change.

Workflow

  1. Define representative inputs and record a correct baseline.
  2. Establish repeatable timing and allocation measurements. Read measurement-and-benchmarks.md.
  3. Locate time or allocation hotspots before editing. Read profiling-and-allocations.md.
  4. Inspect inference and memory access only where measurements point. Read inference-and-data-layout.md.
  5. Apply annotations, concurrency, or specialized tooling only after simpler changes. Read advanced-optimization.md.
  6. Run correctness tests and the original measurement after each focused change. Keep only improvements that hold on representative inputs.

Boundaries

  • Separate first-call latency from steady-state runtime.
  • Prefer algorithmic and data-movement improvements over syntax-level tweaks.
  • Do not make argument types concrete merely for speed; Julia specializes on actual argument types.
  • Do not add an optimization package without a measured bottleneck, a current compatibility check, and a benchmark against Base Julia.
  • Report the workload, Julia version, thread configuration, inputs, and measurement method with performance conclusions.
Files

What ships with it

5 files 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 · 26 lines · 61 tokens per session scan A 40258e7410a4

Subscribe to this mod's changes

optimize-julia-code is a skill published in the GitHub repository Krastanov/JuliaLLMAgentSkills (30 stars, last pushed 1mo ago), licensed Unlicense. It adds 61 tokens to every session and 330 once invoked, about $0.0003 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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