Julia Perf
Optimize Julia code for performance. Use this skill when diagnosing slow code, reducing allocations, fixing type instabilities, or applying performance best practices.
Install
$ agentstack add skill-krastanov-juliallmagentskills-julia-perf ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Links to your public security report.[](https://agentstack.voostack.com/security/report/skill-krastanov-juliallmagentskills-julia-perf) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
Julia Performance Optimization
Diagnose and fix performance issues in Julia code following the official Performance Tips.
Diagnosis Workflow
- Profile first — identify the actual bottleneck before optimizing.
- Check type stability — run
@code_warntypeon hot functions. - Measure allocations — use
@time/@btimeto find unexpected allocations. - Apply targeted fixes — use the references below for specific patterns.
using BenchmarkTools, Profile
# Step 1: Measure
@btime my_function($args)
# Step 2: Check type stability
@code_warntype my_function(args)
# Step 3: Profile
@profile my_function(args)
Profile.print(noisefloor=2.0)
# Step 4: Track allocations
julia --track-allocation=user -e 'using MyPkg; my_function(args); Profile.clear_malloc_data(); my_function(args)'Top Rules
- Put hot code in functions — never at global/script scope.
- Avoid untyped globals — use
constor pass as arguments. - Use concrete types everywhere — in structs, containers, return values.
- Pre-allocate and mutate — use
!functions to reuse buffers. - Access arrays in column-major order — first index varies fastest.
- Fuse broadcasts — use
@.or dot syntax to avoid temporaries.
Reference
- [Type Stability](references/type-stability.md) — Type inference, stable returns, function barriers
- [Structs & Dispatch](references/structs-dispatch.md) — Concrete fields, parametric types, Val
- [Memory & Arrays](references/memory-arrays.md) — Pre-allocation, views, column-major, broadcasting
- [Annotations & Tweaks](references/annotations-tweaks.md) — @inbounds, @fastmath, @simd, misc tips
- [Profiling & Diagnosis](references/profiling.md) — @code_warntype, profiling, allocation tracking
Related Skills
julia-jet- JET.jl static analysis overviewjulia-bench- Quick benchmarks, suites, and benchmark CI
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: Krastanov
- Source: Krastanov/JuliaLLMAgentSkills
- License: Unlicense
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.