Write correct, performant Julia for research. Use whenever the user runs Julia, writes hot numeric code, or does numerical experiments, AD/gradients, optimization, symbolic algebra, differential equations, package architecture, TTFX, or .so/AOT. Trigger on scope/let/const, Val, column-major loops, immutable/mutable struct, type stability / 型安定, dynamic vs multiple dispatch, function barrier, DifferentiationInterface, ADTypes, ForwardDiff, Enzyme, Zygote, JET, DispatchDoctor, AllocCheck, Aqua, ExplicitImports, Chairmarks, StaticArrays, ComponentArrays, OhMyThreads, Optim/JuMP/Manopt, SymEngine/Symbolics/ModelingToolkit/SymPyPythonCall, DrWatson/Pluto/Documenter/Quarto, PackageCompiler, juliac --trim, @ccallable, include order, submodules vs subpackages, weakdeps, type piracy, public API. MANDATORY — read BEFORE writing ANY Julia code. §2.0 forbids FD derivative estimation, grid sampling, and lerp-as-evaluation. Use DI for AD; multiple dispatch is not banned — the bug is type-unstable hot paths. Co-fires with ORDER: Julia feature/bugfix → implementing-and-debugging first (change-safety), this for idiom; Julia refactor → refactoring-code governs (two hats/oracle), this supplies JET/Aqua brackets + Julia-safe transforms. Lean/formal proofs → proving-theorems; Python tooling → running-python-tools (PythonCall/SymPyPythonCall from Julia stays here).
Install with
Claude CodeCodexCursorGitHub CopilotGemini CLI
Supported by the Atlas install prompt. Skill-specific requirements vary.
Skill stats
Skills1CategoryScience & researchStars0
About this skill
Write correct, performant Julia for research. Use whenever the user runs Julia, writes hot numeric code, or does numerical experiments, AD/gradients, optimization, symbolic algebra, differential equations, package architecture, TTFX, or .so/AOT. Trigger on scope/let/const, Val, column-major loops, immutable/mutable struct, type stability / 型安定, dynamic vs multiple dispatch, function barrier, DifferentiationInterface, ADTypes, ForwardDiff, Enzyme, Zygote, JET, DispatchDoctor, AllocCheck, Aqua, ExplicitImports, Chairmarks, StaticArrays, ComponentArrays, OhMyThreads, Optim/JuMP/Manopt, SymEngine/Symbolics/ModelingToolkit/SymPyPythonCall, DrWatson/Pluto/Documenter/Quarto, PackageCompiler, juliac --trim, @ccallable, include order, submodules vs subpackages, weakdeps, type piracy, public API. MANDATORY — read BEFORE writing ANY Julia code. §2.0 forbids FD derivative estimation, grid sampling, and lerp-as-evaluation. Use DI for AD; multiple dispatch is not banned — the bug is type-unstable hot paths. Co-fires with ORDER: Julia feature/bugfix → implementing-and-debugging first (change-safety), this for idiom; Julia refactor → refactoring-code governs (two hats/oracle), this supplies JET/Aqua brackets + Julia-safe transforms. Lean/formal proofs → proving-theorems; Python tooling → running-python-tools (PythonCall/SymPyPythonCall from Julia stays here).
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Write correct, performant Julia for research. Use whenever the user runs Julia, writes hot numeric code, or does numerical experiments, AD/gradients, optimization, symbolic algebra, differential equations, package architecture, TTFX, or .so/AOT. Trigger on scope/let/const, Val, column-major loops, immutable/mutable struct, type stability / 型安定, dynamic vs multiple dispatch, function barrier, DifferentiationInterface, ADTypes, ForwardDiff, Enzyme, Zygote, JET, DispatchDoctor, AllocCheck, Aqua, ExplicitImports, Chairmarks, StaticArrays, ComponentArrays, OhMyThreads, Optim/JuMP/Manopt, SymEngine/Symbolics/ModelingToolkit/SymPyPythonCall, DrWatson/Pluto/Documenter/Quarto, PackageCompiler, juliac --trim, @ccallable, include order, submodules vs subpackages, weakdeps, type piracy, public API. MANDATORY — read BEFORE writing ANY Julia code. §2.0 forbids FD derivative estimation, grid sampling, and lerp-as-evaluation. Use DI for AD; multiple dispatch is not banned — the bug is type-unstable hot paths. Co-fires with ORDER: Julia feature/bugfix → implementing-and-debugging first (change-safety), this for idiom; Julia refactor → refactoring-code governs (two hats/oracle), this supplies JET/Aqua brackets + Julia-safe transforms. Lean/formal proofs → proving-theorems; Python tooling → running-python-tools (PythonCall/SymPyPythonCall from Julia stays here).
About this listing
Category
Science & research
Reported license
Not specified
Listing language (estimated)
English
Review status
Not individually reviewed
Agent compatibility
Atlas installation supports Claude Code, Codex, and Cursor. Skill-specific dependencies and features are checked during setup.
Claude Code
Codex
Cursor
GitHub Copilot
Gemini CLI