Use this skill to structure a Julia package as a standalone command-line app. It explains how to add a `@main` entry point and declare executable names in `Project.toml`, so users can run the installed app from a terminal with arguments.
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Use when you create a CLI written in Julia packages or MCP servers
实测评分 未实测
查看安装方法安装前可以查看交给 AI 代理的指令内容。
Apps are Julia packages that are intended to be run as "standalone programs" (by e.g. typing the name of the app in the terminal possibly together with some arguments or flags/options). This is in contrast to most Julia packages that are used as "libraries" and are loaded by other files or in the Julia REPL.
A Julia app is structured similar to a standard Julia library with the following additions:
A @main entry point in the package module (see the Julia help on @main for details)
An [apps] section in the Project.toml file listing the executable names that the package provides.
A very simple example of an app that prints the reversed input arguments would be:
# src/MyReverseApp.jl
module MyReverseApp
function (@main)(ARGS)
for arg in ARGS
print(stdout, reverse(arg), " ")
end
return
end
end # module
# Project.toml
# standard fields here
[apps]
reverse = {}
The empty table {} is to allow for giving metadata about the app.
After installing this app one could run:
$ ~/.julia/bin/reverse some input string
emos tupni gnirts
directly in the terminal. See the following link to learn more:
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Plan worthwhile local tasks by checking dependencies, effects, resources, and platform boundaries, then execute the exact approved plan. It can use safe parallel work or suitable Apple-silicon backends when the project exposes them.
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Record deferred engineering decisions and temporary workarounds in a debt registry, with context such as effort, ongoing cost, affected area, and payoff trigger. Entries receive short labels so they can be reviewed or dropped later.
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Generate or compare AI agent code for LangChain, EINO, AutoGen, AgentScope, and CrewAI using framework-specific templates, API mappings, conventions, and project structures. Reference files include verification dates to help identify potentially outdated guidance.
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Investigate a reproducible bug, test failure, or unexpected page behavior, then make a narrowly scoped fix. The workflow calls for a regression test before changing code, verifies the fix, and reports remaining risks and evidence while avoiding unrelated refactoring or feature work.
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Build an AI agent project through six collaborative stages, from requirements and architecture to tools, prompts, code generation, and verification. Start framework-independent, then generate a runnable project for frameworks such as LangChain, EINO, AutoGen, AgentScope, or CrewAI.
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Audit a project or learning workflow for scope drift, conflicting plans, inconsistent names, and code quality concerns. The skill reads project files and code, then reports evidence, severity, and suggested next steps without changing files.
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