Zig Best Practices logo

Zig Best Practices

Organization
aiskillstore
zig-best-practices

Use when reading or writing Zig files (.zig, build.zig, build.zig.zon).

Overview

Publisheraiskillstore
Repositorymarketplace
Skill namezig-best-practices
Stars
427
Forks
45
Bundled files
4
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by aiskillstore on GitHub. Read the source before you install it.

Installation

Install the Zig Best Practices AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/aiskillstore/marketplace.git /tmp/marketplace
mkdir -p .claude/skills
cp -r /tmp/marketplace/skills/0xbigboss/zig-best-practices .claude/skills/zig-best-practices
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Zig Best Practices in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Zig Best Practices on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Zig Best Practices is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Zig Best Practices

Follows type-first, functional, and error handling patterns from CLAUDE.md. This skill covers Zig-specific idioms only.

Type System Patterns

Tagged unions for mutually exclusive states — prevents invalid combinations that a struct with multiple nullable fields would allow:

zig
const RequestState = union(enum) {
    idle,
    loading,
    success: []const u8,
    failure: anyerror,
};

Explicit error sets — documents exactly what can fail; anyerror hides failure modes:

zig
const ParseError = error{ InvalidSyntax, UnexpectedToken, EndOfInput };
fn parse(input: []const u8) ParseError!Ast { ... }

Distinct types for domain IDs — compiler prevents mixing up different ID types:

zig
const UserId = enum(u64) { _ };
const OrderId = enum(u64) { _ };

Comptime validation — catch invalid configurations at compile time, not runtime:

zig
fn Buffer(comptime size: usize) type {
    if (size == 0) @compileError("buffer size must be greater than 0");
    return struct { data: [size]u8 = undefined, len: usize = 0 };
}

Memory Management

  • Pass allocators explicitly to every function that allocates; no global allocator state.
  • Place defer resource.deinit() immediately after acquisition — keeps cleanup co-located with creation.
  • Use errdefer for cleanup on error paths; defer for unconditional cleanup.
  • Use arena allocators for batch/temporary work; they free everything at once.
  • Use std.testing.allocator in tests — reports leaks with stack traces.
zig
fn createResource(allocator: std.mem.Allocator) !*Resource {
    const resource = try allocator.create(Resource);
    errdefer allocator.destroy(resource);  // runs only on error
    resource.* = try initializeResource();
    return resource;
}

Key Conventions

  • Prefer const over var; prefer slices over raw pointers.
  • Prefer comptime T: type over anytype; explicit types produce clearer errors. Use anytype only for genuinely polymorphic cases (callbacks, std.debug.print-style).
  • Exhaustive switch: include an else returning an error or unreachable for truly impossible cases.
  • Use std.log.scoped(.module_name) for namespaced logging; define a module-level const log constant.
  • Larger cohesive files are idiomatic — tests alongside implementation, comptime generics at file scope.

Advanced Topics

  • Generic containers (queues, stacks, trees): See GENERICS.md
  • C library interop (raylib, SDL, curl): See C-INTEROP.md
  • Debugging memory leaks (GPA, stack traces): See DEBUGGING.md

Tooling

zigdoc — browse std library and dependency docs:

bash
zigdoc std.mem.Allocator   # std lib symbol
zigdoc vaxis.Window        # project dependency
zigdoc @init               # create AGENTS.md with API patterns

ziglint — static analysis with .ziglint.zon config:

bash
ziglint                    # lint current directory
ziglint --ignore Z001      # suppress specific rule

References

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Zig Best Practices AI skill do?

Use when reading or writing Zig files (.zig, build.zig, build.zig.zon).

Why use Zig Best Practices on TypingMind?

Because you install it once and use it with any model. Zig Best Practices is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Zig Best Practices in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/aiskillstore/marketplace/tree/main/skills/0xbigboss/zig-best-practices. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Zig Best Practices?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Zig Best Practices?

As many as you like. As long as a model supports skills, you can use Zig Best Practices with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Zig Best Practices AI skill free?

It is published on GitHub by aiskillstore. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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