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Command Creator

OrganizationPopular
softaworks
command-creator

This skill should be used when creating a Claude Code slash command. Use when users ask to "create a command", "make a slash command", "add a command", or want to document a workflow as a reusable command. Essential for creating optimized, agent-executable slash commands with proper structure and best practices.

Overview

Publishersoftaworks
Repositoryagent-toolkit
Skill namecommand-creator
Stars
2.5K
Forks
226
Bundled files
3
LicenseMIT
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.

  • 3 bundled files

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

  • Open source

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

Installation

Install the Command Creator 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/softaworks/agent-toolkit.git /tmp/agent-toolkit
mkdir -p .claude/skills
cp -r /tmp/agent-toolkit/skills/command-creator .claude/skills/command-creator
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Command Creator 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 Command Creator 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 Command Creator 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.

Command Creator

This skill guides the creation of Claude Code slash commands - reusable workflows that can be invoked with /command-name in Claude Code conversations.

About Slash Commands

Slash commands are markdown files stored in .claude/commands/ (project-level) or ~/.claude/commands/ (global/user-level) that get expanded into prompts when invoked. They're ideal for:

  • Repetitive workflows (code review, PR submission, CI fixing)
  • Multi-step processes that need consistency
  • Agent delegation patterns
  • Project-specific automation

When to Use This Skill

Invoke this skill when users:

  • Ask to "create a command" or "make a slash command"
  • Want to automate a repetitive workflow
  • Need to document a consistent process for reuse
  • Say "I keep doing X, can we make a command for it?"
  • Want to create project-specific or global commands

Bundled Resources

This skill includes reference documentation for detailed guidance:

  • references/patterns.md - Command patterns (workflow automation, iterative fixing, agent delegation, simple execution)
  • references/examples.md - Real command examples with full source (submit-stack, ensure-ci, create-implementation-plan)
  • references/best-practices.md - Quality checklist, common pitfalls, writing guidelines, template structure

Load these references as needed when creating commands to understand patterns, see examples, or ensure quality.

Command Structure Overview

Every slash command is a markdown file with:

markdown
---
description: Brief description shown in /help (required)
argument-hint: <placeholder> (optional, if command takes arguments)
---

# Command Title

[Detailed instructions for the agent to execute autonomously]

Command Creation Workflow

Step 1: Determine Location

Auto-detect the appropriate location:

  1. Check git repository status: git rev-parse --is-inside-work-tree 2>/dev/null
  2. Default location:
    • If in git repo → Project-level: .claude/commands/
    • If not in git repo → Global: ~/.claude/commands/
  3. Allow user override:
    • If user explicitly mentions "global" or "user-level" → Use ~/.claude/commands/
    • If user explicitly mentions "project" or "project-level" → Use .claude/commands/

Report the chosen location to the user before proceeding.

Step 2: Show Command Patterns

Help the user understand different command types. Load references/patterns.md to see available patterns:

  • Workflow Automation - Analyze → Act → Report (e.g., submit-stack)
  • Iterative Fixing - Run → Parse → Fix → Repeat (e.g., ensure-ci)
  • Agent Delegation - Context → Delegate → Iterate (e.g., create-implementation-plan)
  • Simple Execution - Run command with args (e.g., codex-review)

Ask the user: "Which pattern is closest to what you want to create?" This helps frame the conversation.

Step 3: Gather Command Information

Ask the user for key information:

A. Command Name and Purpose

Ask:

  • "What should the command be called?" (for filename)
  • "What does this command do?" (for description field)

Guidelines:

  • Command names MUST be kebab-case (hyphens, NOT underscores)
    • ✅ CORRECT: submit-stack, ensure-ci, create-from-plan
    • ❌ WRONG: submit_stack, ensure_ci, create_from_plan
  • File names match command names: my-command.md → invoked as /my-command
  • Description should be concise, action-oriented (appears in /help output)
B. Arguments

Ask:

  • "Does this command take any arguments?"
  • "Are arguments required or optional?"
  • "What should arguments represent?"

If command takes arguments:

  • Add argument-hint: <placeholder> to frontmatter
  • Use <angle-brackets> for required arguments
  • Use [square-brackets] for optional arguments
C. Workflow Steps

Ask:

  • "What are the specific steps this command should follow?"
  • "What order should they happen in?"
  • "What tools or commands should be used?"

Gather details about:

  • Initial analysis or checks to perform
  • Main actions to take
  • How to handle results
  • Success criteria
  • Error handling approach
D. Tool Restrictions and Guidance

Ask:

  • "Should this command use any specific agents or tools?"
  • "Are there any tools or operations it should avoid?"
  • "Should it read any specific files for context?"

Step 4: Generate Optimized Command

Create the command file with agent-optimized instructions. Load references/best-practices.md for:

  • Template structure
  • Best practices for agent execution
  • Writing style guidelines
  • Quality checklist

Key principles:

  • Use imperative/infinitive form (verb-first instructions)
  • Be explicit and specific
  • Include expected outcomes
  • Provide concrete examples
  • Define clear error handling

Step 5: Create the Command File

  1. Determine full file path:

    • Project: .claude/commands/[command-name].md
    • Global: ~/.claude/commands/[command-name].md
  2. Ensure directory exists:

    bash
    mkdir -p [directory-path]
  3. Write the command file using the Write tool

  4. Confirm with user:

    • Report the file location
    • Summarize what the command does
    • Explain how to use it: /command-name [arguments]

Step 6: Test and Iterate (Optional)

If the user wants to test:

  1. Suggest testing: You can test this command by running: /command-name [arguments]
  2. Be ready to iterate based on feedback
  3. Update the file with improvements as needed

Quick Tips

For detailed guidance, load the bundled references:

  • Load references/patterns.md when designing the command workflow
  • Load references/examples.md to see how existing commands are structured
  • Load references/best-practices.md before finalizing to ensure quality

Common patterns to remember:

  • Use Bash tool for pytest, pyright, ruff, prettier, make, gt commands
  • Use Task tool to invoke subagents for specialized tasks
  • Check for specific files first (e.g., .PLAN.md) before proceeding
  • Mark todos complete immediately, not in batches
  • Include explicit error handling instructions
  • Define clear success criteria

Summary

When creating a command:

  1. Detect location (project vs global)
  2. Show patterns to frame the conversation
  3. Gather information (name, purpose, arguments, steps, tools)
  4. Generate optimized command with agent-executable instructions
  5. Create file at appropriate location
  6. Confirm and iterate as needed

Focus on creating commands that agents can execute autonomously, with clear steps, explicit tool usage, and proper error handling.

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 Command Creator AI skill do?

This skill should be used when creating a Claude Code slash command. Use when users ask to "create a command", "make a slash command", "add a command", or want to document a workflow as a reusable command. Essential for creating optimized, agent-executable slash commands with proper structure and best practices.

Why use Command Creator on TypingMind?

Because you install it once and use it with any model. Command Creator 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 Command Creator in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/softaworks/agent-toolkit/tree/main/skills/command-creator. 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 Command Creator?

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 Command Creator?

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

Is the Command Creator AI skill free?

Yes. It is published on GitHub by softaworks under the MIT license. 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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