generate_command

Create a new custom slash command following conventions and best practices

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Install skill "generate_command" with this command: npx skills add ratacat/claude-skills/ratacat-claude-skills-generate-command

Arguments

[command purpose and requirements]

Create a Custom Claude Code Command

Create a new slash command in .claude/commands/ for the requested task.

Goal

#$ARGUMENTS

Key Capabilities to Leverage

File Operations:

  • Read, Edit, Write - modify files precisely
  • Glob, Grep - search codebase
  • MultiEdit - atomic multi-part changes

Development:

  • Bash - run commands (git, tests, linters)
  • Task - launch specialized agents for complex tasks
  • TodoWrite - track progress with todo lists

Web & APIs:

  • WebFetch, WebSearch - research documentation
  • GitHub (gh cli) - PRs, issues, reviews
  • Playwright - browser automation, screenshots

Integrations:

  • AppSignal - logs and monitoring
  • Context7 - framework docs
  • Stripe, Todoist, Featurebase (if relevant)

Best Practices

  1. Be specific and clear - detailed instructions yield better results
  2. Break down complex tasks - use step-by-step plans
  3. Use examples - reference existing code patterns
  4. Include success criteria - tests pass, linting clean, etc.
  5. Think first - use "think hard" or "plan" keywords for complex problems
  6. Iterate - guide the process step by step

Required: YAML Frontmatter

EVERY command MUST start with YAML frontmatter:

---
name: command-name
description: Brief description of what this command does (max 100 chars)
argument-hint: "[what arguments the command accepts]"
---

Fields:

  • name: Lowercase command identifier (used internally)
  • description: Clear, concise summary of command purpose
  • argument-hint: Shows user what arguments are expected (e.g., [file path], [PR number], [optional: format])

Structure Your Command

# [Command Name]

[Brief description of what this command does]

## Steps

1. [First step with specific details]
   - Include file paths, patterns, or constraints
   - Reference existing code if applicable

2. [Second step]
   - Use parallel tool calls when possible
   - Check/verify results

3. [Final steps]
   - Run tests
   - Lint code
   - Commit changes (if appropriate)

## Success Criteria

- [ ] Tests pass
- [ ] Code follows style guide
- [ ] Documentation updated (if needed)

Tips for Effective Commands

  • Use $ARGUMENTS placeholder for dynamic inputs
  • Reference CLAUDE.md patterns and conventions
  • Include verification steps - tests, linting, visual checks
  • Be explicit about constraints - don't modify X, use pattern Y
  • Use XML tags for structured prompts: <task>, <requirements>, <constraints>

Example Pattern

Implement #$ARGUMENTS following these steps:

1. Research existing patterns
   - Search for similar code using Grep
   - Read relevant files to understand approach

2. Plan the implementation
   - Think through edge cases and requirements
   - Consider test cases needed

3. Implement
   - Follow existing code patterns (reference specific files)
   - Write tests first if doing TDD
   - Ensure code follows CLAUDE.md conventions

4. Verify
   - Run tests: `bin/rails test`
   - Run linter: `bundle exec standardrb`
   - Check changes with git diff

5. Commit (optional)
   - Stage changes
   - Write clear commit message

Creating the Command File

  1. Create the file at .claude/commands/[name].md (subdirectories like workflows/ supported)
  2. Start with YAML frontmatter (see section above)
  3. Structure the command using the template above
  4. Test the command by using it with appropriate arguments

Command File Template

---
name: command-name
description: What this command does
argument-hint: "[expected arguments]"
---

# Command Title

Brief introduction of what the command does and when to use it.

## Workflow

### Step 1: [First Major Step]

Details about what to do.

### Step 2: [Second Major Step]

Details about what to do.

## Success Criteria

- [ ] Expected outcome 1
- [ ] Expected outcome 2

Source Transparency

This detail page is rendered from real SKILL.md content. Trust labels are metadata-based hints, not a safety guarantee.

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