{"slug":"spawn","title":"spawn","summary":"Generate PhD-level expert-agent prompt files (.md) for Claude Code - authoring, not runtime. For spawning parallel RUNTIME workers use fleet-worker/fleetflow (via parallel-ops). Triggers: spawn agent, create agent file, generate expert prompt, new agent persona.","platform":"Claude","tags":[],"authorName":"LLM Mart","authorSlug":"llm-mart","score":0,"source":"github","price":null,"verified":false,"createdAt":"2026-09-30T19:36:56.442498Z","repo":{"url":"https://github.com/0xDarkMatter/claude-mods","stars":43,"forks":7,"license":"MIT","updatedAt":"2026-09-30T15:18:48Z"},"bodyHtml":"
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name: spawn\ndescription: \"Generate PhD-level expert-agent prompt files (.md) for Claude Code - authoring, not runtime. For spawning parallel RUNTIME workers use fleet-worker/fleetflow (via parallel-ops). Triggers: spawn agent, create agent file, generate expert prompt, new agent persona.\"\nlicense: MIT\ncompatibility: \"Requires internet access for WebSearch/WebFetch to research official docs.\"\nallowed-tools: \"Read Write Bash WebSearch WebFetch AskUserQuestion\"\nmetadata:\nauthor: claude-mods

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Spawn - Expert Agent Generator

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Generate world-class, comprehensive expert agent prompts for Claude Code. Each agent should be a definitive reference for its domain - the kind of guide a PhD-level practitioner would create.

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Target quality: 500-1000 lines per agent with real code examples, complete configs, and detailed patterns.

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Benchmark agents: firecrawl-expert.md (1026 lines), project-organizer.md (610 lines)

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Usage Modes

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Mode 1: Single Agent Generation

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Generate one expert agent prompt for a specific technology platform.

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Prompt for:

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Mode 2: Batch Agent Generation

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Create multiple agent prompts from a list of technology platforms.

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Accept:

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Mode 3: Architecture Analysis

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Analyze a tech stack or architecture description and suggest relevant agents.

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Process:

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  1. Read architecture description (from user input or file)
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  2. Identify all technology platforms/services
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  3. Ask for scope (project or global)
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  4. Present checkbox selector for agent creation
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  5. Generate selected agents
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Agent File Format

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All agents MUST be created as Markdown files with YAML frontmatter:

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File Structure:

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---\nname: technology-name-expert\ndescription: When this agent should be used. Can include examples and use cases. No strict length limit - be clear and specific. Include \"use PROACTIVELY\" for automatic invocation.\nmodel: inherit\ncolor: blue\n---\n\n[Agent system prompt content here]\n
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YAML Frontmatter Fields:

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File Creation:\nAgents can be created programmatically using the Write tool:

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Project-level: .claude/agents/[platform]-expert.md\nGlobal/User-level: ~/.claude/agents/[platform]-expert.md (or C:\\Users\\[username]\\.claude\\agents\\ on Windows)\n
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Choosing Scope:

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After creation, the agent is immediately available for use with the Task tool.

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Claude Code Agent Documentation

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Essential Reading:

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Key Concepts from Documentation:

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Generation Requirements

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For each agent, create a comprehensive expert prompt with:

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Agent Content Structure (10-Part Template):

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Every generated agent MUST follow this comprehensive 10-part structure:

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  1. Part 1: Core Concepts - Fundamental principles, mental model, architecture overview
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  2. Part 2: Essential Patterns (5-10 patterns) - Each with: when to use, full implementation (20-50 lines), variations, common mistakes
    3. \n
  3. Part 3: Advanced Techniques (3-5 techniques) - Deep dives with complete examples
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  4. Part 4: Configuration - Complete dev config, complete prod config, environment variables table
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  5. Part 5: Integration Patterns - Integration code for 2-3 common technologies
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  6. Part 6: Testing Strategies - Unit tests with mocks, integration tests, test configuration
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  7. Part 7: Error Handling - Custom exception hierarchy, retry/circuit breaker patterns, structured logging
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  8. Part 8: Performance Optimization - Profiling techniques, optimization table, caching strategies
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  9. Part 9: Security Considerations - Common vulnerabilities, security hardening checklist
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  10. Part 10: Quick Reference - Common operations cheat sheet (20-30 snippets), CLI commands, troubleshooting table
    11. \n
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Plus: Quality Checklist, Anti-Patterns (5-10 with bad/good code), Canonical Resources (10-15 URLs)

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See firecrawl-expert.md and project-organizer.md in agents/ for reference implementations.

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Requirements:

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Output Options

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Ask user to choose scope:

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  1. Project Agent - Save to .claude/agents/ (project-specific, version controlled)
    2. \n
  2. Global Agent - Save to ~/.claude/agents/ or C:\\Users\\[username]\\.claude\\agents\\ (all projects)
    3. \n
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Ask user to choose format:

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  1. Clipboard-ready - Output complete markdown (with YAML frontmatter) in code block
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  2. File creation - Use Write tool to save to appropriate agents directory based on scope
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  3. Both - Create file using Write tool AND show complete content in chat for review
    4. \n
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File Creation Process:\nWhen creating files programmatically:

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  1. Generate complete agent content with YAML frontmatter
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  2. Determine path based on scope selection:\n\n
    3. \n
  3. Use Write tool with appropriate path
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  4. Verify file was created successfully
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  5. Agent is immediately available for use
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Examples

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Example 1: Single Agent

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User: /spawn\nAgent: [Shows multi-tab AskUserQuestion with 5 tabs]\n  Tab 1 (Mode): Single Agent / Batch Generation / Architecture Analysis\n  Tab 2 (Scope): Project Agent / Global Agent\n  Tab 3 (Output): Create File / Show in Chat / Both\n  Tab 4 (Platform): Custom Platform / [or popular options]\n  Tab 5 (Focus): [Multi-select] General Coverage / Caching Patterns / Pub/Sub / etc.\nUser: [Selects all answers and submits once]\n  Mode: Single Agent\n  Scope: Global Agent\n  Output: Both\n  Platform: Redis (via Other field)\n  Focus: General Coverage, Caching Patterns, Pub/Sub\nAgent: [Generates Redis expert prompt and saves to ~/.claude/agents/redis-expert.md]\n
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Example 2: Batch Generation

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User: /spawn\nAgent: [Shows multi-tab AskUserQuestion with 3 tabs]\n  Tab 1 (Mode): Single Agent / Batch Generation / Architecture Analysis\n  Tab 2 (Scope): Project Agent / Global Agent\n  Tab 3 (Output): Create Files / Show in Chat / Both\nUser: [Submits]\n  Mode: Batch Generation\n  Scope: Project Agent\n  Output: Create Files\nAgent: Please provide platforms (one per line):\nUser: PostgreSQL\nRedis\nRabbitMQ\n\nAgent: [Creates 3 .md files in .claude/agents/ (project directory)]\n
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Example 3: Architecture Analysis

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User: /spawn\nAgent: [Shows multi-tab AskUserQuestion with 3 tabs]\n  Tab 1 (Mode): Single Agent / Batch Generation / Architecture Analysis\n  Tab 2 (Scope): Project Agent / Global Agent\n  Tab 3 (Output): Create Files / Show in Chat / Both\nUser: [Submits]\n  Mode: Architecture Analysis\n  Scope: Global Agent\n  Output: Both\nAgent: Describe your architecture or provide file path:\nUser: E-commerce platform: Next.js frontend, Node.js API, PostgreSQL, Redis cache, Stripe payments, AWS S3 storage, SendGrid emails\nAgent: Found platforms: Next.js, Node.js, PostgreSQL, Redis, Stripe, AWS S3, SendGrid\n[Shows multi-select AskUserQuestion]\nUser: [Selects: nextjs-expert, redis-expert, stripe-expert, sendgrid-expert]\nAgent: [Generates 4 selected agents in ~/.claude/agents/]\n
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Implementation Steps

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  1. Ask All Questions at Once using a single multi-question AskUserQuestion call:

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    For Single Mode, also ask in the same call:

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    2. \n

  2. For Single Mode:

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    3. \n

  3. For Batch Mode:

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    4. \n

  4. For Architecture Analysis:

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    5. \n

  5. Generate Each Agent Prompt:

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    6. \n

  6. Output:

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    7. \n
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Important: Always use a single AskUserQuestion call with multiple questions (2-4) to create the multi-tab interface. Never ask questions sequentially one at a time.

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Quality Checklist

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Before outputting each agent prompt, verify:

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Post-Generation

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After creating agents, remind user:

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  1. Review generated prompts
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  2. Test agent with sample questions
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  3. Refine based on actual usage
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  4. Add to version control if satisfied
    5. \n
  5. Consult Claude Code documentation links above for advanced features and best practices
    6. \n
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Additional Resources:

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