Agent Creator - Enhanced with 4-Phase SOP Methodology

This skill provides the official comprehensive framework for creating specialized AI agents, integrating the proven 4-phase methodology from Desktop .claude-flow with Claude Agent SDK implementation and evidence-based prompting techniques.

When to Use This Skill

Use agent-creator for:

• Creating project-specialized agents with deeply embedded domain knowledge
• Building agents for recurring tasks requiring consistent behavior
• Rewriting existing agents to optimize performance
• Creating multi-agent workflows with sequential or parallel coordination
• Agents that will integrate with MCP servers and Claude Flow

The 4-Phase Agent Creation Methodology

Source: Desktop .claude-flow/ official SOP documentation Total Time: 2.5-4 hours per agent (first-time), 1.5-2 hours (speed-run)

This methodology was developed through systematic reverse engineering of fog-compute agent creation and validated through production use.

Phase 1: Initial Analysis & Intent Decoding (30-60 minutes)

Objective: Deep domain understanding through systematic research, not assumptions.

Activities:

1. Domain Breakdown

   • What problem does this agent solve?
   • What are the key challenges in this domain?
   • What patterns do human experts use?
   • What are common failure modes?

2. Technology Stack Mapping

   • What tools, frameworks, libraries are used?
   • What file types, formats, protocols?
   • What integrations or APIs?
   • What configuration patterns?

3. Integration Points

   • What MCP servers will this agent use?
   • What other agents will it coordinate with?
   • What data flows in/out?
   • What memory patterns needed?

Validation Gate:

• Can describe domain in specific, technical terms
• Identified 5+ key challenges
• Mapped technology stack comprehensively
• Clear on integration requirements

Outputs:

• Domain analysis document
• Technology stack inventory
• Integration requirements list

Phase 2: Meta-Cognitive Extraction (30-45 minutes)

Objective: Identify the cognitive expertise domains activated when you reason about this agent's tasks.

Activities:

1. Expertise Domain Identification

   • What knowledge domains are activated when you think about this role?
   • What heuristics, patterns, rules-of-thumb?
   • What decision-making frameworks?
   • What quality standards?

2. Agent Specification Creation

   # Agent Specification: [Name]
   
   ## Role & Expertise
   - Primary role: [Specific title]
   - Expertise domains: [List activated domains]
   - Cognitive patterns: [Heuristics used]
   
   ## Core Capabilities
   1. [Capability with specific examples]
   2. [Capability with specific examples]
   ...
   
   ## Decision Frameworks
   - When X, do Y because Z
   - Always check A before B
   - Never skip validation of C
   
   ## Quality Standards
   - Output must meet [criteria]
   - Performance measured by [metrics]
   - Failure modes to prevent: [list]
   

3. Supporting Artifacts

   • Create examples of good vs bad outputs
   • Document edge cases
   • List common pitfalls

Validation Gate:

• Identified 3+ expertise domains
• Documented 5+ decision heuristics
• Created complete agent specification
• Examples demonstrate quality standards

Outputs:

• Agent specification document
• Example outputs (good/bad)
• Edge case inventory

Phase 3: Agent Architecture Design (45-60 minutes)

Objective: Transform specification into production-ready base system prompt.

Activities:

1. System Prompt Structure Design

   # [AGENT NAME] - SYSTEM PROMPT v1.0
   
   ## 🎭 CORE IDENTITY
   
   I am a **[Role Title]** with comprehensive, deeply-ingrained knowledge of [domain]. Through systematic reverse engineering and domain expertise, I possess precision-level understanding of:
   
   - **[Domain Area 1]** - [Specific capabilities from Phase 2]
   - **[Domain Area 2]** - [Specific capabilities from Phase 2]
   - **[Domain Area 3]** - [Specific capabilities from Phase 2]
   
   My purpose is to [primary objective] by leveraging [unique expertise].
   
   ## 📋 UNIVERSAL COMMANDS I USE
   
   **File Operations**:
   - /file-read, /file-write, /glob-search, /grep-search
   WHEN: [Specific situations from domain analysis]
   HOW: [Exact patterns]
   
   **Git Operations**:
   - /git-status, /git-commit, /git-push
   WHEN: [Specific situations]
   HOW: [Exact patterns]
   
   **Communication & Coordination**:
   - /memory-store, /memory-retrieve
   - /agent-delegate, /agent-escalate
   WHEN: [Specific situations]
   HOW: [Exact patterns with namespace conventions]
   
   ## 🎯 MY SPECIALIST COMMANDS
   
   [List role-specific commands with exact syntax and examples]
   
   ## 🔧 MCP SERVER TOOLS I USE
   
   **Claude Flow MCP**:
   - mcp__claude-flow__agent_spawn
     WHEN: [Specific coordination scenarios]
     HOW: [Exact function call patterns]
   
   - mcp__claude-flow__memory_store
     WHEN: [Cross-agent data sharing]
     HOW: [Namespace pattern: agent-role/task-id/data-type]
   
   **[Other relevant MCP servers from Phase 1]**
   
   ## 🧠 COGNITIVE FRAMEWORK
   
   ### Self-Consistency Validation
   Before finalizing deliverables, I validate from multiple angles:
   1. [Domain-specific validation 1]
   2. [Domain-specific validation 2]
   3. [Cross-check with standards]
   
   ### Program-of-Thought Decomposition
   For complex tasks, I decompose BEFORE execution:
   1. [Domain-specific decomposition pattern]
   2. [Dependency analysis]
   3. [Risk assessment]
   
   ### Plan-and-Solve Execution
   My standard workflow:
   1. PLAN: [Domain-specific planning]
   2. VALIDATE: [Domain-specific validation]
   3. EXECUTE: [Domain-specific execution]
   4. VERIFY: [Domain-specific verification]
   5. DOCUMENT: [Memory storage patterns]
   
   ## 🚧 GUARDRAILS - WHAT I NEVER DO
   
   [From Phase 2 failure modes and edge cases]
   
   **[Failure Category 1]**:
   ❌ NEVER: [Dangerous pattern]
   WHY: [Consequences from domain knowledge]
   
   WRONG:
     [Bad example]
   
   CORRECT:
     [Good example]
   
   ## ✅ SUCCESS CRITERIA
   
   Task complete when:
   - [ ] [Domain-specific criterion 1]
   - [ ] [Domain-specific criterion 2]
   - [ ] [Domain-specific criterion 3]
   - [ ] Results stored in memory
   - [ ] Relevant agents notified
   
   ## 📖 WORKFLOW EXAMPLES
   
   ### Workflow 1: [Common Task Name from Phase 1]
   
   **Objective**: [What this achieves]
   
   **Step-by-Step Commands**:
   ```yaml
   Step 1: [Action]
     COMMANDS:
       - /[command-1] --params
       - /[command-2] --params
     OUTPUT: [Expected]
     VALIDATION: [Check]
   
   Step 2: [Next Action]
     COMMANDS:
       - /[command-3] --params
     OUTPUT: [Expected]
     VALIDATION: [Check]
   

   Timeline: [Duration] Dependencies: [Prerequisites]

2. Evidence-Based Technique Integration

   For each technique (from existing agent-creator skill):

   • Self-consistency: When to use, how to apply
   • Program-of-thought: Decomposition patterns
   • Plan-and-solve: Planning frameworks

   Integrate these naturally into the agent's methodology.

3. Quality Standards & Guardrails

   From Phase 2 failure modes, create explicit guardrails:

   • What patterns to avoid
   • What validations to always run
   • When to escalate vs. retry
   • Error handling protocols

Validation Gate:

• System prompt follows template structure
• All Phase 2 expertise embedded
• Evidence-based techniques integrated
• Guardrails cover identified failure modes
• 2+ workflow examples with exact commands

Outputs:

• Base system prompt (v1.0)
• Cognitive framework specification
• Guardrails documentation

Phase 4: Deep Technical Enhancement (60-90 minutes)

Objective: Reverse-engineer exact implementation patterns and document with precision.

Activities:

1. Code Pattern Extraction

   For technical agents, extra