Skill Execution Criteria

When to Use This Skill

• Creating new specialist agents with domain-specific expertise
• Refining existing agent system prompts for better performance
• Designing multi-agent coordination systems
• Implementing role-based agent hierarchies
• Building production-ready agents with embedded domain knowledge

When NOT to Use This Skill

• For simple one-off tasks that don't need agent specialization
• When existing agents already cover the required domain
• For casual conversational interactions without systematic requirements
• When the task is better suited for a slash command or micro-skill

Success Criteria

• primary_outcome: "Production-ready agent with optimized system prompt, clear role definition, and validated performance"
• quality_threshold: 0.9
• verification_method: "Agent successfully completes domain-specific tasks with consistent high-quality output, passes validation tests, and integrates with Claude Agent SDK"

Edge Cases

• case: "Vague agent requirements" handling: "Use Phase 1 (Initial Analysis) to research domain, identify patterns, and clarify scope before proceeding"
• case: "Overlapping agent capabilities" handling: "Conduct agent registry search, identify gaps vs duplicates, propose consolidation or specialization"
• case: "Agent needs multiple conflicting personas" handling: "Decompose into multiple focused agents with clear coordination pattern"

Skill Guardrails

NEVER:

• "Create agents without deep domain research (skipping Phase 1 undermines quality)"
• "Use generic prompts without evidence-based techniques (CoT, few-shot, role-based)"
• "Skip validation testing (Phase 3) before considering agent production-ready"
• "Create agents that duplicate existing registry agents without justification" ALWAYS:
• "Complete all 4 phases: Analysis -> Prompt Engineering -> Testing -> Integration"
• "Apply evidence-based prompting: Chain-of-Thought for reasoning, few-shot for patterns, clear role definition"
• "Validate with diverse test cases and measure against quality criteria"
• "Document agent capabilities, limitations, and integration points"

Evidence-Based Execution

self_consistency: "After agent creation, test with same task multiple times to verify consistent outputs and reasoning quality" program_of_thought: "Decompose agent creation into: 1) Domain analysis, 2) Capability mapping, 3) Prompt architecture, 4) Test design, 5) Validation, 6) Integration" plan_and_solve: "Plan: Research domain + identify capabilities -> Execute: Build prompts + test cases -> Verify: Multi-run consistency + edge case handling"

Agent Creation - Systematic Agent Design

Evidence-based agent creation following best practices for prompt engineering and agent specialization.

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When to Use This Skill

Use when creating new specialist agents for specific domains, refining existing agent capabilities, designing multi-agent coordination systems, or implementing role-based agent hierarchies.

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4-Phase Agent Creation SOP

Phase 1: Specification

• Define agent purpose and domain
• Identify core capabilities needed
• Determine input/output formats
• Specify quality criteria

Tools: Use resources/scripts/generate_agent.sh for automated generation

Phase 2: Prompt Engineering

• Apply evidence-based prompting principles
• Use Chain-of-Thought for reasoning tasks
• Implement few-shot learning with examples (2-5 examples)
• Define role and persona clearly

Reference: See references/prompting-principles.md for detailed techniques

Phase 3: Testing & Validation

• Test with diverse inputs
• Validate output quality using resources/scripts/validate_agent.py
• Measure performance metrics
• Iterate based on results

Tests: Run tests from tests/ directory (basic, specialist, integration)

Phase 4: Integration

• Define agent coordination protocols
• Establish communication patterns via Memory MCP
• Configure memory and state management
• Deploy with monitoring using Claude-Flow hooks

Examples: See examples/example-1-python-specialist.md for complete walkthrough

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Quick Start

Generate Agent

cd resources/scripts
./generate_agent.sh <agent-name> <category> --interactive

Categories: specialist, coordinator, hybrid, research, development, testing, documentation, security

Validate Agent

python3 validate_agent.py <path-to-agent-spec.yaml>

Expected Output: All validation checks pass (metadata, role, capabilities, prompting, quality, integration)

Deploy Agent

# Copy to Claude-Flow agents directory
cp agent-spec.yaml ~/.claude-flow/agents/<agent-name>.yaml

# Test with Claude Code
Task("<Agent Name>", "<Task Description>", "<category>")

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Evidence-Based Prompting Principles

1. Role Definition

Clear agent identity and expertise improves performance by 15-30%

role:
  identity: "You are a [Specific Role] with expertise in [Domain]"
  expertise: ["skill-1", "skill-2", "skill-3"]
  responsibilities: ["task-1", "task-2"]

2. Chain-of-Thought Reasoning

Explicit reasoning steps improve accuracy by 20-40% on complex tasks

reasoning_steps:
  - "Step 1: Analyze requirements"
  - "Step 2: Identify solutions"
  - "Step 3: Evaluate trade-offs"
  - "Step 4: Select optimal approach"

3. Few-Shot Learning

2-5 examples improve performance by 30-50% compared to zero-shot

examples:
  - input: "Concrete example input"
    reasoning: "Step-by-step thinking"
    output: "Expected output with explanation"

4. Plan-and-Solve

Planning before execution reduces errors by 25-35% on complex workflows

workflow:
  - name: "Planning Phase"
    steps: ["Understand requirements", "Outline approach"]
  - name: "Execution Phase"
    steps: ["Implement solution", "Handle edge cases"]

Reference: Complete guide in references/prompting-principles.md

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Agent Types

Specialist Agents

• Domain-specific expertise (Python, React, SQL)
• Single responsibility principle
• 5-7 core competencies
• Deep technical knowledge

Example: Python Backend Specialist with FastAPI, SQLAlchemy, pytest

Coordinator Agents

• Multi-agent orchestration
• Task delegation and routing
• Progress monitoring
• Dependency management

Example: Backend Coordinator managing API, database, and testing agents

Hybrid Agents

• Multi-domain capabilities
• Adaptive role switching
• 2-3 related domains
• Context-aware mode switching

Example: Full-Stack Developer switching between backend and frontend

Reference: Complete patterns in references/agent-patterns.md

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Integration

Claude Code Task Tool

// Spawn agent for parallel execution
Task("Agent Name", "Task description", "category")

Memory MCP

integration:
  memory_mcp:
    enabled: true
    tagging_protocol:
      WHO: "agent-name"
      WHEN: "timestamp"
      PROJECT: "project-name"
      WHY: "intent"

Claude-Flow Hooks

integration:
  hooks:
    pre_task: ["npx claude-flow@alpha hooks pre-task"]
    post_task: ["npx claude-flow@alpha hooks post-task"]

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Resources

Scripts

• validate_agent.py: Comprehensive agent specification validator
• generate_agent.sh: Interactive agent generation with templates

Templates

• agent-spec.yaml: Complete agent specification (250+ lines)
• capabilities.json: Structured capabilities configuration (150+ lines)

Tests

• test-1-basic.md: Basic agent creation (~10 min)
• test-2-specialist.md: Specialist agent with advanced config (~20 min)
• test-3-integration.md: Multi-agent coordination (~30 min)

Examples

• example-1-python-specialist.md: Complete Python specialist walkthrough

Documentation

• prompting-principles.md: Evidence-based prompting techniques
• agent-patterns.md: Agent design patterns and anti-patterns
• agent-creation-process.dot: GraphViz process flow diagram

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Quality Assurance

Validation Checks

• YAML syntax and structure
• Metadata completeness (name, version, category, description)
• Role definition (identity, expertise, responsibilities)
• Capability specifications (primary, secondary, tools)
• Prompting techniques (chain-of-thought, few-shot, role-based)
• Few-shot examples (2+ with input/reasoning/output)
• Quality criteria (success criteria, failure modes, metrics)
• Integration configuration (Memory MCP, Claude-Flow hooks)

Success Criteria

• Functional correctness > 95%
• Output completeness > 90%
• Test coverage > 80%
• Response time < 30 seconds

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Process Flow

See graphviz/agent-creation-process.dot for visual workflow:

1. Specification Phase: Define purpose, capabilities, quality criteria
2. Prompting Phase: Apply evidence-based techniques, create examples
3. Testing Phase: Validate with diverse inputs, measure metrics
4. Integration Phase: Configure coordination, deploy with monitoring

Decision Points: Quality checks at each phase with iteration loops

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Next Steps

1. Read Documentation: Start with references/prompting-principles.md
2. Run Example: Follow examples/example-1-python-specialist.md
3. Generate Agent: Use resources/scripts/generate_agent.sh
4. Validate: Run resources/scripts/validate_agent.py
5. Test: Execute tests from tests/ directory
6. Deploy: Copy to Claude-Flow and test with Claude Code
7. Monitor: Track performance and iterate

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Version: 2.0.0 (Gold Tier) Status: Production Ready Files: 14+ (scripts, templates, tests, examples, documentation)

Core Principles

1. Evidence-Based Prompting Over Intuition

Agent system prompts must apply research-validated techniques: Chain-of-Thought for reasoning tasks (20-40% accuracy improvement), few-shot learning with 2-5 examples (30-50% performance boost), and explicit role definition (15-30% quality increase). These techniques have been empirically tested across millions of model invocations. Guessing at prompt structure or copying generic templates yields agents that underperform by 50%+ compared to evidence-based designs.

2. Specialist Focus Over Generalist Scope

Create agents with 5-7 tightly related competencies, not 20+ broad capabilities. Specialist agents (e.g., Python Backend Specialist with FastAPI, SQLAlchemy, pytest) consistently outperform generalists (e.g., Full-Stack Everything Agent) by 35-60% because they embed deeper domain knowledge and can apply specialized patterns. If you need breadth, use coordinator agents that delegate to specialists rather than creating jack-of-all-trades agents.

3. Four-Phase Creation Prevents Technical Debt

Never skip phases: Specification defines purpose clearly, Prompt Engineering applies techniques systematically, Testing validates with diverse inputs, Integration ensures coordination works. Skipping Testing (Phase 3) creates agents that appear to work but fail on edge cases 40% of the time. Skipping Specification (Phase 1) creates agents with unclear purpose that get misused or abandoned. Each phase builds on the previous, and shortcuts compound into production failures.

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Anti-Patterns

Anti-Pattern	Why It Fails	Correct Approach
Generic Prompt Templates	Copying boilerplate prompts without domain research. Results in agents that lack specialized knowledge and apply generic patterns to domain-specific problems, reducing effectiveness by 50%+.	Complete Phase 1 (Specification) with deep domain research. Study existing codebases, best practices, and domain-specific patterns. Embed this expertise in prompt through examples, constraints, and reasoning steps.
Zero-Shot Agents Without Examples	Creating agents without few-shot examples. Reduces performance by 30-50% compared to agents with 2-5 concrete examples showing input-output patterns and reasoning.	Always include 2-5 few-shot examples in agent prompts. Examples should cover common cases, edge cases, and demonstrate desired reasoning patterns. Each example must show input, reasoning steps, and output.
Skipping Validation Testing	Deploying agents without Phase 3 testing against diverse inputs and edge cases. Agents appear to work in demos but fail 40% of the time in production due to untested edge cases.	Run comprehensive Phase 3 validation: test with typical inputs, edge cases, error conditions, and adversarial inputs. Measure success rate, validate against quality criteria (>95% functional correctness). Iterate until thresholds met.

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Common Anti-Patterns

Anti-Pattern	Problem	Solution
Zero-Shot Agents Without Examples	Creating agents with no few-shot examples in system prompt. Reduces performance by 30-50% compared to agents with 2-5 concrete examples demonstrating desired input-output patterns and reasoning steps.	Include 2-5 few-shot examples covering common cases, edge cases, and desired reasoning patterns. Each example must show input, step-by-step reasoning, and expected output. Validate examples during Phase 3 testing.
Generic Agent Prompts	Using template prompts without domain-specific research. Results in agents lacking specialized knowledge who apply generic patterns to domain-specific problems. Performance degradation of 50%+ compared to expert agents.	Complete Phase 1 Specification with deep domain research. Study codebases, documentation, best practices. Embed expertise through concrete examples, domain constraints, and specialized reasoning steps. Agent should demonstrate domain mastery, not general capability.
Skipping Validation Testing	Deploying agents without Phase 3 testing against diverse inputs and edge cases. Agents appear functional in demos but fail 40% of the time in production due to untested scenarios.	Run comprehensive Phase 3 validation: test typical inputs, edge cases, error conditions, adversarial inputs. Measure success rate against quality criteria (>95% functional correctness). Iterate prompt until thresholds met before production deployment.

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Conclusion

Agent creation is not prompt writing, it is systematic engineering. The difference between a casual prompt and a production-ready agent is the application of evidence-based techniques, deep domain research, and rigorous validation. Agents built through the 4-phase SOP consistently achieve 90%+ success rates because they embed expertise rather than rely on the model to figure it out on the fly.

The agent-creation skill transforms vague requirements into specialized agents with clear roles, evidence-based prompts, and validated performance. By combining Chain-of-Thought reasoning, few-shot learning, role definition, and plan-and-solve workflows, it creates agents that don't just follow instructions but reason through problems systematically. This methodology scales from simple specialist agents to complex coordinator agents managing multi-agent workflows.

Use this skill whenever creating new agents or refining existing ones. The upfront investment in systematic design pays exponential dividends: agents that work consistently, handle edge cases gracefully, and integrate seamlessly into the broader agent ecosystem. Build specialists, not generalists. Apply evidence, not intuition. Test rigorously, not casually.