Meta Generation Skill

Overview

This skill enables AI-powered generation of Paracle framework artifacts from natural language descriptions. It leverages the paracle_meta engine to create well-structured, validated components.

When to Use

Use this skill when you need to:

•Create new agent specifications from requirements
•Generate workflow definitions from process descriptions
•Build skill templates from capability descriptions
•Draft policy documents from compliance requirements

Prerequisites

•
LLM provider configured (one of):
- •ANTHROPIC_API_KEY for Claude
- •OPENAI_API_KEY for GPT models
- •Ollama running locally
•
paracle_meta package available

Generation Commands

Generate an Agent

python

from paracle_meta import MetaAgent, GenerationRequest

async with MetaAgent() as meta:
    result = await meta.generate_agent(
        name="SecurityAuditor",
        description="Reviews code for security vulnerabilities following OWASP guidelines"
    )
    print(result.content)

Generate a Workflow

python

result = await meta.generate_workflow(
    name="code-review-pipeline",
    description="Multi-stage code review with security and quality checks"
)

Generate a Skill

python

result = await meta.generate_skill(
    name="api-testing",
    description="Automated REST API testing with validation"
)

Generate a Policy

python

result = await meta.generate_policy(
    name="data-retention",
    description="GDPR-compliant data retention policy"
)

Provider Selection

python

# Use specific provider
result = await meta.generate_agent(
    name="MyAgent",
    description="...",
    provider="anthropic",  # or "openai", "ollama"
    model="claude-sonnet-4-20250514"
)

# Cost-optimized (uses cheapest suitable provider)
result = await meta.generate_agent(
    name="MyAgent",
    description="...",
    optimize_cost=True
)

Output Locations

Generated artifacts are saved to:

•Agents: .parac/agents/specs/{name}.md
•Workflows: .parac/workflows/{name}.yaml
•Skills: .parac/agents/skills/{name}/SKILL.md
•Policies: .parac/policies/{name}.md

Quality Scoring

Each generation includes quality metrics:

python

result = await meta.generate_agent(...)

print(f"Quality Score: {result.quality_score}")
print(f"Tokens Used: {result.tokens_input + result.tokens_output}")
print(f"Cost: ${result.cost:.4f}")

Best Practices

•Be Specific: Detailed descriptions produce better results
•Provide Context: Include domain-specific requirements
•Review Output: Always review and validate generated artifacts
•Iterate: Use feedback to improve generations
•Track Costs: Monitor token usage and costs

Error Handling

python

try:
    result = await meta.generate_agent(...)
except ProviderError as e:
    print(f"Provider error: {e}")
    # Try fallback provider
except GenerationError as e:
    print(f"Generation failed: {e}")

Integration with CLI

bash

# Generate via CLI (future)
paracle generate agent SecurityAuditor \
    --description "Reviews code for security vulnerabilities"

paracle generate workflow review-pipeline \
    --description "Multi-stage code review"

Related Skills

•workflow-orchestration: Execute generated workflows
•agent-configuration: Configure generated agents
•tool-integration: Add tools to generated agents