Strands Workflow Spec Creation Skill

Expert guidance for building production-ready strands-cli workflow specifications.

Core Principles

1. Declarative Over Imperative: Define what the workflow should do, not how to execute it
2. Schema-First: All specs validate against strands-workflow.schema.json (JSON Schema Draft 2020-12)
3. Progressive Loading: Start simple, add complexity only when needed
4. Safe by Default: Always include budgets, retries, and validation

When to Use This Skill

Load this skill when you need to:

• Create a new strands-cli workflow from scratch
• Add new patterns (chain, parallel, routing, etc.) to existing specs
• Configure tools, runtime providers, or telemetry
• Debug schema validation errors
• Optimize workflow performance or token usage

Quick Start Template

version: 0
name: "my-workflow"
description: "Brief description of what this workflow does"

runtime:
  provider: bedrock  # or openai, ollama
  model_id: "anthropic.claude-3-sonnet-20240229-v1:0"
  region: "us-east-1"
  budgets:
    max_tokens: 100000
    max_duration_s: 300

agents:
  main-agent:
    prompt: "Your clear, specific instructions here"

pattern:
  type: chain  # Start with chain, evolve to other patterns
  config:
    steps:
      - agent: main-agent
        input: "Task description"

outputs:
  artifacts:
    - path: "./output.txt"
      from: "{{ last_response }}"

Progressive Loading Strategy

DO NOT load all reference files at once. Instead:

1. Start here for basic workflow structure and patterns
2. Load patterns.md when working with specific orchestration patterns (chain, routing, parallel, etc.)
3. Load tools.md when configuring custom tools or troubleshooting tool execution
4. Load advanced.md for context management, telemetry, security features
5. Load examples.md for real-world workflow patterns and templates
6. Load troubleshooting.md when debugging validation errors or runtime issues

Essential Workflow Elements

1. Runtime Configuration

runtime:
  provider: bedrock          # REQUIRED: bedrock | openai | ollama
  model_id: "..."            # Model identifier (provider-specific)
  region: "us-east-1"        # Required for Bedrock
  temperature: 0.7           # 0.0-2.0 (default: 0.7)
  max_tokens: 2000           # Per-message limit
  budgets:                   # Prevent runaway costs
    max_tokens: 100000       # Total token budget
    max_duration_s: 600      # Timeout in seconds
    max_steps: 50            # Max agent invocations

Provider Quick Reference:

• Bedrock: Requires region, uses AWS credentials (fully supported)
• OpenAI: Requires OPENAI_API_KEY env var (fully supported)
• Ollama: Requires host: "http://localhost:11434" (fully supported)

2. Agent Definition

agents:
  agent-name:
    prompt: |
      Clear, specific instructions.
      Use {{ variables }} for dynamic content.
    tools: ["python_exec", "http_request"]  # Optional
    runtime:                                 # Optional overrides
      temperature: 0.3
      max_tokens: 4000

Prompt Best Practices:

• Be specific about expected output format
• Include examples when format is complex
• Reference template variables with {{ var_name }}
• Keep prompts focused on single responsibility

3. Pattern Selection

Choose based on your coordination needs:

Pattern	Use Case	Complexity
chain	Sequential steps, each builds on previous	⭐ Simple
routing	Dynamic agent selection based on input	⭐⭐ Medium
parallel	Independent tasks executed concurrently	⭐⭐ Medium
workflow	DAG with dependencies, parallel where possible	⭐⭐⭐ Complex
graph	State machines with loops and conditionals	⭐⭐⭐⭐ Advanced
evaluator-optimizer	Iterative refinement with quality gates	⭐⭐⭐ Complex
orchestrator-workers	Dynamic task delegation	⭐⭐⭐⭐ Advanced

All 7 patterns are fully implemented and production-ready. Start with chain, migrate to complex patterns only when needed.

4. Inputs and Variables

inputs:
  required:
    topic: string                # Shorthand syntax
  optional:
    format:
      type: string
      description: "Output format"
      default: "markdown"
      enum: ["markdown", "json", "yaml"]

Use variables in prompts and artifacts:

prompt: "Research {{ topic }} and output as {{ format }}"

5. Outputs

outputs:
  artifacts:
    - path: "./artifacts/result.md"
      from: "{{ last_response }}"           # Last agent output
    - path: "./artifacts/trace.json"
      from: "{{ $TRACE }}"                  # Full execution trace

Template Variables:

• {{ last_response }} - Most recent agent output
• {{ steps[0].response }} - Chain step output (0-indexed)
• {{ tasks.task_id.response }} - Workflow task output
• {{ $TRACE }} - Complete execution trace

Common Validation Errors

Error: "Property 'version' is required"

Fix: Add version: 0 at top level

Error: "Property 'pattern.type' must be one of..."

Fix: Use valid pattern type: chain, routing, parallel, workflow, graph, evaluator-optimizer, orchestrator-workers

Error: "Agent 'xyz' not found in agents"

Fix: Ensure agent referenced in pattern is defined in agents: section

Error: "Invalid provider"

Fix: Use bedrock, openai, or ollama

Performance Optimization

1. Agent Caching: Reuse agents across steps with same configuration
2. Model Client Pooling: Same provider/model/region shares HTTP client
3. Parallel Execution: Use parallel or workflow patterns for independent tasks
4. Token Budgets: Set realistic limits to avoid runaway costs
5. Context Compression: Enable context_policy.compression for long workflows

Next Steps

Once you understand this core structure:

1. For pattern details: Load Skill("strands-spec/patterns")
2. For tool configuration: Load Skill("strands-spec/tools")
3. For advanced features: Load Skill("strands-spec/advanced")
4. For examples: Load Skill("strands-spec/examples")
5. For debugging: Load Skill("strands-spec/troubleshooting")

Schema Validation

Always validate before execution:

uv run strands validate my-workflow.yaml

Or use inline schema reference:

# yaml-language-server: $schema=./strands-workflow.schema.json
version: 0
# ...

Minimal Working Example

version: 0
name: "hello-world"

runtime:
  provider: bedrock
  model_id: "anthropic.claude-3-sonnet-20240229-v1:0"
  region: "us-east-1"

agents:
  greeter:
    prompt: "Say hello to {{ name }}"

inputs:
  required:
    name: string

pattern:
  type: chain
  config:
    steps:
      - agent: greeter
        input: "Greet the user"

outputs:
  artifacts:
    - path: "./greeting.txt"
      from: "{{ last_response }}"

Run with:

uv run strands run hello-world.yaml --var name="World"