Cookbook

Recipe 1: Research → Validate → Report

name: validated-research
inputs:
 - name: topic
steps:
 # Parallel research
 - id: source_a
 pre_tools:
 - type: web_search
 query: "{input.topic} overview"
 inject_as: data
 prompt: "Analyze: {data}"
 output_var: research_a
 - id: source_b
 pre_tools:
 - type: web_search
 query: "{input.topic} criticism debate"
 inject_as: data
 prompt: "Analyze contrarian view: {data}"
 output_var: research_b
 # Validate
 - id: validate
 type: evaluator
 depends_on: [source_a, source_b]
 input_var: research_a
 criteria: "Are sources cited? Is data specific? Score 1-10."
 eval_scoring: true
 eval_threshold: 6
 on_fail: retry
 retry_target: source_a
 max_retries: 1
 prompt: "validate"
 output_var: validation
 # Report
 - id: report
 depends_on: [validate, source_b]
 prompt: |
 Write a balanced report using:
 Main research: {research_a}
 Contrarian view: {research_b}
 output_var: final_report
output: final_report

Recipe 2: Router-Based Code Review

name: smart-review
inputs:
 - name: path
steps:
 - id: classify
 type: router
 tools: [Glob, Read]
 prompt: |
 Look at {input.path}. Classify as: frontend, backend, or infra.
 routes:
 frontend: [review_ui, review_a11y]
 backend: [review_api, review_security]
 infra: [review_security, review_perf]
 default_route: backend
 output_var: type
 - id: review_ui
 depends_on: [classify]
 tools: [Read, Grep]
 prompt: "Review UI code at {input.path} for component design and UX."
 output_var: ui_review
 - id: review_a11y
 depends_on: [classify]
 tools: [Read, Grep]
 prompt: "Review accessibility at {input.path}."
 output_var: a11y_review
 - id: review_api
 depends_on: [classify]
 tools: [Read, Grep]
 prompt: "Review API design at {input.path}."
 output_var: api_review
 - id: review_security
 depends_on: [classify]
 tools: [Read, Grep]
 prompt: "Security audit of {input.path}."
 output_var: security_review
 - id: review_perf
 depends_on: [classify]
 tools: [Read, Grep, Bash]
 prompt: "Performance review of {input.path}."
 output_var: perf_review
 - id: summary
 depends_on: [review_ui, review_a11y, review_api, review_security, review_perf]
 prompt: |
 Compile final review report from available reviews:
 UI: {ui_review}
 A11y: {a11y_review}
 API: {api_review}
 Security: {security_review}
 Performance: {perf_review}
 output_var: report
output: report

Recipe 3: Loop with Early Exit

name: iterative-improvement
inputs:
 - name: draft
steps:
 - id: improve
 type: loop
 items_var: "1,2,3,4,5" # Max 5 iterations
 max_parallel: 1 # Sequential (each builds on previous)
 loop_until: '{eval_score} > "8"' # Stop when quality is high enough
 step_template:
 type: agent
 prompt: |
 Improve this draft (iteration {item}):
 {draft}
 Fix any issues found. Make it clearer and more complete.
 prompt: "Improve iteratively"
 output_var: improved
 - id: evaluate
 type: evaluator
 depends_on: [improve]
 input_var: improved
 criteria: "Score quality 1-10."
 eval_scoring: true
 eval_threshold: 8
 on_fail: skip
 prompt: "evaluate"
 output_var: eval_score
output: improved

Recipe 4: Human-in-the-Loop Pipeline

name: safe-deploy
inputs:
 - name: changes
steps:
 - id: analyze
 prompt: "Analyze these changes for risk: {input.changes}"
 output_var: risk_analysis
 - id: approve
 type: gate
 depends_on: [analyze]
 prompt: |
 Risk analysis: {risk_analysis}

 Approve deployment?
 timeout_hours: 4
 on_timeout: error
 output_var: approval
 - id: deploy
 depends_on: [approve]
 condition: '{approval} contains "approved"'
 tools: [Bash]
 prompt: "Deploy the changes: {input.changes}"
 output_var: deploy_result
output: deploy_result

Recipe 5: Data Pipeline with Transforms

name: data-pipeline
inputs:
 - name: api_url
steps:
 - id: fetch
 pre_tools:
 - type: http_fetch
 url: "{input.api_url}"
 inject_as: raw_data
 prompt: "Parse this API response into structured JSON: {raw_data}"
 output_var: parsed
 output_schema: json
 - id: extract_items
 type: transform
 operation: json_extract
 input_var: parsed
 json_path: "items"
 prompt: "extract"
 output_var: items
 - id: filter_active
 type: transform
 operation: filter
 input_var: items
 prompt: "filter"
 output_var: active_items
 - id: analyze
 depends_on: [filter_active]
 prompt: "Analyze these items and identify trends: {active_items}"
 output_var: analysis
output: analysis

Recipe 6: Multi-Model Cost Optimization

name: cost-optimized
inputs:
 - name: document
steps:
 - id: classify
 model: claude-haiku-4-5 # Cheapest: 0ドル.25/M
 prompt: "Classify document type: {input.document}. Return: legal, technical, or business."
 output_var: doc_type
 - id: extract
 model: claude-haiku-4-5 # Simple extraction
 depends_on: [classify]
 prompt: "Extract key facts from: {input.document}"
 output_var: facts
 - id: analyze
 model: claude-sonnet-4-6 # Mid-tier: 3ドル/M
 depends_on: [extract]
 prompt: "Deep analysis of {doc_type} document. Facts: {facts}"
 output_var: analysis
 - id: executive_summary
 model: claude-opus-4-6 # Premium: 15ドル/M (only for final output)
 depends_on: [analyze]
 prompt: "Write executive summary: {analysis}"
 output_var: summary
output: summary

Pattern: Retry with Escalation

- id: generate
 model: claude-haiku-4-5
 retry:
 max: 2
 delay_ms: 1000
 fallback_models:
 - claude-sonnet-4-6 # Try Sonnet if Haiku fails
 - claude-opus-4-6 # Try Opus as last resort
 prompt: "Generate complex analysis..."
 output_var: result

Pattern: Cached Expensive Steps

- id: market_data
 model: claude-opus-4-6
 cache:
 enabled: true
 ttl_minutes: 360 # Cache for 6 hours
 pre_tools:
 - type: web_search
 query: "market analysis {input.sector}"
 inject_as: web_data
 prompt: "Comprehensive market analysis of {input.sector}: {web_data}"
 output_var: market_analysis

See Also