Design workflows in which AI agents handle multiple steps or specialist tasks. Choose a suitable structure, generate starter configurations, and define information handoffs, retries, validation checks, and limits on cost and context size.
Design production-grade multi-agent workflows with clear pattern choice (sequential, parallel, hierarchical), handoff contracts, failure handling, and cost/context controls. Use when architecting a multi-step agent pipeline, choosing between single-agent vs multi-agent approaches, or refactoring an LLM workflow that suffers from context bloat or unreliable handoffs.
Review the instructions the agent will receive before you install.
Tier: POWERFUL
Category: Engineering
Domain: Multi-Agent Systems / AI Orchestration
Design production-grade multi-agent workflows with clear pattern choice, handoff contracts, failure handling, and cost/context controls.
# Generate a sequential workflow skeleton
python3 scripts/workflow_scaffolder.py sequential --name content-pipeline
# Generate an orchestrator workflow and save it
python3 scripts/workflow_scaffolder.py orchestrator --name incident-triage --output workflows/incident-triage.json
sequential: strict step-by-step dependency chainparallel: fan-out/fan-in for independent subtasksrouter: dispatch by intent/type with fallbackorchestrator: planner coordinates specialists with dependenciesevaluator: generator + quality gate loopDetailed templates: references/workflow-patterns.md
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Audit web applications for accessibility issues against WCAG 2.2 Level A and AA, the web accessibility standards. Get framework-specific code fixes, contrast checks, verification steps, and reports, with options for automated checks in development pipelines.
Plan A/B tests that compare page variations, from headlines to pricing layouts. Define a hypothesis, choose success metrics, calculate sample size and duration, and prepare launch checks and results reports to support decisions.
Create paid ad headlines and body copy tailored to each platform's character limits. Use existing performance data to develop variations, explore new messaging angles, and check copy for length, quality, and potential rejection risks.
Review code changes from three critical perspectives: production failures, maintainability, and security. Get a consolidated report that ranks findings by severity and provides merge guidance, helping examine assumptions that an ordinary self-review may overlook.
Audit and improve content intended for citation in AI answers. Review evidence, author information, and structure, generate revised content and structured metadata, and keep a local record of observed citations across AI services.
Create signed records for consequential agent actions such as deployments, deletions, and payments. Decide which actions need evidence, record the agent, action, target, and policy, then verify the resulting certificate offline to detect later changes.