// Use when the workflow is too slow, too expensive, or both and needs latency, cost, or token usage optimization.
Use when porting a workflow to a different AI provider, deployment environment, model tier, or organizational context.
Use when any Maestro command is invoked — provides foundational workflow design principles across prompt engineering, context management, tool orchestration, agent architecture, feedback loops, knowledge systems, and guardrails.
Use when the workflow works but needs to handle more complex cases or produce higher-quality output through better tools, context, prompts, or models.
Use when workflow components are inconsistent, naming conventions vary, or a new team member's work needs alignment to project standards.
Capture a session summary — what was done, what decisions were made, and what to do next.
Use when the workflow needs multi-step processing with sequential, parallel, or conditional tool compositions and proper data flow.
| name | accelerate |
| description | Use when the workflow is too slow, too expensive, or both and needs latency, cost, or token usage optimization. |
| argument-hint | [target metric] |
| category | enhancement |
| version | 2.0.0 |
| user-invocable | true |
Invoke /agent-workflow — it contains workflow principles, anti-patterns, and the Context Gathering Protocol. Follow the protocol before proceeding — if no workflow context exists yet, you MUST run /teach-maestro first. Consult the context-management reference in the agent-workflow skill for window optimization and budget strategies.
Make the workflow faster and cheaper without sacrificing quality. Measure before and after.
Measure current performance:
Current metrics:
Latency (p50): ___ms
Latency (p95): ___ms
Cost per request: $___
Token usage (avg): ___ input / ___ output
Error rate: ___%
Reduce Token Usage
Model Cascading
Caching
Parallelization
Context Optimization
For each optimization:
After optimization, run /evaluate to verify quality didn't degrade, or /iterate to set up continuous monitoring.
NEVER: