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Use when workflow components are inconsistent, naming conventions vary, or a new team member's work needs alignment to project standards.
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Based on SOC occupation classification
| name | calibrate |
| description | Use when workflow components are inconsistent, naming conventions vary, or a new team member's work needs alignment to project standards. |
| argument-hint | [target area] |
| category | fix |
| 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 prompt-engineering reference in the agent-workflow skill for naming and style consistency patterns.
Ensure consistency across all workflow components. Inconsistency creates confusion — for the model, for developers, and for users.
Naming Conventions
Prompt Style
Error Handling
Logging
.maestro.md with the established conventions.| Dimension | Standard | Deviations Found | Priority |
|---|---|---|---|
| Tool naming | ? | ? of ? tools | High/Med/Low |
| Prompt structure | ? | ? of ? prompts | High/Med/Low |
| Error format | ? | ? of ? tools | High/Med/Low |
| Log format | ? | ? of ? entries | High/Med/Low |
.maestro.md with established conventionsAfter calibration, run /refine for a final polish pass, or /evaluate to verify consistency improvements.
NEVER:
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.
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.