/calibrate
Use when workflow components are inconsistent, naming conventions vary, or a new team member's work needs alignment to project standards.
$ npx -y skills add sharpdeveye/maestro --skill calibrate --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/calibrate
Context preview
The summary Claude sees to decide when to auto-load this skill.
Use when workflow components are inconsistent, naming conventions vary, or a new team member's work needs alignment to project standards.
SKILL.md
calibrate.SKILL.mdname: 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
MANDATORY PREPARATION
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.
Calibration Dimensions
**Naming Conventions**
- Tool names follow consistent pattern (verb_noun, noun.verb, or camelCase — pick one)
- Agent names follow consistent pattern
- Configuration keys follow consistent pattern
- File names follow consistent pattern
**Prompt Style**
- All prompts use the same structural pattern (4-zone)
- Consistent delimiter style (XML tags, markdown headers, triple-dash)
- Consistent output schema format (JSON schema, markdown template)
- Consistent instruction style (imperative, numbered steps)
**Error Handling**
- All tools return errors in the same format
- Error codes follow consistent scheme
- Error messages follow consistent tone
- Retry logic uses consistent strategy
**Logging**
- All logs use the same format (JSON structured, text, etc.)
- Consistent field names across all log entries
- Consistent log levels (debug, info, warn, error)
- Consistent PII redaction approach
Calibration Process
1. **Identify the standard**: What's the most common pattern in the existing codebase? That's the standard. 2. **List deviations**: Find all components that deviate from the standard. 3. **Prioritize**: Fix the most impactful deviations first (user-facing > internal). 4. **Apply**: Make the changes, ensuring tests still pass. 5. **Document**: Update `.maestro.md` with the established conventions.
Consistency Audit Table
| 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 |
Calibration Checklist
- [ ] Convention standard identified for each dimension
- [ ] All deviations listed with location
- [ ] Highest impact deviations fixed first
- [ ] Tests pass after each calibration change
- [ ] Updated `.maestro.md` with established conventions
Recommended Next Step
After calibration, run `/refine` for a final polish pass, or `/evaluate` to verify consistency improvements.
**NEVER**:
- Invent new conventions when existing ones work
- Calibrate in a way that changes behavior (this is standardization, not refactoring)
- Skip test verification after calibration
- Change naming conventions without updating all references
Read more
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
MANDATORY PREPARATION
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.
Calibration Dimensions
**Naming Conventions**
- Tool names follow consistent pattern (verb_noun, noun.verb, or camelCase — pick one)
- Agent names follow consistent pattern
- Configuration keys follow consistent pattern
- File names follow consistent pattern
**Prompt Style**
- All prompts use the same structural pattern (4-zone)
- Consistent delimiter style (XML tags, markdown headers, triple-dash)
- Consistent output schema format (JSON schema, markdown template)
- Consistent instruction style (imperative, numbered steps)
**Error Handling**
- All tools return errors in the same format
- Error codes follow consistent scheme
- Error messages follow consistent tone
- Retry logic uses consistent strategy
**Logging**
- All logs use the same format (JSON structured, text, etc.)
- Consistent field names across all log entries
- Consistent log levels (debug, info, warn, error)
- Consistent PII redaction approach
Calibration Process
1. **Identify the standard**: What's the most common pattern in the existing codebase? That's the standard. 2. **List deviations**: Find all components that deviate from the standard. 3. **Prioritize**: Fix the most impactful deviations first (user-facing > internal). 4. **Apply**: Make the changes, ensuring tests still pass. 5. **Document**: Update `.maestro.md` with the established conventions.
Consistency Audit Table
| 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 |
Calibration Checklist
- [ ] Convention standard identified for each dimension
- [ ] All deviations listed with location
- [ ] Highest impact deviations fixed first
- [ ] Tests pass after each calibration change
- [ ] Updated `.maestro.md` with established conventions
Recommended Next Step
After calibration, run `/refine` for a final polish pass, or `/evaluate` to verify consistency improvements.
**NEVER**:
- Invent new conventions when existing ones work
- Calibrate in a way that changes behavior (this is standardization, not refactoring)
- Skip test verification after calibration
- Change naming conventions without updating all references
Workflow fluency for AI coding agents. 1 core skill · 25 commands · 7 domain references · memory layer · audit trail — works across Cursor, Claude Code, Gemini CLI, Copilot, and 6 more.
Repo: sharpdeveye/maestro
Other skills on maestro.
- /accelerate
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Open skill - /adapt-workflow
Use when porting a workflow to a different AI provider, deployment environment, model tier, or organizational context.
Open skill - /agent-workflow
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.
Open skill - /amplify
Use when the workflow works but needs to handle more complex cases or produce higher-quality output through better tools, context, prompts, or models.
Open skill - /capture
Capture a session summary — what was done, what decisions were made, and what to do next.
Open skill - /chain
Use when the workflow needs multi-step processing with sequential, parallel, or conditional tool compositions and proper data flow.
Open skill