Use when the workflow is too slow, too expensive, or both and needs latency, cost, or token usage optimization.
中文简介正在准备中。当前显示原文说明。
sharpdeveye/maestro5912026年4月29日 更新
免费在 GitHub 公开
calibrate
Use when workflow components are inconsistent, naming conventions vary, or a new team member's work needs alignment to project standards.
中文简介正在准备中。当前显示原文说明。
sharpdeveye/maestro星标 591(GitHub 591 + cccskills 0)
实测评分 未实测
查看安装方法包含的文件（1）
- SKILL.md3.1 KB
SKILL.md（原文）
安装前可以查看交给 AI 代理的指令内容。
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
- Identify the standard: What's the most common pattern in the existing codebase? That's the standard.
- List deviations: Find all components that deviate from the standard.
- Prioritize: Fix the most impactful deviations first (user-facing > internal).
- Apply: Make the changes, ensuring tests still pass.
- Document: Update
.maestro.mdwith 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.mdwith 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
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同一仓库的技能
Use when porting a workflow to a different AI provider, deployment environment, model tier, or organizational context.
中文简介正在准备中。当前显示原文说明。
sharpdeveye/maestro5912026年4月29日 更新
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.
中文简介正在准备中。当前显示原文说明。
sharpdeveye/maestro5912026年4月29日 更新
amplify
免费Use when the workflow works but needs to handle more complex cases or produce higher-quality output through better tools, context, prompts, or models.
中文简介正在准备中。当前显示原文说明。
sharpdeveye/maestro5912026年4月29日 更新
capture
免费Capture a session summary — what was done, what decisions were made, and what to do next.
中文简介正在准备中。当前显示原文说明。
sharpdeveye/maestro5912026年4月29日 更新
chain
免费Use when the workflow needs multi-step processing with sequential, parallel, or conditional tool compositions and proper data flow.
中文简介正在准备中。当前显示原文说明。
sharpdeveye/maestro5912026年4月29日 更新