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streamline

Use when the workflow feels too complex, has accumulated cruft, or has redundant steps and overlapping tools that need consolidation.

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sharpdeveye/maestro星标 591(GitHub 591 + cccskills 0)

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  • SKILL.md3.0 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 agent-architecture reference in the agent-workflow skill for complexity assessment and topology simplification.


Reduce complexity without reducing capability. Every component should earn its place.

Streamlining Analysis

For each component, ask:

  1. Does this add measurable value? If you can't name the specific value, remove it.
  2. Can this be combined with another component? Merge overlapping responsibilities.
  3. Is this solving a real problem or an imagined one? Remove speculative complexity.
  4. Would a simpler alternative work? Prefer simplicity over elegance.

Common Streamlining Targets

Pipeline Steps

  • Remove steps that transform data without changing it meaningfully
  • Combine sequential steps that could be one prompt
  • Eliminate validation steps that duplicate downstream validation

Tool Consolidation

  • Merge tools that operate on the same data with different filters
  • Remove tools the model never selects (check usage logs)
  • Combine read tools with similar signatures

Prompt Simplification

  • Remove instructions the model follows by default
  • Consolidate redundant constraints
  • Shorten few-shot examples to minimum viable length

Configuration Reduction

  • Remove config parameters that always use the default
  • Hardcode values that never change between environments
  • Merge related config into logical groups

Streamlining Report

For each recommendation:

  1. What to remove/simplify — specific component or code
  2. Why it's safe — what ensures functionality is preserved
  3. Expected impact — latency reduction, cost reduction, or maintainability improvement

Complexity Score

ComponentCurrentMinimal ViableAction
Pipeline steps??Remove/merge ?
Tools??Consolidate ?
Config params??Remove ?
Agent count??Collapse ?

Streamlining Checklist

  • Each component justified with concrete value
  • Redundant steps identified and marked for removal
  • Tool usage logs checked for never-used tools
  • Golden test set passes after each simplification
  • No safety guardrails, logging, or error handling removed

Recommended Next Step

After streamlining, run /evaluate to verify functionality is preserved, then /refine for final polish.

NEVER:

  • Remove error handling in the name of simplicity
  • Streamline without verifying the golden test set still passes
  • Remove logging or observability
  • Simplify below the level of correctness
  • Remove safety guardrails

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同一仓库的技能

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

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

calibrate

免费

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/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

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