Use when the workflow is too slow, too expensive, or both and needs latency, cost, or token usage optimization.
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sharpdeveye/maestro5912026年4月29日 更新
免费在 GitHub 公开
turbocharge
Use when the user wants to push past conventional workflow limits with advanced performance techniques like parallel orchestration, streaming pipelines, or adaptive routing.
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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.
Start your response with:
──────────── ⚡ TURBOCHARGE ─────────────
》》》 Entering turbocharge mode...
Push a workflow past conventional limits. This isn't about adding features — it's about making existing capabilities operate at a level users didn't think was possible.
EXTRA IMPORTANT: Context determines what "extraordinary" means. Understand the project's scale before deciding what to turbocharge.
Propose Before Building
- Think through 2-3 different directions with trade-offs
- Present these options to the user and wait for their selection before writing code
- Only proceed with the confirmed direction
For high-throughput workflows
- Parallel fan-out: Split input, process N simultaneously, merge results
- Streaming pipelines: Start processing step N+1 while step N runs
- Progressive quality: Fast pass on everything, detailed pass on flagged items
- Smart batching: Group similar items, outliers get individual treatment
For latency-critical workflows
- Speculative execution: Start likely next step before current finishes
- Cached warm paths: Pre-compute responses for common patterns
- Model cascading: Try fastest model first, escalate only when needed
For reliability-critical workflows
- Automatic failover: Detect failures, switch to alternatives automatically
- State checkpointing: Save state, resume from any point after crash
- Chaos testing: Intentionally break dependencies to verify recovery
For adaptive workflows
- Complexity routing: Route simple inputs to fast paths, complex to thorough
- Dynamic model selection: Choose model based on task requirements
- Feedback-driven optimization: Track what works best, adapt routing
Progressive enhancement is non-negotiable
Every turbocharge technique must degrade gracefully. The workflow without the enhancement must still work.
Verification
- Performance test: Is it measurably faster/cheaper/more reliable?
- Degradation test: Disable enhancement — does it still work?
- Cost test: Does improvement justify complexity?
- Maintenance test: Can someone else maintain this in 6 months?
Recommended Next Step
After turbocharging, run /evaluate to verify the enhancement works and degrades gracefully.
NEVER:
- Turbocharge before the workflow is correct (make it right, then make it fast)
- Add complexity without measuring the improvement
- Build self-healing without testing the healing
- Layer multiple turbocharge techniques at once
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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
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