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Skill

/turbocharge

Use when the user wants to push past conventional workflow limits with advanced performance techniques like parallel orchestration, streaming pipelines, or adaptive routing.

BOOST
From plugin
maestro
59325 skills
Install
$ npx -y skills add sharpdeveye/maestro --skill turbocharge --agent claude-code

How 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/turbocharge

Context preview

The summary Claude sees to decide when to auto-load this skill.

Use when the user wants to push past conventional workflow limits with advanced performance techniques like parallel orchestration, streaming pipelines, or adaptive routing.

SKILL.md

turbocharge.SKILL.md
name: turbocharge
description: "Use when the user wants to push past conventional workflow limits with advanced performance techniques like parallel orchestration, streaming pipelines, or adaptive routing."
argument-hint: "[target]"
category: enhancement
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.

---

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

1. **Think through 2-3 different directions** with trade-offs 2. **Present these options to the user and wait for their selection** before writing code 3. 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
Read more
Ships withmaestro

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.

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TypeScript
Language
MIT
License
5mo ago
Last commit
5mo ago
Created

Repo: sharpdeveye/maestro

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