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turbocharge

turbocharge

github.com/sharpdeveyeAgent workflows592331 installsUpdated 26w ago
Repository

About turbocharge

A skill for improving an existing workflow by proposing and evaluating targeted enhancements. It asks agents to present options before implementation and verify performance, graceful degradation, cost, and maintainability.

Key features

  1. 01Compare enhancement directions and trade-offs
  2. 02Get approval before implementation
  3. 03Apply throughput or latency techniques
  4. 04Add reliability or adaptive routing techniques
  5. 05Test performance and graceful degradation

Use cases

  1. 01Improve throughput in an established workflow
  2. 02Reduce latency on time-critical tasks
  3. 03Add recovery checks to reliability-critical processes

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/sharpdeveye/maestro --skill turbocharge

After installing, type/turbochargein Claude Code to confirm it loaded.

Details

From the project README

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

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