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SKILL verified MIT Self-run

Turbocharge

skill-sharpdeveye-maestro-turbocharge · by sharpdeveye

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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Install

$ agentstack add skill-sharpdeveye-maestro-turbocharge

✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets No
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

View the full security report →

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Reliability & compatibility

Security review passed
0 installs to date
no reviews yet
3mo ago

Declared compatibility

Claude CodeClaude Desktop

Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.

Preview Execution monitoring

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About

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

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

Reviews

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Versions

  • v0.1.0 Imported from the upstream source.