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Turbocharge

Community
sharpdeveye
turbocharge

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

Overview

Publishersharpdeveye
Repositorymaestro
Skill nameturbocharge
Stars
591
Forks
73
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by sharpdeveye on GitHub. Read the source before you install it.

Installation

Install the Turbocharge AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/sharpdeveye/maestro/tree/main/source/skills/turbocharge
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/sharpdeveye/maestro.git /tmp/maestro
mkdir -p .claude/skills
cp -r /tmp/maestro/source/skills/turbocharge .claude/skills/turbocharge
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Turbocharge in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Turbocharge on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Turbocharge is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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:

text
──────────── ⚡ 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

Frequently asked questions

What does the Turbocharge AI skill do?

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

Why use Turbocharge on TypingMind?

Because you install it once and use it with any model. Turbocharge is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Turbocharge in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/sharpdeveye/maestro/tree/main/source/skills/turbocharge. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Turbocharge?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Turbocharge?

As many as you like. As long as a model supports skills, you can use Turbocharge with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Turbocharge AI skill free?

Yes. It is published on GitHub by sharpdeveye under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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