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Token Efficiency

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rohitg00
token-efficiency

Reduce token waste by 40-60% through anti-sycophancy rules, tool-call budgets, one-pass coding, task profiles, and read-before-write enforcement. Inspired by drona23/claude-token-efficient.

Overview

Publisherrohitg00
Repositorypro-workflow
Skill nametoken-efficiency
Stars
2.9K
Forks
286
Bundled files
Instructions only
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 rohitg00 on GitHub. Read the source before you install it.

Installation

Install the Token Efficiency 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.
  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/rohitg00/pro-workflow.git /tmp/pro-workflow
mkdir -p .claude/skills
cp -r /tmp/pro-workflow/skills/token-efficiency .claude/skills/token-efficiency
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Token Efficiency 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 Token Efficiency 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 Token Efficiency 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.

Token Efficiency

Reduce output token waste and prevent iteration cycles that consume context.

Trigger

Use when:

  • Sessions feel expensive or slow
  • Output is verbose with filler text
  • Claude is re-reading files or iterating unnecessarily
  • Setting up a new project for token-efficient work

Anti-Sycophancy Rules

These patterns waste 30-60% of output tokens:

PatternExampleFix
Sycophantic opener"Sure! Great question!"Delete. Lead with answer.
Prompt restatement"You're asking about X..."Delete. Answer directly.
Closing fluff"Let me know if you need anything!"Delete. Stop after the answer.
Unsolicited suggestions"You might also want to..."Delete unless asked.
AI disclaimers"As an AI model..."Delete entirely.
Verbose preambles"I'll help you with that..."Delete. Start with the action.

Tool-Call Budgets

Set explicit budgets by task complexity:

Task TypeTool-Call BudgetWrap-Up At
Quick fix / lookup20 calls15
Bug fix30 calls25
Feature (small)50 calls40
Feature (large)80 calls65
Refactor50 calls40
Exploration / research30 calls25

At the wrap-up threshold: commit progress, assess remaining work, decide whether to continue or start fresh.

One-Pass Coding Discipline

For simple-to-medium tasks:

  1. Read all relevant files including tests first
  2. Understand what tests assert before coding
  3. Write complete solution in one pass — not incrementally
  4. Run tests once — if pass, STOP immediately
  5. If fail: read the error, fix once, retest
  6. Never iterate more than twice on the same failure — rethink approach
  7. Never refactor, improve, or polish passing code

Task Profiles

Switch profiles based on what you're doing:

Coding Profile

  • Return code first, explanation after (only if non-obvious)
  • Simplest working solution, no over-engineering
  • Read file before modifying — always
  • No docstrings on unchanged code
  • No error handling for impossible scenarios
  • State bug, show fix, stop

Agent/Pipeline Profile

  • Structured output only: JSON, bullets, tables
  • No prose unless targeting a human reader
  • Every output must be parseable without post-processing
  • Execute task, do not narrate actions
  • Never invent file paths, API endpoints, or function names
  • If unknown: return null or "UNKNOWN", never guess

Analysis Profile

  • Lead with finding, context and methodology after
  • Tables and bullets over prose
  • Numbers must include units
  • Never fabricate data points
  • Summary first (3 bullets max), caveats last

Read-Before-Write Enforcement

Hard rules:

  1. Never write a file you haven't read in this session
  2. Never re-read a file already read unless it was modified
  3. Read tests before coding — understand what passes before writing
  4. Read error output carefully before attempting a fix

ASCII-Only Output

Use ASCII characters only in all output:

  • -- not (em dash)
  • " not " " (smart quotes)
  • ' not ' ' (curly apostrophes)
  • No emoji unless explicitly requested
  • No Unicode decorators or special characters

This ensures clean copy-paste for code and compatibility with downstream systems.

Measuring Impact

Track these metrics to measure token savings:

  • Output length: average words per response (target: 30-50% reduction)
  • Tool calls per task: should stay within budget tier
  • Re-read count: should be near zero
  • Write-without-read count: should be zero
  • Iteration cycles: tests should pass in 1-2 attempts, not 5+

Attribution

Token efficiency patterns adapted from drona23/claude-token-efficient (MIT).

Frequently asked questions

What does the Token Efficiency AI skill do?

Reduce token waste by 40-60% through anti-sycophancy rules, tool-call budgets, one-pass coding, task profiles, and read-before-write enforcement. Inspired by drona23/claude-token-efficient.

Why use Token Efficiency on TypingMind?

Because you install it once and use it with any model. Token Efficiency 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 Token Efficiency in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/rohitg00/pro-workflow/tree/main/skills/token-efficiency. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Token Efficiency?

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 Token Efficiency?

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

Is the Token Efficiency AI skill free?

It is published on GitHub by rohitg00. Check the repository for licensing terms. 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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