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Token Budget Advisor

Community
Fmarzochi
token-budget-advisor

Offers the user an informed choice about how much response depth to consume before answering. Use this skill when the user explicitly wants to control response length, depth, or token budget. TRIGGER when: "token budget", "token count", "token usage", "token limit", "response length", "answer depth", "short version", "brief answer", "detailed answer", "exhaustive answer", "respuesta corta vs larga", "cuántos tokens", "ahorrar tokens", "responde al 50%", "dame la versión corta", "quiero controlar cuánto usas", or clear variants where the user is explicitly asking to control answer size or depth. DO NOT TRIGGER when: user has already specified a level in the current session (maintain it), the request is clearly a one-word answer, or "token" refers to auth/session/payment tokens rather than response size.

Overview

PublisherFmarzochi
RepositoryEGC
Skill nametoken-budget-advisor
Stars
68
Forks
49
Bundled files
Instructions only
LicenseApache-2.0
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 Fmarzochi on GitHub. Read the source before you install it.

Installation

Install the Token Budget Advisor 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/Fmarzochi/EGC/tree/main/skills/ai/token-budget-advisor
  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/Fmarzochi/EGC.git /tmp/EGC
mkdir -p .claude/skills
cp -r /tmp/EGC/skills/ai/token-budget-advisor .claude/skills/token-budget-advisor
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Token Budget Advisor 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 Budget Advisor 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 Budget Advisor 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 Budget Advisor (TBA)

Intercept the response flow to offer the user a choice about response depth before Gemini answers.

When to Use

  • User wants to control how long or detailed a response is
  • User mentions tokens, budget, depth, or response length
  • User says "short version", "tldr", "brief", "al 25%", "exhaustive", etc.
  • Any time the user wants to choose depth/detail level upfront

Do not trigger when: user already set a level this session (maintain it silently), or the answer is trivially one line.

How It Works

Step 1: Estimate input tokens

Use the repository's canonical context-budget heuristics to estimate the prompt's token count mentally.

Use the same calibration guidance as context-budget:

  • prose: words × 1.3
  • code-heavy or mixed/code blocks: chars / 4

For mixed content, use the dominant content type and keep the estimate heuristic.

Step 2: Estimate response size by complexity

Classify the prompt, then apply the multiplier range to get the full response window:

ComplexityMultiplier rangeExample prompts
Simple3× – 8×"What is X?", yes/no, single fact
Medium8× – 20×"How does X work?"
Medium-High10× – 25×Code request with context
Complex15× – 40×Multi-part analysis, comparisons, architecture
Creative10× – 30×Stories, essays, narrative writing

Response window = input_tokens × mult_min to input_tokens × mult_max (but don’t exceed your model’s configured output-token limit).

Step 3: Present depth options

Present this block before answering, using the actual estimated numbers:

Analyzing your prompt...

Input: ~[N] tokens  |  Type: [type]  |  Complexity: [level]  |  Language: [lang]

Choose your depth level:

[1] Essential   (25%)  ->  ~[tokens]   Direct answer only, no preamble
[2] Moderate    (50%)  ->  ~[tokens]   Answer + context + 1 example
[3] Detailed    (75%)  ->  ~[tokens]   Full answer with alternatives
[4] Exhaustive (100%)  ->  ~[tokens]   Everything, no limits

Which level? (1-4 or say "25% depth", "50% depth", "75% depth", "100% depth")

Precision: heuristic estimate ~85-90% accuracy (±15%).

Level token estimates (within the response window):

  • 25% → min + (max - min) × 0.25
  • 50% → min + (max - min) × 0.50
  • 75% → min + (max - min) × 0.75
  • 100% → max

Step 4: Respond at the chosen level

LevelTarget lengthIncludeOmit
25% Essential2-4 sentences maxDirect answer, key conclusionContext, examples, nuance, alternatives
50% Moderate1-3 paragraphsAnswer + necessary context + 1 exampleDeep analysis, edge cases, references
75% DetailedStructured responseMultiple examples, pros/cons, alternativesExtreme edge cases, exhaustive references
100% ExhaustiveNo restrictionEverything: full analysis, all code, all perspectivesNothing

Shortcuts: skip the question

If the user already signals a level, respond at that level immediately without asking:

What they sayLevel
"1" / "25% depth" / "short version" / "brief answer" / "tldr"25%
"2" / "50% depth" / "moderate depth" / "balanced answer"50%
"3" / "75% depth" / "detailed answer" / "thorough answer"75%
"4" / "100% depth" / "exhaustive answer" / "full deep dive"100%

If the user set a level earlier in the session, maintain it silently for subsequent responses unless they change it.

Precision note

This skill uses heuristic estimation: no real tokenizer. Accuracy ~85-90%, variance ±15%. Always show the disclaimer.

Examples

Triggers

  • "Give me the short version first."
  • "How many tokens will your answer use?"
  • "Respond at 50% depth."
  • "I want the exhaustive answer, not the summary."
  • "Dame la version corta y luego la detallada."

Does Not Trigger

  • "What is a JWT token?"
  • "The checkout flow uses a payment token."
  • "Is this normal?"
  • "Complete the refactor."
  • Follow-up questions after the user already chose a depth for the session

Source

Standalone skill from TBA: Token Budget Advisor for Claude Code. Original project also ships a Python estimator script, but this repository keeps the skill self-contained and heuristic-only.

Frequently asked questions

What does the Token Budget Advisor AI skill do?

Offers the user an informed choice about how much response depth to consume before answering. Use this skill when the user explicitly wants to control response length, depth, or token budget. TRIGGER when: "token budget", "token count", "token usage", "token limit", "response length", "answer depth", "short version", "brief answer", "detailed answer", "exhaustive answer", "respuesta corta vs larga", "cuántos tokens", "ahorrar tokens", "responde al 50%", "dame la versión corta", "quiero controlar cuánto usas", or clear variants where the user is explicitly asking to control answer size or de...

Why use Token Budget Advisor on TypingMind?

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

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

Which AI models can use Token Budget Advisor?

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 Budget Advisor?

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

Is the Token Budget Advisor AI skill free?

Yes. It is published on GitHub by Fmarzochi under the Apache-2.0 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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