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Automations280k · #300

token-budget-advisor

Lets your agent ask about the desired response length before answering. It estimates token usage to help control the budget.

What it does

Helps you control the AI's response length. Before answering, it estimates how many 'tokens' (pieces of text) the response will use and offers depth options (short, medium, detailed, exhaustive).

When to use it

  • ✓When you want a shorter or longer answer.
  • ✓When you're concerned about 'token' usage or response size.
  • ✓When you ask for a 'summary' or a 'full' response.

Example of what to ask your AI

“Give me a short answer about how to bake a cake.”

Want more? Open the sections below.

How to use

The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.

Skill link

https://github.com/affaan-m/ECC/blob/main/skills/token-budget-advisor/SKILL.md

Ready-to-paste message

Use this skill to help me: https://github.com/affaan-m/ECC/blob/main/skills/token-budget-advisor/SKILL.md Read the SKILL.md file at that link and follow its instructions for my task.
How it works

The AI first estimates how many 'tokens' your question uses. Then, it calculates the estimated response size based on your question's complexity and offers depth options (25%, 50%, 75%, 100%).

Installation

Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.

In the terminal (Claude Code, Codex, Cursor), in your project folder:

npx skills add affaan-m/ECC --skill token-budget-advisor

Or download just the file:

curl -L https://raw.githubusercontent.com/affaan-m/ECC/main/skills/token-budget-advisor/SKILL.md -o SKILL.md
Technical content (original SKILL.md)

Original text by the author, straight from GitHub.

Token Budget Advisor (TBA)

Intercept the response flow to offer the user a choice about response depth before Claude 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:

Complexity Multiplier range Example prompts
Simple 3× – 8× "What is X?", yes/no, single fact
Medium 8× – 20× "How does X work?"
Medium-High 10× – 25× Code request with context
Complex 15× – 40× Multi-part analysis, comparisons, architecture
Creative 10× – 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

Level Target length Include Omit
25% Essential 2-4 sentences max Direct answer, key conclusion Context, examples, nuance, alternatives
50% Moderate 1-3 paragraphs Answer + necessary context + 1 example Deep analysis, edge cases, references
75% Detailed Structured response Multiple examples, pros/cons, alternatives Extreme edge cases, exhaustive references
100% Exhaustive No restriction Everything — full analysis, all code, all perspectives Nothing

Shortcuts — skip the question

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

What they say Level
"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."

…

FAQ

What are 'tokens'?

Think of 'tokens' as pieces of words. The AI uses 'tokens' to measure the size of the text it reads and writes.

Why control response length?

Shorter answers are faster and use fewer resources. Longer answers can provide more detail and examples.

Is this token count exact?

No, it's an estimate. The AI uses an approximate method, but it's usually quite accurate (around 85-90% precision).

Details

Original description by the author

Offer a choice of response depth (25%/50%/75%/100%) with token estimates before answering, then answer at that level. Use when the user asks to control response length or token budget, such as 'token budget', 'short version', 'brief answer', 'respuesta corta vs larga', 'cuántos tokens', 'ahorrar tokens', 'responde al 50%', 'dame la versión corta', 'quiero controlar cuánto usas'. Skip if depth is a

repo: affaan-m/ECC

Popularity

GitHub stars
280k
Ranking position
#300
License
MIT
Updated
10/15/2026

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