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
Ready-to-paste message
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-advisorOr download just the file:
curl -L https://raw.githubusercontent.com/affaan-m/ECC/main/skills/token-budget-advisor/SKILL.md -o SKILL.mdTechnical 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
Comments
- …
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