---
name: token-budget-advisor
description: >-
  Intercepts the response flow to offer the user an informed choice about
  how much depth/tokens to consume — BEFORE responding. Use this skill
  when the user wants to control token consumption, adjust response depth,
  choose between short/long answers, or optimize their prompt.
  TRIGGER when: "token budget", "token count", "token usage", "token limit",
  "respuesta corta vs larga", "cuántos tokens", "ahorrar tokens",
  "responde al 50%", "dame la versión corta", "quiero controlar cuánto usas",
  "75%", "100%", "at 25%", "at 50%", "at 75%", "at 100%", "exhaustive", or any variant where the user wants
  to control length, depth, or token usage — even without mentioning tokens.
  DO NOT TRIGGER when: user has already specified a level in the current
  session (maintain it) or the request is clearly a one-word answer.
origin: community
---

# 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.

## Workflow

### Step 1 — Estimate input tokens

Use the calibration tables below to estimate the prompt's token count mentally.

**Chars-per-token by content type:**

| Content type      | Chars / Token |
|-------------------|---------------|
| English natural   | ~4.0          |
| Spanish natural   | ~3.5          |
| Code              | ~3.0          |
| JSON              | ~2.8          |
| Markdown          | ~3.3          |

Formula: `input_tokens ≈ char_count / chars_per_token`

For mixed content, use the dominant type's ratio.

### 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%", "50%", "75%", "100%")

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 |
|----------------------------------------------------|-------|
| "25%" / "short" / "brief" / "tldr" / "one-liner"  | 25%   |
| "50%" / "moderate" / "normal"                      | 50%   |
| "75%" / "detailed" / "thorough" / "complete"       | 75%   |
| "100%" / "exhaustive" / "everything" / "full"      | 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.

## Source

Standalone skill from [TBA — Token Budget Advisor for Claude Code](https://github.com/Xabilimon1/Token-Budget-Advisor-Claude-Code-).
Full version includes a Python estimator script for exact counts: `npx token-budget-advisor`.