everything-claude-code/skills/token-budget-advisor/SKILL.md

---
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", "response token budget", "token count",
  "token usage", "response length", "response depth", "brief answer",
  "short answer", "detailed answer", "full answer",
  "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%",
  "give me the full answer", 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.

## How It Works

### Step 1 — Estimate input tokens

Use the repository's canonical estimation guidance from `skills/context-budget`.

- Prose-first prompts: `input_tokens ≈ word_count × 1.3`
- Code-heavy or mixed prompts: `input_tokens ≈ char_count / 4`

For mixed content, prefer the code-heavy estimate as the conservative default.

### 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 |
|----------------------------------------------------|-------|
| "1" / "25%" / "short answer" / "brief" / "tldr" / "one-liner" | 25% |
| "2" / "50%" / "moderate detail" / "balanced answer" | 50% |
| "3" / "75%" / "detailed answer" / "thorough explanation" | 75% |
| "4" / "100%" / "exhaustive" / "everything" / "full answer" | 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 brief answer first."
- "How many tokens will your response use?"
- "Respond at 50% depth."
- "I want the full answer."
- "Dame la version corta."

### Does Not Trigger

- "Explain OAuth token refresh flow." (`token` here is domain language, not a budget request)
- "Why is this JWT token invalid?" (security/domain usage, not response sizing)
- "What is 2 + 2?" (trivially short answer)

## Source

Standalone skill from [TBA — Token Budget Advisor for Claude Code](https://github.com/Xabilimon1/Token-Budget-Advisor-Claude-Code-).
The upstream project includes an optional estimator script, but this ECC skill intentionally stays zero-dependency and heuristic-only.