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token-budget-advisor — Claude Code skill: what it does, how to install

This skill offers response-depth choices with token estimates before generation. Use it when you want to set answer length in advance.

Install into Claude Code · Skills CLI (Vercel) All methods and platforms ↓
npx skills add affaan-m/ECC --skill token-budget-advisor -a claude-code
Open on GitHub

What token-budget-advisor does

token-budget-advisor intercepts requests about response length, depth, or token budget. It offers 25%, 50%, 75%, and 100% levels, estimates the input and expected response range, then answers at the selected level. The estimates use word- or character-based heuristics rather than a real tokenizer.

The skill skips prompts when a depth level is already set for the session. It also ignores mentions of authentication or payment tokens. The chosen level carries over to later responses until the user changes it. Installation targets include Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, and OpenCode.

Install it when response detail needs to be controlled before generation. It adds little for one-line requests or a session with a fixed depth.

What it helps with

  • choose response depth before generation
  • estimate input and output token ranges
  • get a short answer without extra context
  • keep a selected depth for the session

Example prompts for the agent

  • Answer at 25% depth.
  • Estimate the token budget and offer depth options.
  • Give me a detailed answer at 75% depth.

How to install token-budget-advisor

1Skills CLI (Vercel)

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

2Manually: put the files in place

~/.claude/skills/token-budget-advisor/SKILL.md

Copy the whole skill folder; for one project use .claude/skills/token-budget-advisor/

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Similar

Questions about token-budget-advisor

How do I install token-budget-advisor in Claude Code?

Run npx skills add affaan-m/ECC --skill token-budget-advisor -a claude-code. The skill file is placed at ~/.claude/skills/token-budget-advisor/SKILL.md.

What does token-budget-advisor do?

It offers four response-depth levels, estimates the prompt and expected answer size, then responds at the selected level.

How does token-budget-advisor distinguish response budget from a payment token?

It responds to requests about answer length, depth, or token budget. Authentication and payment token mentions are skipped.

Source text

SKILL.md · MIT
---
name: token-budget-advisor
description: 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 already set this session or 'token' means an auth/payment token.
metadata:
  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 context-budget heuristics to estimate the prompt's token count mentally.

Use the same calibration guidance as [context-budget](../context-budget/SKILL.md):

- 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."
- "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](https://github.com/Xabilimon1/Token-Budget-Advisor-Claude-Code-).
Original project also ships a Python estimator script, but this repository keeps the skill self-contained and heuristic-only.

© affaan-m · MIT · affaan-m/ECC

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