---
id: daily-token-budget-advisor
name: "token-budget-advisor"
url: https://skills.yangsir.net/skill/daily-token-budget-advisor
author: affaan-m
domain: ai-llm-engineering
tags: ["prompt-engineering", "ai-agents", "workflow-automation", "cost-optimization", "llm-integration"]
install_count: 3900
rating: 4.40 (20 reviews)
github: https://github.com/affaan-m/everything-claude-code
---

# token-budget-advisor

> 在响应生成前拦截并询问用户期望的深度，控制 token 消耗和响应长度，优化成本和效率

**Stats**: 3,900 installs · 4.4/5 (20 reviews)

## Before / After 对比

### 响应控制

**Before**:

每次都生成完整长响应，用户只需简短答案时也消耗大量 token，月度 API 费用超预算 50%

**After**:

提前询问响应深度，按需生成简短或详细答案，token 消耗降低 40%，费用控制在预算内

| Metric | Before | After | Change |
|---|---|---|---|
| Token 消耗 | 100000个/月 | 60000个/月 | -40% |

## Readme

# token-budget-advisor

# 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](https://github.com/affaan-m/everything-claude-code/blob/HEAD/skills/token-budget-advisor/../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.
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*Source: https://skills.yangsir.net/skill/daily-token-budget-advisor*
*Markdown mirror: https://skills.yangsir.net/api/skill/daily-token-budget-advisor/markdown*