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token-budget-advisor

该 Skill 在回答前先用启发式方法估算输入 token 与提示复杂度,向用户展示四档响应深度(25%/50%/75%/100%)及对应 token 估算,用户回复数字或快捷语后按其长度与内容范围作答,并在会话内静默保持该档位;估算不使用真实分词器,自述准确度约85-90%。

affaan-m
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最近更新 2026-10-18 16:20:24
分类:效率依赖:纯提示词应用场景:摘要总结 / 工作流 / 模板

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add affaan-m/ecc/token-budget-advisor
Metadata
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:

  • 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. Original project also ships a Python estimator script, but this repository keeps the skill self-contained and heuristic-only.