Token Budget Advisor

纯提示词型 Skill：回答前先展示深度选项菜单（Essential 25%、Moderate 50%、Detailed 75%、Exhaustive 100%），并给出输入 token 与复杂度的启发式估算；用户选定后按该层级控制回答长度与详略，并在本次会话内保持该层级，除非用户更改。

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热度 45.19

最近更新 2026-05-11 17:40:23

分类：效率依赖：纯提示词应用场景：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 token-budget-advisor

Metadata

name: token-budget-advisor
description: Offer users informed choice about response depth before answering. Control token usage, response length, and answer depth. Trigger phrases: token budget, token count, token usage, response length, short version, brief answer, detailed answer, exhaustive, save tokens. Adapted from everything-claude-code by @affaan-m (MIT)
metadata: {"clawdbot":{"emoji":"💰","requires":{"bins":[],"env":[]},"os":["linux","darwin","win32"]}}

Token Budget Advisor

Intercept response flow to let users choose depth before answering.

When to Use

User mentions tokens, budget, depth, or response length
User says "short version", "tldr", "brief", "exhaustive", etc.
User wants to control detail level upfront

Do not trigger when: user already set depth this session, or answer is trivially one line.

Depth Options

Present before answering:

Input: ~[N] tokens | Type: [type] | Complexity: [level]

[1] Essential (25%)   -> ~[tokens]   Direct answer only
[2] Moderate (50%)    -> ~[tokens]   Answer + context + example
[3] Detailed (75%)    -> ~[tokens]   Multiple examples + alternatives
[4] Exhaustive (100%) -> ~[tokens]   Everything

Level Guide

Level	Length	Include	Omit
25%	2-4 sent	Direct answer, conclusion	Context, examples
50%	1-3 para	Answer + context + 1 example	Deep analysis, edge cases
75%	Structured	Examples, pros/cons, alternatives	Extreme cases
100%	No limit	Everything	Nothing

Shortcuts

If user signals depth, respond at that level immediately:

"1" / "25%" / "short" / "brief" / "tldr" → 25%
"2" / "50%" / "moderate" → 50%
"3" / "75%" / "detailed" → 75%
"4" / "100%" / "exhaustive" → 100%

Maintain user's chosen level for rest of session unless changed.

Estimation

Heuristic-based (no real tokenizer):

Input tokens = estimate from prompt size
Response window = input × complexity multiplier
Accuracy ~85-90%, variance ±15%