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himanshu231204
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token-budget-advisor

Select a 4-tier response depth to control AI token usage.

2|Updated May 11, 2026
Search Tags:#token budget#token saving#token estimation#response depth#prompt complexity#depth control#ai response optimization
One-click install
npx skills add https://github.com/himanshu231204/AI_Research_agent --skill token-budget-advisor-himanshu231204
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: token-budget-advisor
Source: https://github.com/himanshu231204/AI_Research_agent/tree/main/.opencode/skills/token-budget-advisor
Command: npx skills add https://github.com/himanshu231204/AI_Research_agent --skill token-budget-advisor-himanshu231204

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill eliminates the frustration of receiving AI responses that are either too long and token-heavy or too short and lacking necessary context, by letting you explicitly choose your desired level of detail before the AI generates a full answer.

Core Features & Use Cases

  • Heuristic Token Estimation: Accurately estimates input token count and potential response size based on prompt complexity without needing a real tokenizer.
  • 4-Tier Depth Selection: Offers standardized depth levels (25% Essential to 100% Exhaustive) with clear token estimates for each, so you know exactly what you will get.
  • Session State Persistence: Remembers your chosen depth level for the entire conversation, so you don't have to repeat your preference for every follow-up question.

Real-world use case: If you are researching a new AI framework and first want a quick high-level overview before diving into implementation details, use this Skill to request a 25% depth summary first, then switch to 100% depth when you are ready to explore every detail.

Quick Start

Ask the AI to provide a 50% depth explanation of how LangGraph orchestrates multi-agent workflows for research tasks.

Frequently Asked Questions about token-budget-advisor

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I control AI response depth and token spend before generating an answer?▼

To control AI response depth, you specify a desired detail level upfront using a 4-tier depth selection system. This ensures responses match your token budget and complexity requirements without generating unnecessary content.

What is the best way to estimate token usage for complex prompts without a real tokenizer?▼

Estimating token usage for complex prompts requires heuristic input token estimation, which calculates potential response size based on prompt complexity. This method accurately predicts token spend without needing a real tokenizer.

Can I set a consistent response depth level for an entire conversation?▼

Yes, you can set a consistent response depth for an entire conversation using session state persistence. This feature remembers your chosen depth level, so you do not need to repeat your preference for follow-up questions.

Does prompt complexity classification affect token budgeting and response length?▼

Prompt complexity classification directly affects token budgeting by analyzing your input to estimate the necessary response length. This classification helps deliver responses that match your specified detail requirements while minimizing token expenditure.

How do I switch between a quick summary and an exhaustive technical analysis in one chat?▼

You switch between a quick summary and an exhaustive technical analysis by selecting different 4-tier depth levels, ranging from 25% Essential to 100% Exhaustive. This provides clear token estimates for each response size.

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