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BytesAgain β€Ί Skills β€Ί Token Cost Optimization
πŸ¦€ ClawHub

Token Cost Optimization

by @openlark

Token savings and API cost optimization. Provides token calculator, three-tier optimization strategies (prompt compression / cache reuse / model downgrade),...

Versionv1.0.0
Downloads418
#crypto-defi#legal#gaming
TERMINAL
clawhub install token-cost-optimization

πŸ“– About This Skill

name: token-cost-optimization description: Token savings and API cost optimization. Provides token calculator, three-tier optimization strategies (prompt compression / cache reuse / model downgrade), specific configuration guides, and quantified effect analysis.

Token Cost Optimization

Use Cases

User mentions token savings, API cost optimization, prompt compression, cache strategy, model downgrade, cost analysis.

Quick Start

Token Calculator

Run the calculation script, input conversation scale, and quickly estimate current token consumption and optimization potential:

python scripts/token_calculator.py

The script will prompt for:

  • Number of conversation history items / average length
  • Model and pricing used
  • Current optimization status

    • Output: Current cost, optimized cost, savings percentage.

    Three-Tier Optimization Strategy

    Ranked by effect / implementation cost:

    | Tier | Strategy | Effect | Implementation Cost | |------|----------|--------|---------------------| | L1 | Prompt compression & output truncation | 10-30% | Low | | L2 | Conversation summary caching | 30-50% | Medium | | L3 | Model downgrade + task routing | 50-70% | High |

    Priority Recommendation: Implement in order L1 β†’ L2 β†’ L3, verifying results at each stage before proceeding.

    Detailed strategies, configuration guides, and pitfalls β†’ See references/tier-strategies.md

    Phased Implementation Guide

    Phase 1: L1 Compression (Immediate Effect)

  • Clean up redundant descriptions in system prompt
  • Set max_tokens limits for long responses
  • Remove outdated/unused messages from conversation history

    • Phase 2: L2 Caching (1-3 Days)

  • Establish FAQ shortcuts for high-frequency repeat questions
  • Add summary compression at the beginning of conversations (execute every N rounds)

    • Phase 3: L3 Routing (1-2 Weeks)

  • Route simple tasks to cheaper models (e.g., 4o-mini / Haiku)
  • Retain strong models for complex tasks
  • Configure model routing rules

    • Quantifiable Comparison Example

    See the "Quantified Comparison" section in references/tier-strategies.md for details.

    ⚑ When to Use

    User mentions token savings, API cost optimization, prompt compression, cache strategy, model downgrade, cost analysis.

    πŸ’‘ Examples

    Token Calculator

    Run the calculation script, input conversation scale, and quickly estimate current token consumption and optimization potential:

    python scripts/token_calculator.py

    The script will prompt for:

  • Number of conversation history items / average length
  • Model and pricing used
  • Current optimization status

    • Output: Current cost, optimized cost, savings percentage.

    Three-Tier Optimization Strategy

    Ranked by effect / implementation cost:

    | Tier | Strategy | Effect | Implementation Cost | |------|----------|--------|---------------------| | L1 | Prompt compression & output truncation | 10-30% | Low | | L2 | Conversation summary caching | 30-50% | Medium | | L3 | Model downgrade + task routing | 50-70% | High |

    Priority Recommendation: Implement in order L1 β†’ L2 β†’ L3, verifying results at each stage before proceeding.

    Detailed strategies, configuration guides, and pitfalls β†’ See references/tier-strategies.md