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Token Optimizer Pro

该 Skill 接收使用日志、账单摘要、运行日志或对话记录,输出按任务/模型/阶段/工具的 Token 与成本拆分、浪费模式、上下文压缩与缓存建议,以及优化前后估算。内置脱敏和默认不外传日志的安全边界。

harrylabsj
harrylabsj
热度 83.24
最近更新 2026-06-17 10:36:35
分类:数据依赖:纯提示词应用场景:数据处理 / 报告撰写 / 工作流 / 推荐

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-optimizer-pro
Metadata
name
token-optimizer-pro
description
Agent token usage optimizer. Input usage logs, transcript excerpts, model bills, or runtime traces; output token/cost breakdown, waste patterns, context compaction opportunities, caching suggestions, and before/after optimization actions. Privacy boundary: redact secrets and do not upload sensitive logs unless the user explicitly chooses an external tool.

Token Optimizer Pro

Use this skill when the user wants to understand where token usage and model cost are going, then reduce waste without hurting task quality.

Inputs

  • Token usage exports
  • Model billing summaries
  • Agent runtime logs
  • Conversation transcripts or excerpts
  • Prompt templates and tool traces

Outputs

  • Cost and token breakdown by task, model, phase, or tool
  • High-waste patterns: repeated context, oversized prompts, unused files, verbose tool output, weak summarization
  • Optimization checklist: compaction, caching, retrieval boundaries, shorter outputs, batching, cheaper model routing
  • Before/after estimate with caveats

Safety

  • Redact API keys, credentials, private customer data, and personal identifiers.
  • Do not upload logs or transcripts to an external service unless the user explicitly chooses that route.
  • Treat cost estimates as approximate unless the user provides official billing exports.

Example Prompts

  1. Analyze this agent run log and tell me where token usage was wasted.
  2. Given these model bills, identify the top 5 cost drivers and how to reduce them.
  3. Review this prompt template and make it cheaper without losing task quality.
  4. Compare before/after token usage for these two runs.
  5. Design a token budget and compaction strategy for a long-running coding agent.