Data & analysis

Token Optimizer Pro

Find where agent tokens and model spend go, then plan reductions without sacrificing task quality.

What it does

Analyze usage exports, billing summaries, runtime logs, transcripts, prompts, and tool traces to break down tokens and cost by task, model, phase, or tool. Identify waste such as repeated context, oversized prompts, unused files, verbose tool output, and weak summaries, then produce an optimization checklist and caveated before/after estimate.

When to use it

  • Auditing token waste in agent run logs
  • Finding top cost drivers in model bills
  • Reducing prompt cost while preserving quality
  • Planning token budgets for long-running agents

The skill document

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.

Questions people ask

What inputs can it analyze?
It accepts token usage exports, model billing summaries, agent runtime logs, conversation transcripts or excerpts, prompt templates, and tool traces.
What optimization actions does it recommend?
The checklist may cover context compaction, caching, retrieval boundaries, shorter outputs, batching, and routing work to cheaper models.
How does it handle sensitive data and cost estimates?
API keys, credentials, private customer data, and personal identifiers should be redacted. Logs are not uploaded externally unless the user explicitly chooses that route, and estimates remain approximate without official billing exports.

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