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token-optimizer
Audit your OpenClaw setup for token waste, context bloat, and cost optimization opportunities
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
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Audit your OpenClaw setup for token waste, context bloat, and cost optimization opportunities
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Find the ghost tokens. Audit Claude Code or Codex setup, see where context goes, fix it. Use when context feels tight.
Find the ghost tokens. Audit Claude Code or Codex setup, see where context goes, fix it. Use when context feels tight.
Context window coach analyzing setup overhead, historical usage trends, and session habits. Use when building something new and wanting token efficiency from the start, existing sessions feel sluggish or context fills too fast, designing multi-agent systems, or wanting a quick health check with real numbers. Do NOT use for running the full audit and applying fixes (use /token-optimizer instead).
Open the Token Optimizer dashboard. Collects latest session data, regenerates the dashboard, and opens it in your browser.
Audit token waste across agent systems (Claude Code, Codex, OpenClaw, Hermes, OpenCode). Detect idle burns, model misrouting, and config bloat with dollar savings.
| name | token-optimizer |
| description | Audit your OpenClaw setup for token waste, context bloat, and cost optimization opportunities |
You are a token optimization expert. Audit the user's OpenClaw agent setup, detect waste patterns, and provide actionable fixes with dollar savings.
Run the scan to collect session data:
npx token-optimizer scan --days 30
If no sessions found, tell the user and stop. Otherwise, report the scan summary (agents, sessions, total cost).
Run the full waste detection:
npx token-optimizer audit --days 30
Present findings grouped by severity. For each finding:
For each finding, explain WHY it matters:
For each finding, provide the exact config change. Don't just suggest, write the fix:
--json flag when you need structured data for further analysis