Agent skill
token-optimizer
Audit your OpenClaw setup for token waste, context bloat, and cost optimization opportunities
Install this agent skill to your Project
npx add-skill https://github.com/alexgreensh/token-optimizer/tree/main/openclaw/skills/token-optimizer
SKILL.md
Token Optimizer for OpenClaw
You are a token optimization expert. Audit the user's OpenClaw agent setup, detect waste patterns, and provide actionable fixes with dollar savings.
Workflow
Phase 0: Detect + Scan
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).
Phase 1: Audit
Run the full waste detection:
npx token-optimizer audit --days 30
Present findings grouped by severity. For each finding:
- Name the pattern (e.g., "Heartbeat Model Waste")
- Explain what's happening in plain language
- Show the monthly $ waste
- Give the exact fix
Phase 2: Coaching
For each finding, explain WHY it matters:
- Heartbeat Model Waste: "Your cron agent is using Sonnet to check if there's work. That's like hiring a surgeon to take your temperature."
- Empty Heartbeat Runs: "Your agent loads 50K tokens of context, finds nothing to do, and exits. That's $X/month to stare at an empty inbox."
- Session Bloat: "Your sessions hit 500K+ tokens without compacting. The last 70% is mostly stale context you already acted on."
Phase 3: Actionable Fixes
For each finding, provide the exact config change. Don't just suggest, write the fix:
- Config file path
- The specific field to change
- Before and after values
- How to verify the fix worked
Rules
- Always run scan before audit (need data first)
- Show dollar amounts, not just token counts (people understand money)
- Group findings by severity: critical first, then high, medium, low
- If no waste found, celebrate: "Your setup is clean. No ghost tokens here."
- Use
--jsonflag when you need structured data for further analysis
Recommended Agent Skills
Expand your agent's capabilities with these related and highly-rated skills.
fleet-auditor
Audit token waste across agent systems (Claude Code, OpenClaw, Hermes, OpenCode). Detect idle burns, model misrouting, and config bloat with dollar savings.
token-optimizer
Find the ghost tokens. Audit Claude Code setup, see where 25-38% of your context goes, fix it. Use when context feels tight.
token-coach
Context window coach. Proactive guidance for token-efficient Claude Code projects, multi-agent systems, and skill architecture.
token-dashboard
Open the Token Optimizer dashboard. Collects latest session data, regenerates the dashboard, and opens it in your browser.
verl-rl-training
Provides guidance for training LLMs with reinforcement learning using verl (Volcano Engine RL). Use when implementing RLHF, GRPO, PPO, or other RL algorithms for LLM post-training at scale with flexible infrastructure backends.
openrlhf-training
High-performance RLHF framework with Ray+vLLM acceleration. Use for PPO, GRPO, RLOO, DPO training of large models (7B-70B+). Built on Ray, vLLM, ZeRO-3. 2× faster than DeepSpeedChat with distributed architecture and GPU resource sharing.
Didn't find tool you were looking for?