Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Monitor token usage and estimated costs across all OpenClaw agents in real-time, with alerts and recommendations to stay within budgets and optimize spending.
Monitor token usage and estimated costs across all OpenClaw agents in real-time, with alerts and recommendations to stay within budgets and optimize spending.
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Track token usage, costs, and efficiency across all your OpenClaw agents in real-time. Get alerts before you blow your budget.
Running multiple agents is powerful β but expensive if you're not watching: Which agent is burning the most tokens? Are heartbeats wasting money on expensive models? Is caching actually saving you anything? When will you hit your weekly rate limit?
When triggered (via cron or manually), the agent: Checks session_status for each agent Calculates per-agent and total costs Compares against budget thresholds Sends alerts if limits are approaching Suggests optimization moves
Ask your monitoring agent (or any agent with this skill): "Give me a cost report for all agents" "Which agent used the most tokens today?" "Am I going to hit my rate limit this week?"
{ "name": "Daily Cost Report", "schedule": { "kind": "cron", "expr": "0 20 * * *", "tz": "Europe/Berlin" }, "payload": { "kind": "agentTurn", "message": "Run a cost report across all agents. Check session_status for each. Report: total tokens, cost per agent, top spender, budget warnings. Send summary to user." }, "sessionTarget": "isolated", "delivery": { "mode": "announce" } }
Use these rates for estimation (as of 2026):
ModelInput/1MOutput/1MCache Read/1MCache Write/1MOpus 4.6$5.00$25.00$0.50$6.25Sonnet 4.5$3.00$15.00$0.30$3.75Haiku 4.5$1.00$5.00$0.08$1.25
ModelCostUse ForOllama (local)$0Heartbeats, simple tasksGemini OAuth$0*Fallback (rate limited) *Free tier with rate limits
Heartbeats on Ollama { "heartbeat": { "model": "ollama/llama3.2:3b" } } Saves: 100% of heartbeat costs (can be $5-10/week with Opus) Haiku Cache Retention Off { "anthropic/claude-haiku-4-5": { "params": { "cacheRetention": "none" } } } Saves: Cache write costs on cheap model (not worth caching) Context Pruning { "contextPruning": { "mode": "cache-ttl", "ttl": "5m" } } Saves: Stale context re-reads on every turn Opus/Sonnet Cache Retention Long { "anthropic/claude-opus-4-6": { "params": { "cacheRetention": "long" } } } Saves: Re-sending system prompt every turn (biggest single saving)
Task TypeUse ThisNot ThisSavingCoordination, complex reasoningOpusβJustifiedFinance, data analysisSonnetOpus-40%Sales drafts, marketing copyHaikuSonnet-67%Heartbeats, health checksOllamaAny paid-100%Tweet draftsHaiku or GrokOpus-80%
Daily reset: Sessions auto-clear at a set hour (reduces token accumulation) { "session": { "reset": { "mode": "daily", "atHour": 4, "idleMinutes": 45 } } } Memory flush: Save important context before compaction { "compaction": { "memoryFlush": { "enabled": true } } }
Q: Does this skill make API calls? A: No. It uses OpenClaw's built-in session_status tool. No external APIs, no additional costs. Q: How accurate are cost estimates? A: Based on published model pricing. Actual costs may vary with caching hits. Estimates are conservative (slightly high). Q: Can I track costs per conversation? A: Not directly. Costs are tracked per session. Use sessions_list to see per-session token counts. Q: Works with non-Anthropic models? A: Yes. Token counts work for all providers. Cost estimation requires known pricing (add custom rates in the cost reference section).
Generalized all agent names in examples No specific setup references
Initial release
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Largest current source with strong distribution and engagement signals.