SKILL: Token Cost Intelligence — Free Primer
Source: Production agent stack running at $0.91/day (down from $8–10/session) Domain: Token cost optimization, OpenClaw deployments Type: Free primer
THE CORE TRUTH
*The models are not expensive. Your habits are.*
Most OpenClaw operators are spending 8–10x more than they need to. This primer gives you the diagnostic framework to find out where you're leaking.
THE "STUPID BUTTON" — 6 DIAGNOSTIC QUESTIONS
Run these before every session:
- Are you feeding raw PDFs/images when you only need text?
Screenshots are the worst offender. Copy-paste or convert to Markdown. A 4,500-word PDF = 100,000+ tokens raw. The same content in Markdown = 4,000–6,000 tokens. ~20x reduction.
- When did you last start a fresh conversation?
Every new turn re-sends the *entire* conversation history. 30-turn threads don't just feel inefficient — they are. 10–15 turn cap, then summarize and start fresh.
- Are you using the most expensive model for everything?
Opus for formatting and proofreading is a Ferrari to the grocery store. Haiku handles light tasks at 1/30th the cost.
- Do you know what's loading in context before you type?
Each loaded plugin = silent token tax per session. Documented case: 50,000 tokens consumed before the first keystroke. Audit your connectors. Disable what you don't use.
- Are you caching stable context? (API builders)
Cache hits on Opus: $0.50/M vs $5.00/M standard = 90% discount. System prompts, tool definitions, persona instructions → all cacheable. If you're not caching, you're paying full price for the same tokens every call.
- How are you handling web search?
Native model web search is token-heavy. MCP-routed alternatives return structured results at a fraction of the cost. Know what you're paying per search.
COST COMPARISON (CONCRETE)
| Session Type | Input Tokens | Output Tokens | Cost (Opus pricing) |
|---|---|---|---|
| Sloppy (raw PDFs, 30-turn sprawl, Opus-everything) | 800K–1M | 150K–200K | $8–$10 |
| Clean (markdown, 10-turn cap, tiered models) | 100K–150K | 50K–80K | ~$1 |
| Reduction | ~8x | ~3x | 8–10x |
Scaled to a team of 10 for one month:
- Sloppy habits: ~$2,000/month
- Clean habits: ~$250/month
- Same output volume.
5 AGENT COMMANDMENTS
For anyone running OpenClaw agents at any scale:
- Index your references. Agents get relevant chunks, not raw document dumps. Dumping full documents per agent call is architectural waste.
- Pre-process context before it hits the window. Chunk, summarize, and clean *before* ingestion. If the model's first tokens are spent parsing your bad preprocessing, you failed.
- Cache your stable context. System prompts, tool definitions, persona instructions, reference material → all cacheable. Thousands of agent calls per day without caching is pouring money out.
- Scope each agent to minimum viable context. Planning agent doesn't need the full codebase. Editing agent doesn't need the project roadmap. Passing everything to every agent is measurable waste — and models perform *worse* drowning in irrelevant context.
- Measure what you burn. Instrument all agent calls: input tokens, output tokens, model mix, cost ratio. You cannot optimize what you don't measure.
Full framework with anti-patterns by tier, tiered model routing, and confirmed production delta available in Token Cost Intelligence on Claw Mart.