Skill: thebrierfox/free-token-optimization-primer (version 1.0.0)

# 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: 1. **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. 2. **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. 3. **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. 4. **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. 5. **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. 6. **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: 1. **Index your references.** Agents get relevant chunks, not raw document dumps. Dumping full documents per agent call is architectural waste. 2. **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. 3. **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. 4. **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. 5. **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](https://www.shopclawmart.com/listings/token-cost-intelligence-openclaw-optimization-framework-a417717e).


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