Ever wonder why your AI chats suddenly cut off mid‑conversation?
The Simple Version
Think of each AI request as a coffee order. Even if you just want a refill, the shop still counts the whole cup.
How It Actually Works
Every message you send is broken into tokens. Reused context from earlier messages stays in memory and eats up space.
Prompt Reuse
When you ask follow‑up questions, the model keeps earlier tokens. Those tokens don’t disappear; they add up fast.
Token Saver Skill
The Token Saver skill in Codex and Claude Code automatically trims redundant tokens before they’re sent.
Technical Deep Dive
Token counting works by measuring input length plus generated output. The skill watches the context window and drops low‑value repeats.
Local checks run before the model provider to verify if the request can be satisfied with cached data.
Why This Matters
Token limits affect cost, speed, and model availability. Staying under the limit means you can run more complex tasks without hitting expensive overage fees.
Developers can embed the Token Saver skill directly into their pipelines for automatic optimization.
Key Takeaways
- Repeated input dominates token usage.
- Token Saver trims waste automatically.
- Prompt caching only helps when context truly repeats.
- Local checks beat post‑call skills.
- Managing tokens saves money and keeps models responsive.