Reduce Astra token use by auditing the workflow
Keep the model and reasoning setting you value, then measure where instructions, context, and repeated work consume tokens.
A creator says they iteratively audited their setup and now use GPT-6 Astra at High reasoning effort with better token efficiency. That is a personal result, not a promise that one prompt will reduce usage for everyone. The transferable idea is to ask the agent to measure a baseline, inspect the system around it, make scoped changes, and compare quality as well as token use.
OpenAI’s guidance for Astra specifically recommends revisiting skills, AGENTS.md, and task prompts that force unnecessary reading or repeated checks. The official reasoning guide says High is suited to hard reasoning and complex agentic work, while token use depends on the task and settings. Keep the chosen model and effort fixed during the first comparison so the effect of workflow changes is visible.
Measure before changing anything
Pick several representative tasks and record the available input, output, and reasoning-token usage, time to completion, and whether the result passed review. A shorter response is not an improvement if it causes more retries or misses requirements.
Audit the instructions and context
Look for duplicated rules, skills loaded for unrelated tasks, mandatory full-repository reads, oversized tool output, repeated searches, and tests that do not address a concrete risk. Keep durable constraints, but make references conditional on the task. Compare one scoped change at a time.
Repeat with a quality gate
Run the same task set after the change. Compare total tokens and completion time alongside correctness and human corrections. If a change saves tokens but weakens outcomes, restore it. Document the final configuration and the cases where High effort is still worth its cost.
Copy a scoped audit request
This version preserves the current model and reasoning effort, asks for before-and-after evidence, and limits changes to the agent setup you approve.
My GPT-6 Astra sessions use more tokens than I expect. Audit this Codex setup for avoidable context and repeated work while preserving the current model and High reasoning effort. Do not reduce the quality bar. First, identify a small set of representative tasks and collect whatever token, latency, and outcome data is actually available. Do not invent missing metrics. Inspect the relevant project instructions, skills, tool output patterns, and task prompts for duplicated rules, unconditional reading, and unnecessary repeated checks. Propose the smallest changes with expected benefits and risks, then make only local, reversible changes within the scope I have already authorized. Preserve unrelated settings and integrations. Repeat the same tasks or a comparable evaluation after each meaningful change. Report before-and-after token use, time, correctness, and human correction needed. Revert any change that saves tokens by lowering quality. If the measurement reveals another clear improvement, apply and measure it the same way. Finish with the exact changes, evidence, and remaining uncertainty.