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Get Started Free →athola/context-optimization
Optimizes context window via MECW principles and memory tiering.
Passed 19 of 21 test cases with the skill, and 7 of 21 without it.
and 15 more in the full report
14 fixed6 passed either way1 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
This benchmark measured an earlier version of the skill.
One of 13 test cases the skill fixed. Same model, same prompt, with and without the skill.
A developer working on a feature branch notices token utilization has reached 40% of the maximum window size. What classification status and operational response should be recorded?
| Checked | Without skill | With skill |
|---|---|---|
| Classifies status as MODERATE. | Missed | Passed |
| Advises monitoring usage and applying optimization principles. | Passed | Passed |
The instructions your model reads when the skill loads.
def select_optimal_modules(context_situation, task_complexity):
if context_situation == "CRITICAL":
return ["mecw-assessment", "subagent-coordination"]
elif task_complexity == "high":
return ["mecw-principles", "subagent-coordination"]
else:
return ["mecw-assessment"]
| Utilization | Status | Action |
|---|---|---|
| < 30% | LOW | Continue normally |
| 30-50% | MODERATE | Monitor, apply principles |
| > 50% | CRITICAL | Immediate optimization required |
test cases passed, vs 7 / 21 without
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