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Strategies for managing LLM context windows including summarization, trimming, routing, and avoiding context rot
Passed 19 of 20 test cases with the skill, and 10 of 20 without it.
and 9 more in the full report
10 fixed11 passed either way1 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 9 test cases the skill fixed. Same model, same prompt, with and without the skill.
In an importance-based message summarization algorithm, what exact numeric weights are added to a message's baseline importance score when `hasCriticalInfo` or `referenced` flags are true?
| Checked | Without skill | With skill |
|---|---|---|
| Adds 0.5 weight boost for critical info flag | Missed | Passed |
| Adds 0.3 weight boost for referenced flag | Missed | Passed |
The instructions your model reads when the skill loads.
Strategies for managing LLM context windows including summarization, trimming, routing, and avoiding context rot
Different strategies based on context size
When to use: Building any multi-turn conversation system
test cases passed, vs 10 / 20 without
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