Strategies for managing LLM context windows including summarization, trimming, routing, and avoiding context rot
NewMaintained
Automatic
◆AI Engineer pack
Last verified 8/29/2026
What it changes
Passed 19 of 22 test cases with the skill, and 19 of 22 without it.
Without skill86%
With skill86%
Checks it passed that the model alone missed
Avoids sending the uncurated full history of 100 turns
Selects semantic vector retrieval of top-k relevant turns to save context tokens
1 fixed18 passed either way2 still failing1 broke
Pass-rate lift
0 pp
Tokens
+18%
Agent turns
0%
Response time
+4%
Model gemini-3.6-flashMeasured 8/29/2026v119 of 22 cases passed with skill
Tokens, turns and time describe resource use, not a dollar price.
See it in action
A test case the skill fixed. Same model, same prompt, with and without the skill.
Prompt
A user asks an enterprise search assistant about a project detail discussed 20 turns ago. The system has 100 historical message turns. Compare fetching relevant historical turns via vector similarity search against sending all 100 turns in context.
What was checked, without and with the skill
Checked
Without skill
With skill
Selects semantic vector retrieval of top-k relevant turns to save context tokens
Missed
Passed
Avoids sending the uncurated full history of 100 turns
Missed
Passed
case-07 · gemini-3.6-flash · 8/29/2026 · answers are capped and redacted
What's inside
The instructions your model reads when the skill loads.
SKILL.md32 linessingle file
Context Window Management
You're a context engineering specialist who has optimized LLM applications handling
millions of conversations. You've seen systems hit token limits, suffer context rot,
and lose critical information mid-dialogue.
You understand that context is a finite resource with diminishing returns. More tokens
doesn't mean better results—the art is in curating the right information. You know
the serial position effect, the lost-in-the-middle problem, and when to summarize
versus when to retrieve.