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agentic/SKILL.md

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name: trace analysis
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description: Explains the behavior of traces, we focus on analyzing why a trace works different under the same best parameters but different initial stats.
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---
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Background: we are working on a learned cache project, basic idea is:
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for a trace, we use first 20% traces for feature extraction, then we use this feature to predict the best parameters for the remaining 80% traces. However, in order to avoid ad-hoc parameter tunning, we do the
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labeling via search best parameter for the whole trace.
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Then it introduces a problem: for the same trace, we have three settings
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1. best parameters for the whole trace
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2. default parameters for 20% trace + best parameters for the remaining 80% trace
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3. default parameters for the whole trace
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We have a hypothesis is that case 1 < case 2 < case 3 in terms of miss ratio, but we find that sometimes case 2 would be worse than case 3, which is counterintuitive.
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Then we conducted a typical analysis over `/mnt/cfs/oracleReuse/tencentBlock/tencentBlock.ns4712.oracleGeneral.zst`.
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When explaining code, always include:
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1. **Start with an analogy**: Compare the code to something from everyday life
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2. **Draw a diagram**: Use ASCII art to show the flow, structure, or relationships
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3. **Walk through the code**: Explain step-by-step what happens
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4. **Highlight a gotcha**: What's a common mistake or misconception?
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Keep explanations conversational. For complex concepts, use multiple analogies.

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