performance-at-scale
Skill2 starsChanged 4 months agoMITView on GitHub
Use when writing code that runs per item, per frame, or per event, or building a cache or lookup over a collection that can grow large.
1 other project uses a version of this.
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Most recently taken up by
- Bbeierle12/Skill-MCP-Claudetheir own version, first seen Nov 1, 2026
Other versions1 project uses a version of this.
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Wrote their own version · 1
- Bbeierle12/Skill-MCP-Claude★ 9same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository19 other files it gives its agents.
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- entry-point-auditSkill
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- fix-in-the-shared-layerSkill
- fix-the-root-causeSkill
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- logging-for-remote-diagnosisSkill
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- plan-with-teethSkill
- project-onto-all-systemsSkill
- question-the-premiseSkill
- tests-with-teethSkill
- user-action-edge-casesSkill
- using-instinctsSkill
- ux-designer-mindsetSkill
- verify-against-codeSkill
The file
We split this skill into the rules it gives an agent
2 rules. About 0 are common, 2 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈2
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 16
Before writing data-path code, ask "does this hold at the largest realistic input?"
- Distinctive: line 20
If the answer is "no, it won't scale", redesign before you write it, not after.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
The rule
Hot-path code meets your data at production scale, not at the handful of rows in your test fixture. A linear scan that's instant on ten items freezes the UI on a hundred thousand. The cost is invisible in the test and brutal in the field. Design the hot path for the largest realistic input before you write it, not after a user reports a freeze.
Fires when
Writing code that runs per item, per frame, or per event. Building a cache or a lookup. Iterating a collection that could grow large. Rebuilding a whole list when one entry changed.
How to apply
Before writing data-path code, ask "does this hold at the largest realistic input?"
Use O(1) lookups with an early exit — a map keyed by the thing you're asking about, so 99% of queries return immediately. Prefer incremental point updates (remove one, add one) over rebuilding the whole structure. Keep allocations and copies out of tight loops. Verbose logging in a hot loop is fine, but only after the early exit, never before it.
If the answer is "no, it won't scale", redesign before you write it, not after.
Worked example
A handler runs once per tile and scans a flat list of issues linearly to find the ones that match. With a dozen issues in the test, it's instant. In a real project the list holds four thousand issues, and every tile now costs a one-to-two second freeze. Keyed into a map by tile, each query early-exits in O(1) and the freeze is gone. The scan looked fine because the test never had enough data to make it hurt.
Red flags
| Thought | Reality |
| "It's fast enough" | Fast on the fixture, frozen at scale. |
| "I'll rebuild the whole list, it's simpler" | Simpler to write, O(N) to run every time. |
| "Just loop and find it" | A linear scan on a hot path is a freeze waiting for data. |
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