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SKILL verified MIT Self-run

Performance At Scale

skill-fergius-engineering-instincts-performance-at-scale · by Fergius-Engineering

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.

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Install

$ agentstack add skill-fergius-engineering-instincts-performance-at-scale

✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets No
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

View the full security report →

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Reliability & compatibility

Security review passed
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Declared compatibility

Claude CodeClaude Desktop

Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.

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About

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. |

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

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Versions

  • v0.1.0 Imported from the upstream source.