Install
$ agentstack add skill-fergius-engineering-instincts-performance-at-scale ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo 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.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →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.
- Author: Fergius-Engineering
- Source: Fergius-Engineering/instincts
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.