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eng-performance

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Mis à jour6 février 2026 à 04:08

Guard latency, memory, battery, bandwidth, and gas/compute budgets by measuring before and after every change.

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tjboudreaux

tjboudreaux/cc-plugin-engineering-excellence

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Métiers associésSOC

Basé sur la classification professionnelle SOC

Développeurs de logicielsProfessions informatiques et mathématiques·SOC 15-1252

SKILL.md

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name	eng-performance
description	Guard latency, memory, battery, bandwidth, and gas/compute budgets by measuring before and after every change.

Performance and Efficiency Awareness

Intent

Treat budgets (frame time, network round-trips, server CPU, on-chain gas) as first-class requirements.
Prevent regressions by profiling early, not after user complaints.

Inputs

Existing SLAs/budgets per platform (FPS, TTI, API latency, contract gas caps).
Representative workloads, datasets, replays, or load scripts.
Tooling: profilers, flamegraphs, shader analyzers, gas estimators, Lighthouse, etc.

Workflow

Establish baseline
- Capture metrics for the current implementation under realistic load/device tiers.
Design with budgets
- Choose data structures, rendering strategies, and contract patterns aligned with constraints.
- Consider caching, pagination, batching, and compression aggressively.
Measure iteratively
- After each significant change, run targeted benchmarks or simulations.
- Track results in the PR/issue; highlight deltas and why they are acceptable.
Optimize where it matters
- Focus on user-facing bottlenecks; avoid premature micro-optimizations.
- For web3, minimize storage writes and external calls; for mobile, monitor battery/thermal impact.

Verification

Benchmarks or profiling output attached, showing no regression (or justified improvements).
Budgets documented; alarms/alerts configured if applicable.
For regressions that cannot be fixed immediately, open a follow-up issue with owner, impact, and mitigation.

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name	eng-performance
description	Guard latency, memory, battery, bandwidth, and gas/compute budgets by measuring before and after every change.

Performance and Efficiency Awareness

Intent

Treat budgets (frame time, network round-trips, server CPU, on-chain gas) as first-class requirements.
Prevent regressions by profiling early, not after user complaints.

Inputs

Existing SLAs/budgets per platform (FPS, TTI, API latency, contract gas caps).
Representative workloads, datasets, replays, or load scripts.
Tooling: profilers, flamegraphs, shader analyzers, gas estimators, Lighthouse, etc.

Workflow

Establish baseline
- Capture metrics for the current implementation under realistic load/device tiers.
Design with budgets
- Choose data structures, rendering strategies, and contract patterns aligned with constraints.
- Consider caching, pagination, batching, and compression aggressively.
Measure iteratively
- After each significant change, run targeted benchmarks or simulations.
- Track results in the PR/issue; highlight deltas and why they are acceptable.
Optimize where it matters
- Focus on user-facing bottlenecks; avoid premature micro-optimizations.
- For web3, minimize storage writes and external calls; for mobile, monitor battery/thermal impact.

Verification

Benchmarks or profiling output attached, showing no regression (or justified improvements).
Budgets documented; alarms/alerts configured if applicable.
For regressions that cannot be fixed immediately, open a follow-up issue with owner, impact, and mitigation.