Optimize

Skill v0idOS/performance-deity/skills/optimize

A ruthlessly strict algorithmic optimization methodology for AI coding agents. Forces your agent to profile, benchmark, and mathematically prove performance gains before writing code.

Install

npx -y skills add v0idOS/performance-deity --skill optimize

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Benchmark, analyze, and optimize a function's execution time. Produces a before/after performance table with real numbers. Never presents a change without proving it is faster.

SKILL.md

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Execute all four phases in order. Do not skip any phase.

Phase 1 — Establish Baseline

Identify the exact code to optimize.
Run a micro-benchmark:
- Write a temporary micro-benchmark script in the user's workspace.
- The script MUST contain a warm-up phase (discard ≥10 iterations).
- The script MUST run ≥100 iterations and output the Average and P95 execution time.
- Run the script using the terminal.
- Delete the temporary script after recording the results.
- Note: If the script fails due to missing imports or dependencies, skip it and use the bash time command as a fallback.
Record P95 and Average. Do not proceed until the benchmark runs without error.
Report baseline numbers before writing any new code.

Phase 2 — Algorithmic Analysis

State the current Time Complexity (Big-O) explicitly.
State the current Space Complexity and identify the primary allocation sites.
Name the bottleneck precisely:
- "Nested loops causing O(n²) scaling"
- "Repeated string concatenation causing N heap allocations per call"
- "Full table scan caused by missing index on user_id"

Phase 3 — Refactoring

Rewrite using a more efficient algorithm or data structure (apply in priority order):
- Replace Array/List lookups with Hash Sets/Dictionaries: O(N) → O(1)
- Vectorization or batching instead of per-item iteration
- Caching/memoization of expensive pure computations
- Zero-allocation patterns and buffer reuse to reduce GC pressure
- Bitwise operations where mathematically equivalent
Run the benchmark on the new code.
If the new code is not measurably faster: discard it, select a different approach, repeat.

Phase 4 — Report

Present a Performance Report table:

Metric	Baseline	Optimized	Δ
Average	Xms	Yms	-Z%
P95	Xms	Yms	-Z%

Follow with a one-paragraph explanation grounded in CPU/memory theory.