performance-optimization

Name: performance-optimization
Author: DevelopersGlobal

64stars10forks

How to install performance-optimization?

developersglobal/ai-agent-skills/performance-optimization

$npx -y skills add DevelopersGlobal/ai-agent-skills --skill performance-optimization

Installs into the current project.

Summary

Measure first, optimize second. Data-driven performance improvements with before/after benchmarks and production validation.

Files · 1

View on GitHub

SKILL.md

1	## Overview
2
3	Premature optimization is the root of all evil. But ignoring performance until it's a crisis is equally harmful. This skill enforces data-driven optimization: profile first, optimize the bottleneck, measure the improvement.
4
5	## When to Use
6
7	- When performance issues are reported in production
8	- Before optimizing any code (to ensure you're optimizing the right thing)
9	- When reviewing changes that touch performance-sensitive paths
10
11	## Process
12
13	### Step 1: Measure the Baseline
14
15	1. Reproduce the performance issue reliably.
16	2. Measure current performance: latency p50/p95/p99, throughput, memory, CPU.
17	3. Profile to find the actual bottleneck — not where you think it is.
18	4. Write the performance test you'll use to validate improvement.
19
20	Verify: You have concrete baseline numbers, not gut feelings.
21
22	### Step 2: Identify the Real Bottleneck
23
24	5. Use profiling tools: flame graphs, CPU profiles, memory profiles.
25	6. Find the top 3 hotspots by actual execution time (not lines of code).
26	7. The bottleneck is rarely where you expect it to be. Trust the data.
27
28	Verify: Bottleneck identified by profiling data, not assumption.
29
30	### Step 3: Optimize Only the Bottleneck
31
32	8. Fix only the profiled bottleneck — nothing else.
33	9. Common optimizations by type:
34	- CPU: Algorithmic improvement (O(n²) → O(n log n)), caching, batching
35	- Memory: Streaming instead of buffering, object pooling, lazy loading
36	- I/O: Connection pooling, N+1 query elimination, caching, async/parallel calls
37	- AI: Prompt caching, batch inference, smaller models for simpler tasks
38
39	Verify: Change targets the profiled bottleneck, not speculative improvements.
40
41	### Step 4: Measure the Improvement
42
43	10. Run the same performance test from Step 1.
44	11. Compare before vs. after metrics.
45	12. If improvement < 20%: the optimization may not be worth the complexity.
46
47	Verify: Improvement measured with the same test harness as baseline.
48
49	## Common Rationalizations (and Rebuttals)
50
51	\| Excuse \| Rebuttal \|
52	\|--------\|----------\|
53	\| "I know where the bottleneck is" \| You're probably wrong. Profile first. \|
54	\| "This is clearly slow" \| "Clearly slow" rarely matches profiler output. Measure. \|
55	\| "We'll optimize later" \| If it's slow enough to mention, it's slow enough to measure now. \|
56
57	## Verification
58
59	- [ ] Baseline metrics captured before any optimization
60	- [ ] Bottleneck identified by profiler (not assumption)
61	- [ ] Optimization targets only the profiled bottleneck
62	- [ ] Improvement measured with same test harness
63	- [ ] Before/after numbers documented
64
65	## References
66
67	- [references/performance-checklist.md](../../references/performance-checklist.md)
68	- [observability skill](../observability/SKILL.md)

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performance-optimization

64stars10forks

View on GitHub

How to install performance-optimization?

developersglobal/ai-agent-skills/performance-optimization

$npx -y skills add DevelopersGlobal/ai-agent-skills --skill performance-optimization

Installs into the current project.

Summary

Measure first, optimize second. Data-driven performance improvements with before/after benchmarks and production validation.

Files · 1

View on GitHub

SKILL.md

1	## Overview
2
3	Premature optimization is the root of all evil. But ignoring performance until it's a crisis is equally harmful. This skill enforces data-driven optimization: profile first, optimize the bottleneck, measure the improvement.
4
5	## When to Use
6
7	- When performance issues are reported in production
8	- Before optimizing any code (to ensure you're optimizing the right thing)
9	- When reviewing changes that touch performance-sensitive paths
10
11	## Process
12
13	### Step 1: Measure the Baseline
14
15	1. Reproduce the performance issue reliably.
16	2. Measure current performance: latency p50/p95/p99, throughput, memory, CPU.
17	3. Profile to find the actual bottleneck — not where you think it is.
18	4. Write the performance test you'll use to validate improvement.
19
20	Verify: You have concrete baseline numbers, not gut feelings.
21
22	### Step 2: Identify the Real Bottleneck
23
24	5. Use profiling tools: flame graphs, CPU profiles, memory profiles.
25	6. Find the top 3 hotspots by actual execution time (not lines of code).
26	7. The bottleneck is rarely where you expect it to be. Trust the data.
27
28	Verify: Bottleneck identified by profiling data, not assumption.
29
30	### Step 3: Optimize Only the Bottleneck
31
32	8. Fix only the profiled bottleneck — nothing else.
33	9. Common optimizations by type:
34	- CPU: Algorithmic improvement (O(n²) → O(n log n)), caching, batching
35	- Memory: Streaming instead of buffering, object pooling, lazy loading
36	- I/O: Connection pooling, N+1 query elimination, caching, async/parallel calls
37	- AI: Prompt caching, batch inference, smaller models for simpler tasks
38
39	Verify: Change targets the profiled bottleneck, not speculative improvements.
40
41	### Step 4: Measure the Improvement
42
43	10. Run the same performance test from Step 1.
44	11. Compare before vs. after metrics.
45	12. If improvement < 20%: the optimization may not be worth the complexity.
46
47	Verify: Improvement measured with the same test harness as baseline.
48
49	## Common Rationalizations (and Rebuttals)
50
51	\| Excuse \| Rebuttal \|
52	\|--------\|----------\|
53	\| "I know where the bottleneck is" \| You're probably wrong. Profile first. \|
54	\| "This is clearly slow" \| "Clearly slow" rarely matches profiler output. Measure. \|
55	\| "We'll optimize later" \| If it's slow enough to mention, it's slow enough to measure now. \|
56
57	## Verification
58
59	- [ ] Baseline metrics captured before any optimization
60	- [ ] Bottleneck identified by profiler (not assumption)
61	- [ ] Optimization targets only the profiled bottleneck
62	- [ ] Improvement measured with same test harness
63	- [ ] Before/after numbers documented
64
65	## References
66
67	- [references/performance-checklist.md](../../references/performance-checklist.md)
68	- [observability skill](../observability/SKILL.md)

People who liked this also viewed

6 picks

Type