Agent skill for v3-performance-engineer - invoke with $agent-v3-performance-engineer
Install with Tessl CLI
npx tessl i github:ruvnet/claude-flow --skill agent-v3-performance-engineer35
Does it follow best practices?
If you maintain this skill, you can automatically optimize it using the tessl CLI to improve its score:
npx tessl skill review --optimize ./path/to/skillEvaluation — 96%
↑ 3.20xAgent success when using this skill
Validation for skill structure
Attention and search benchmark targets
Attention speedup target range
0%
100%
Attention memory reduction target
0%
100%
Attention sequence lengths
0%
100%
Attention result structure
62%
100%
Search improvement target range
0%
100%
Search achieved flag
37%
100%
Search test query count
0%
100%
Search result structure
50%
75%
Memory usage tracking
50%
100%
Memory reduction target
0%
100%
Memory result structure
50%
66%
Sub-100ms latency note
0%
0%
Without context: $0.2798 · 1m 31s · 14 turns · 21 in / 5,606 out tokens
With context: $0.5294 · 2m 3s · 20 turns · 182 in / 7,779 out tokens
Startup and SONA adaptation benchmarks
Startup target 500ms
62%
100%
Startup return fields
40%
100%
SONA target 0.05ms
0%
100%
hrtime.bigint() for SONA
0%
100%
SONA scenario: pattern_recognition
0%
100%
SONA scenario: task_optimization
0%
100%
SONA scenario: error_correction
0%
100%
SONA scenario: performance_tuning
0%
100%
SONA scenario: behavior_adaptation
0%
100%
agentic-flow store-pattern command
0%
100%
store-pattern flags present
0%
100%
SONA result structure
16%
100%
SONA successRate field
100%
100%
Without context: $0.2938 · 1m 41s · 14 turns · 21 in / 5,230 out tokens
With context: $0.4665 · 1m 40s · 23 turns · 60 in / 5,300 out tokens
Regression detection and performance monitoring
MetricCollector key: flash_attention_speedup
0%
100%
MetricCollector key: search_improvement
0%
100%
MetricCollector key: memory_reduction
0%
100%
MetricCollector key: startup_time
0%
100%
MetricCollector key: sona_adaptation
0%
100%
5% regression threshold
50%
100%
Regression return: hasRegressions
100%
100%
Regression return: regressions array
100%
100%
Regression return: recommendations
100%
100%
PerformanceReport: summary field
100%
100%
PerformanceReport: achievements field
0%
100%
PerformanceReport: recommendations field
0%
100%
PerformanceReport: trends field
0%
100%
PerformanceReport: nextActions field
0%
100%
15-agent swarm benchmark
100%
100%
Swarm: 3 timing measurements
100%
100%
Without context: $0.4602 · 2m 26s · 20 turns · 26 in / 8,648 out tokens
With context: $0.5985 · 2m 31s · 23 turns · 101 in / 9,538 out tokens
Table of Contents
If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.