Agent skill

when-analyzing-performance-use-performance-analysis

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Install this agent skill to your Project

npx add-skill https://github.com/DNYoussef/context-cascade/tree/main/skills/operations/when-analyzing-performance-use-performance-analysis

SKILL.md

/============================================================================/ /* WHEN-ANALYZING-PERFORMANCE-USE-PERFORMANCE-ANALYSIS SKILL :: VERILINGUA x VERIX EDITION / /============================================================================*/

name: when-analyzing-performance-use-performance-analysis version: 1.0.0 description: | [assert|neutral] Comprehensive performance analysis, bottleneck detection, and optimization recommendations for Claude Flow swarms [ground:given] [conf:0.95] [state:confirmed] category: performance tags:

  • performance
  • analysis
  • bottleneck
  • optimization
  • profiling author: system cognitive_frame: primary: evidential goal_analysis: first_order: "Execute when-analyzing-performance-use-performance-analysis workflow" second_order: "Ensure quality and consistency" third_order: "Enable systematic performance processes"

/----------------------------------------------------------------------------/ /* S0 META-IDENTITY / /----------------------------------------------------------------------------*/

[define|neutral] SKILL := { name: "when-analyzing-performance-use-performance-analysis", category: "performance", version: "1.0.0", layer: L1 } [ground:given] [conf:1.0] [state:confirmed]

/----------------------------------------------------------------------------/ /* S1 COGNITIVE FRAME / /----------------------------------------------------------------------------*/

[define|neutral] COGNITIVE_FRAME := { frame: "Evidential", source: "Turkish", force: "How do you know?" } [ground:cognitive-science] [conf:0.92] [state:confirmed]

Kanitsal Cerceve (Evidential Frame Activation)

Kaynak dogrulama modu etkin.

/----------------------------------------------------------------------------/ /* S2 TRIGGER CONDITIONS / /----------------------------------------------------------------------------*/

[define|neutral] TRIGGER_POSITIVE := { keywords: ["when-analyzing-performance-use-performance-analysis", "performance", "workflow"], context: "user needs when-analyzing-performance-use-performance-analysis capability" } [ground:given] [conf:1.0] [state:confirmed]

/----------------------------------------------------------------------------/ /* S3 CORE CONTENT / /----------------------------------------------------------------------------*/

Performance Analysis SOP

Kanitsal Cerceve (Evidential Frame Activation)

Kaynak dogrulama modu etkin.

Overview

Comprehensive performance analysis for Claude Flow swarms including bottleneck detection, profiling, benchmarking, and actionable optimization recommendations.

Agents & Responsibilities

performance-analyzer

Role: Analyze system performance and identify issues Responsibilities:

  • Collect performance metrics
  • Analyze resource utilization
  • Identify bottlenecks
  • Generate analysis reports

performance-benchmarker

Role: Run performance benchmarks and comparisons Responsibilities:

  • Execute benchmark suites
  • Compare performance across configurations
  • Establish performance baselines
  • Validate improvements

perf-analyzer

Role: Deep performance profiling and optimization Responsibilities:

  • Profile code execution
  • Analyze memory usage
  • Optimize critical paths
  • Recommend improvements

Phase 1: Establish Baseline

Objective

Measure current performance and establish baseline metrics.

Scripts

bash
# Collect baseline metrics
npx claude-flow@alpha performance baseline \
  --duration 300 \
  --interval 5 \
  --output baseline-metrics.json

# Run benchmark suite
npx claude-flow@alpha benchmark run \
  --type swarm \
  --iterations 10 \
  --output benchmark-results.json

# Profile system resources
npx claude-flow@alpha performance profile \
  --include-cpu \
  --include-memory \
  --include-network \
  --output resource-profile.json

# Collect agent metrics
npx claude-flow@alpha agent metrics --all --format json > agent-metrics.json

# Store baseline
npx claude-flow@alpha memory store \
  --key "performance/baseline" \
  --file baseline-metrics.json

# Generate baseline report
npx claude-flow@alpha performance report \
  --type baseline \
  --metrics baseline-metrics.json \
  --output baseline-report.md

Key Baseline Metrics

Swarm-Level:

  • Total throughput (tasks/min)
  • Average latency (ms)
  • Resource utilization (%)
  • Error rate (%)
  • Coordination overhead (ms)

Agent-Level:

  • Task completion rate
  • Response time (ms)
  • CPU usage (%)
  • Memory usage (MB)
  • Idle time (%)

System-Level:

  • Total CPU usage (%)
  • Total memory usage (MB)
  • Network bandwidth (MB/s)
  • Disk I/O (MB/s)

Memory Patterns

bash
# Store performance baseline
npx claude-flow@alpha memory store \
  --key "performance/baseline/timestamp" \
  --value "$(date -Iseconds)"

npx claude-flow@alpha memory store \
  --key "performance/baseline/metrics" \
  --value '{
    "throughput": 145.2,
    "latency": 38.5,
    "utilization": 0.78,
    "errorRate": 0.012,
    "timestamp": "'$(date -Iseconds)'"
  }'

Phase 2: Profile System

Objective

Deep profiling of system components to identify performance characteristics.

Scripts

bash
# Profile swarm execution
npx claude-flow@alpha performance profile-swarm \
  --duration 300 \
  --sample-rate 100 \
  --output swarm-profile.json

# Profile individual agents
for AGENT in $(npx claude-flow@alpha agent list --format json | jq -r '.[].id'); do
  npx claude-flow@alpha performance profile-agent \
    --agent-id "$AGENT" \
    --duration 60 \
    --output "profiles/agent-$AGENT.json"
done

# Profile memory usage
npx claude-flow@alpha memory profile \
  --show-hotspots \
  --show-leaks \
  --output memory-profile.json

# Profile network communication
npx claude-flow@alpha performance profile-network \
  --show-latency \
  --show-bandwidth \
  --output network-profile.json

# Generate flamegraph
npx claude-flow@alpha performance flamegraph \
  --input swarm-profile.json \
  --output flamegraph.svg

# Analyze CPU hotspots
npx claude-flow@alpha performance hotspots \
  --type cpu \
  --threshold 5 \
  --output cpu-hotspots.json

Profiling Analysis

bash
# Identify slow functions
SLOW_FUNCTIONS=$(jq '[.profile[] | select(.time > 100)]' swarm-profile.json)

# Identify memory hogs
MEMORY_HOGS=$(jq '[.memory[] | sele

/*----------------------------------------------------------------------------*/
/* S4 SUCCESS CRITERIA                                                         */
/*----------------------------------------------------------------------------*/

[define|neutral] SUCCESS_CRITERIA := {
  primary: "Skill execution completes successfully",
  quality: "Output meets quality thresholds",
  verification: "Results validated against requirements"
} [ground:given] [conf:1.0] [state:confirmed]

/*----------------------------------------------------------------------------*/
/* S5 MCP INTEGRATION                                                          */
/*----------------------------------------------------------------------------*/

[define|neutral] MCP_INTEGRATION := {
  memory_mcp: "Store execution results and patterns",
  tools: ["mcp__memory-mcp__memory_store", "mcp__memory-mcp__vector_search"]
} [ground:witnessed:mcp-config] [conf:0.95] [state:confirmed]

/*----------------------------------------------------------------------------*/
/* S6 MEMORY NAMESPACE                                                         */
/*----------------------------------------------------------------------------*/

[define|neutral] MEMORY_NAMESPACE := {
  pattern: "skills/performance/when-analyzing-performance-use-performance-analysis/{project}/{timestamp}",
  store: ["executions", "decisions", "patterns"],
  retrieve: ["similar_tasks", "proven_patterns"]
} [ground:system-policy] [conf:1.0] [state:confirmed]

[define|neutral] MEMORY_TAGGING := {
  WHO: "when-analyzing-performance-use-performance-analysis-{session_id}",
  WHEN: "ISO8601_timestamp",
  PROJECT: "{project_name}",
  WHY: "skill-execution"
} [ground:system-policy] [conf:1.0] [state:confirmed]

/*----------------------------------------------------------------------------*/
/* S7 SKILL COMPLETION VERIFICATION                                            */
/*----------------------------------------------------------------------------*/

[direct|emphatic] COMPLETION_CHECKLIST := {
  agent_spawning: "Spawn agents via Task()",
  registry_validation: "Use registry agents only",
  todowrite_called: "Track progress with TodoWrite",
  work_delegation: "Delegate to specialized agents"
} [ground:system-policy] [conf:1.0] [state:confirmed]

/*----------------------------------------------------------------------------*/
/* S8 ABSOLUTE RULES                                                           */
/*----------------------------------------------------------------------------*/

[direct|emphatic] RULE_NO_UNICODE := forall(output): NOT(unicode_outside_ascii) [ground:windows-compatibility] [conf:1.0] [state:confirmed]

[direct|emphatic] RULE_EVIDENCE := forall(claim): has(ground) AND has(confidence) [ground:verix-spec] [conf:1.0] [state:confirmed]

[direct|emphatic] RULE_REGISTRY := forall(agent): agent IN AGENT_REGISTRY [ground:system-policy] [conf:1.0] [state:confirmed]

/*----------------------------------------------------------------------------*/
/* PROMISE                                                                     */
/*----------------------------------------------------------------------------*/

[commit|confident] <promise>WHEN_ANALYZING_PERFORMANCE_USE_PERFORMANCE_ANALYSIS_VERILINGUA_VERIX_COMPLIANT</promise> [ground:self-validation] [conf:0.99] [state:confirmed]

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