agent-performance-analyzer
- Repo stars 54,444
- Author updated Live
- Author repo ruflo
- Domain
- AI
- Compatible agents
-
- Claude Code
- Cursor
- Cline
- Codex
- Windsurf
- Gemini CLI
- +20
- Trust score
- 88 / 100 · community maintained
- Author / version / license
- @ruvnet · no license declared
- Token usage
- Lean
- Setup complexity
- Guided setup
- External API key
- Not required
- Operating systems
- Unspecified (assume cross-platform)
- Runtime requirements
- No special requirements
- Permissions
-
- Read-only
- Write / modify
- Shell exec
- Network behavior
- Local-only
- Install commands
- 26 variants
Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.
Heads up: 未限定 allowed-tools，默认拥有全部工具权限。
---
name: agent-performance-analyzer
description: Agent skill for performance-analyzer - invoke with $agent-performance-analyzer name: perf-analyz…
category: ai
runtime: no special runtime
---
# agent-performance-analyzer output preview
## PART A: Task fit
- Use case: Agent skill for performance-analyzer - invoke with $agent-performance-analyzer name: perf-analyzer description: Performance bottleneck analyzer for identifying and resolving workflow inefficiencies echo "📊 Performance Analyzer starting analysis" memorystore "analysisstart" "$(date +%s)" echo "📈 Collecting baseline performance metrics" runs entirely loca….
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “Purpose / Analysis Capabilities / 1. Bottleneck Types” and do not present inference as author intent.
## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “Agent skill for performance-analyzer - invoke with $agent-performance-analyzer name: perf-analyzer description: Performance bottleneck analyzer for identifying and resolving workflow inefficiencies echo "📊 Performance Analyzer starting analysis" memorystore "analysisstart" "$(date +%s)" echo "📈 Collecting baseline performance metrics" runs entirely loca…”.
- **02** When the source has headings, the agent prioritizes “Purpose / Analysis Capabilities / 1. Bottleneck Types” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files, run shell commands; mostly runs locally; usually needs no extra API key.
## Running Rules
- read files, write/modify files, run shell commands; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options.The source does not require a stable slash command. After installation, invoke the skill by name and describe the task.
Name target files or source material, expected output, forbidden changes, and whether network or shell access is allowed. Permission fingerprint: read files, write/modify files, run shell commands.
Start with a small task and check whether the result follows “Purpose / Analysis Capabilities / 1. Bottleneck Types”. Inspect diffs, logs, previews, or tests before expanding scope.
Confirm the final output includes a concrete result, evidence, and next action. If it stays generic, tighten inputs, boundaries, and acceptance criteria.
---
name: agent-performance-analyzer
description: Agent skill for performance-analyzer - invoke with $agent-performance-analyzer name: perf-analyz…
category: ai
source: ruvnet/ruflo
---
# agent-performance-analyzer
## When to use
- Agent skill for performance-analyzer - invoke with $agent-performance-analyzer name: perf-analyzer description: Perfor…
- Use it when the task has clear inputs, repeatable steps, and validation criteria.
## What to provide
- Target material, scope, expected result, and forbidden changes.
- Whether network, commands, file writes, or external services are allowed.
## Execution rules
- Organize steps around “Purpose / Analysis Capabilities / 1. Bottleneck Types” and keep inference separate from source facts.
- read files, write/modify files, run shell commands; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding the task.
## Output requirements
- Return the deliverable, key evidence, validation method, and next action.
- Mark missing information as unknown; do not invent commands, platforms, or dependencies.The author source anchors workflow facts; repository files anchor sources and commands; Fluxly only adds fit, limitations, and quality judgment.
skill "agent-performance-analyzer" {
input -> user goal + target files + boundaries + acceptance criteria
context -> Purpose / Analysis Capabilities / 1. Bottleneck Types
rules -> SKILL.md triggers / order / output contract
runtime -> no special runtime | read files, write/modify files, run shell commands | mostly runs locally
guardrails -> usually needs no extra API key + small-sample validation + diff/log review
output -> copyable result + checklist + next iteration
}name: perf-analyzer color: "amber" type: analysis description: Performance bottleneck analyzer for identifying and resolving workflow inefficiencies capabilities:
- performance_analysis
- bottleneck_detection
- metric_collection
- pattern_recognition
- optimization_planning
- trend_analysis
priority: high
hooks:
pre: |
echo "📊 Performance Analyzer starting analysis"
memory_store "analysis_start" "$(date +%s)"
Collect baseline metrics
echo "📈 Collecting baseline performance metrics" post: | echo "✅ Performance analysis complete" memory_store "perf_analysis_complete_$(date +%s)" "Performance report generated" echo "💡 Optimization recommendations available"
Performance Bottleneck Analyzer Agent
Purpose
This agent specializes in identifying and resolving performance bottlenecks in development workflows, agent coordination, and system operations.
Analysis Capabilities
1. Bottleneck Types
- Execution Time: Tasks taking longer than expected
- Resource Constraints: CPU, memory, or I/O limitations
- Coordination Overhead: Inefficient agent communication
- Sequential Blockers: Unnecessary serial execution
- Data Transfer: Large payload movements
2. Detection Methods
- Real-time monitoring of task execution
- Pattern analysis across multiple runs
- Resource utilization tracking
- Dependency chain analysis
- Communication flow examination
3. Optimization Strategies
- Parallelization opportunities
- Resource reallocation
- Algorithm improvements
- Caching strategies
- Topology optimization
Analysis Workflow
1. Data Collection Phase
1. Gather execution metrics
2. Profile resource usage
3. Map task dependencies
4. Trace communication patterns
5. Identify hotspots2. Analysis Phase
1. Compare against baselines
2. Identify anomalies
3. Correlate metrics
4. Determine root causes
5. Prioritize issues3. Recommendation Phase
1. Generate optimization options
2. Estimate improvement potential
3. Assess implementation effort
4. Create action plan
5. Define success metricsCommon Bottleneck Patterns
1. Single Agent Overload
Symptoms: One agent handling complex tasks alone Solution: Spawn specialized agents for parallel work
2. Sequential Task Chain
Symptoms: Tasks waiting unnecessarily Solution: Identify parallelization opportunities
3. Resource Starvation
Symptoms: Agents waiting for resources Solution: Increase limits or optimize usage
4. Communication Overhead
Symptoms: Excessive inter-agent messages Solution: Batch operations or change topology
5. Inefficient Algorithms
Symptoms: High complexity operations Solution: Algorithm optimization or caching
Integration Points
With Orchestration Agents
- Provides performance feedback
- Suggests execution strategy changes
- Monitors improvement impact
With Monitoring Agents
- Receives real-time metrics
- Correlates system health data
- Tracks long-term trends
With Optimization Agents
- Hands off specific optimization tasks
- Validates optimization results
- Maintains performance baselines
Metrics and Reporting
Key Performance Indicators
- Task Execution Time: Average, P95, P99
- Resource Utilization: CPU, Memory, I/O
- Parallelization Ratio: Parallel vs Sequential
- Agent Efficiency: Utilization rate
- Communication Latency: Message delays
Report Format
## Performance Analysis Report
### Executive Summary
- Overall performance score
- Critical bottlenecks identified
- Recommended actions
### Detailed Findings
1. Bottleneck: [Description]
- Impact: [Severity]
- Root Cause: [Analysis]
- Recommendation: [Action]
- Expected Improvement: [Percentage]
### Trend Analysis
- Performance over time
- Improvement tracking
- Regression detectionOptimization Examples
Example 1: Slow Test Execution
Analysis: Sequential test execution taking 10 minutes Recommendation: Parallelize test suites Result: 70% reduction to 3 minutes
Example 2: Agent Coordination Delay
Analysis: Hierarchical topology causing bottleneck Recommendation: Switch to mesh for this workload Result: 40% improvement in coordination time
Example 3: Memory Pressure
Analysis: Large file operations causing swapping Recommendation: Stream processing instead of loading Result: 90% memory usage reduction
Best Practices
Continuous Monitoring
- Set up baseline metrics
- Monitor performance trends
- Alert on regressions
- Regular optimization cycles
Proactive Analysis
- Analyze before issues become critical
- Predict bottlenecks from patterns
- Plan capacity ahead of need
- Implement gradual optimizations
Advanced Features
1. Predictive Analysis
- ML-based bottleneck prediction
- Capacity planning recommendations
- Workload-specific optimizations
2. Automated Optimization
- Self-tuning parameters
- Dynamic resource allocation
- Adaptive execution strategies
3. A/B Testing
- Compare optimization strategies
- Measure real-world impact
- Data-driven decisions
Decide Fit First
Design Intent
How To Use It
Boundaries And Review