Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/proffesor-for-testing/agentic-qe/performance-analysisnpx skills add proffesor-for-testing/agentic-qe --skill performance-analysisgit clone --depth 1 https://github.com/proffesor-for-testing/agentic-qeWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/proffesor-for-testing/agentic-qe/performance-analysis)<a href="https://agentmods.dev/skills/proffesor-for-testing/agentic-qe/performance-analysis"><img src="https://agentmods.dev/badge/skills/proffesor-for-testing/agentic-qe/performance-analysis.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00020 | $0.03522 |
| Opus 5 | $0.00010 | $0.01761 |
| Sonnet 5 | $0.00004 | $0.00704 |
| Haiku 4.5 | $0.00002 | $0.00352 |
Grade A, and why
performance-analysis scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured today.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
const { exec } = require('child_process'); How it starts
The opening of the file, as written. The whole thing — 564 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Performance Analysis Skill
Comprehensive performance analysis suite for identifying bottlenecks, profiling swarm operations, generating detailed reports, and providing actionable optimization recommendations.
Overview
This skill consolidates all performance analysis capabilities:
- Bottleneck Detection: Identify performance bottlenecks across communication, processing, memory, and network
- Performance Profiling: Real-time monitoring and historical analysis of swarm operations
- Report Generation: Create comprehensive performance reports in multiple formats
- Optimization Recommendations: AI-powered suggestions for improving performance
Quick Start
Basic Bottleneck Detection
npx claude-flow bottleneck detect
Generate Performance Report
npx claude-flow analysis performance-report --format html --include-metrics
Analyze and Auto-Fix
npx claude-flow bottleneck detect --fix --threshold 15
Core Capabilities
1. Bottleneck Detection
Command Syntax
npx claude-flow bottleneck detect [options]
Options
--swarm-id, -s <id>- Analyze specific swarm (default: current)--time-range, -t <range>- Analysis period: 1h, 24h, 7d, all (default: 1h)--threshold <percent>- Bottleneck threshold percentage (default: 20)--export, -e <file>- Export analysis to file--fix- Apply automatic optimizations
Usage Examples
# Basic detection for current swarm
npx claude-flow bottleneck detect
# Analyze specific swarm over 24 hours
npx claude-flow bottleneck detect --swarm-id swarm-123 -t 24h
# Export detailed analysis
npx claude-flow bottleneck detect -t 24h -e bottlenecks.json
# Auto-fix detected issues
npx claude-flow bottleneck detect --fix --threshold 15
# Low threshold for sensitive detection
npx claude-flow bottleneck detect --threshold 10 --export critical-issues.json
Metrics Analyzed
Communication Bottlenecks:
- Message queue delays
- Agent response times
- Coordination overhead
- Memory access patterns
- Inter-agent communication latency
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- today First seen · 564 lines · 20 tokens per session scan A 1ef80aec71c0
performance-analysis is a skill published in the GitHub repository proffesor-for-testing/agentic-qe (473 stars, last pushed 2d ago), licensed MIT. It adds 20 tokens to every session and 3,522 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
performance-analysis
Comprehensive performance analysis, bottleneck detection, and optimization recommendations for Claude Flow swarms.
performance-analysis
Comprehensive performance analysis, bottleneck detection, and optimization recommendations for Claude Flow swarms.
performance-analysis
Comprehensive performance analysis, bottleneck detection, and optimization recommendations for Claude Flow swarms.
performance-analysis
Performance analysis, bottleneck detection, and optimization recommendations. Use when profiling slow code or systems, hunting a performance regression, or producing an optimization plan with measurable targets.
performance-analysis
Comprehensive performance analysis, bottleneck detection, and optimization recommendations for Claude Flow swarms.
performance-analysis
Comprehensive performance analysis, bottleneck detection, and optimization recommendations for Claude Flow swarms.