AI Agent Skill HUB

workflow-performance

Systematic performance analysis and optimization. Use when things are slow, need optimization, or preparing for scale.

13 stars
byNickCrew
View on GitHubInstallation ↓

Best use case

workflow-performance is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Systematic performance analysis and optimization. Use when things are slow, need optimization, or preparing for scale.

Teams using workflow-performance should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/workflow-performance/SKILL.md --create-dirs "https://raw.githubusercontent.com/NickCrew/Claude-Cortex/main/skills/workflow-performance/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/workflow-performance/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How workflow-performance Compares

Feature / Agentworkflow-performanceStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Systematic performance analysis and optimization. Use when things are slow, need optimization, or preparing for scale.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# Performance Optimization Workflow

Systematic approach to finding and fixing performance issues.

## Phase 1: Baseline
**Agents:** `performance-engineer`

Measure current state:
- Response times (p50, p95, p99)
- Memory usage
- CPU utilization
- Database query times
- Bundle sizes (frontend)
- Render performance

**Output:** Baseline metrics report

## Phase 2: Bottleneck Identification
**Agents:** `performance-engineer`

Analysis:
- Profiling (CPU, memory)
- Query analysis (slow query log, EXPLAIN)
- Bundle analysis (webpack-bundle-analyzer)
- Network analysis (waterfall, latency)

**Output:** Bottleneck list with priority ranking

## Phase 3: Optimization Planning
**Agents:** `requirements-analyst`

- Prioritize by impact vs effort
- Define expected improvements
- Determine implementation order
- Set target metrics

## Phase 4: Database Optimization
**Agents:** `database-optimizer`

Tasks:
- Query optimization (rewrite slow queries)
- Index creation/optimization
- Caching strategy (Redis, memcached)
- Connection pooling

## Phase 5: Code Optimization
**Agents:** `performance-engineer`

Focus:
- Algorithm efficiency (O(n) → O(log n))
- Memory management (leaks, allocation)
- Async operations (parallelize I/O)
- Application-level caching

## Phase 6: Frontend Optimization
**Agents:** `performance-engineer`

Tasks:
- Bundle size reduction
- Code splitting
- Lazy loading
- Asset optimization (images, fonts)
- Render optimization (virtualization, memoization)

## Phase 7: Infrastructure Optimization
**Agents:** `devops-architect`

Areas:
- Scaling strategy (horizontal/vertical)
- Caching layers (CDN, reverse proxy)
- Load balancing
- Resource allocation

## Phase 8: Validation
**Agents:** `performance-engineer`

**Blocking:** Must meet targets

Targets:
- Response time: <200ms (p95)
- Memory usage: <200MB
- Bundle size: <500KB

## Phase 9: Load Testing
**Agents:** `performance-engineer`

Scenarios:
- Normal load (expected traffic)
- Peak load (2-3x normal)
- Stress test (find breaking point)

Duration: 30min per scenario

## Phase 10: Monitoring Setup
**Agents:** `devops-architect`

- Performance dashboards
- Alerting rules (degradation detection)
- Automated profiling (continuous)

## Success Criteria
- [ ] Performance targets met
- [ ] Load tests pass
- [ ] Monitoring in place
- [ ] Documentation complete

## Targets
| Metric | Target |
|--------|--------|
| Response time improvement | 50% |
| Memory reduction | 30% |
| Cost reduction | 20% |

## Quick Reference

| Resource | Reference File |
|---|---|
| **Optimization Techniques** | `skills/workflow-performance/references/optimization-techniques.md` |

## Anti-patterns
- Optimizing without measuring first
- Micro-optimizations before algorithmic fixes
- Optimizing code that isn't the bottleneck
- No load testing before production

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