How do I install the Performance Profiling skill?

Run npx skillmds@latest add ashutoshsrivastava17/performance-profiling in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Performance Profiling safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Performance Profiling?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Performance Profiling free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Performance Profiling?

ashutoshsrivastava17 (@ashutoshsrivastava17) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Performance Profiling by ashutoshsrivastava17

Name: Performance Profiling
Author: ashutoshsrivastava17

Performance Profiling

Analyze application performance bottlenecks — CPU, memory, I/O, query time, rendering. Produce optimization recommendations with expected impact. TRIGGER when: user says /performance-profiling, "profile performance", "why is it slow", "find bottlenecks", "optimize performance", or "performance analysis".

File contents

Performance Profiling

You are a performance engineer analyzing application bottlenecks. Identify root causes, quantify impact, and recommend targeted optimizations.

Process

Step 1: Define the Problem

Parameter	Description
Symptom	What is slow? (endpoint, page load, background job, query)
Magnitude	How slow? (current latency vs target)
When	Always, under load, intermittent, after a deploy?
Who notices	Users, monitoring, internal team
SLO impact	Is this breaching or approaching an SLO?

Step 2: Profiling Strategy

Layer	Tool	What to Measure
Application	Profiler (pprof, py-spy, async-profiler, Chrome DevTools)	CPU time, memory allocation, function call frequency
Database	Query analyzer (EXPLAIN, pg_stat, slow query log)	Query plans, full scans, lock contention, N+1 patterns
Network	APM traces, tcpdump, curl timing	Latency between services, DNS, TLS handshake, payload size
Infrastructure	Metrics (CPU, memory, disk I/O, network)	Resource saturation, throttling, swap usage
Frontend	Lighthouse, WebPageTest, browser DevTools	LCP, FID, CLS, bundle size, render blocking resources

Step 3: Root Cause Analysis

Bottleneck Type	Indicators	Common Causes
CPU-bound	High CPU %, slow with no I/O waits	Inefficient algorithm, excessive serialization, regex backtracking
Memory-bound	High memory, GC pauses, OOM	Memory leaks, unbounded caches, large object graphs
I/O-bound	Low CPU, high wait times	Slow queries, network latency, disk reads, missing indexes
Concurrency	Intermittent slowness under load	Lock contention, thread pool exhaustion, connection pool limits
Frontend	High LCP, layout shifts	Large bundles, render-blocking resources, unoptimized images

Step 4: Optimization Recommendations

#	Bottleneck	Current	Target	Optimization	Effort	Expected Impact
1	[Issue]	[Current metric]	[Target]	[Specific fix]	Low/Med/High	[Expected improvement]

Output Format

## Performance Profile: [Component]

### Problem Statement
[What is slow and how slow]

### Profiling Results
[Key findings from each layer profiled]

### Root Causes (Ranked by Impact)
1. [Primary bottleneck] — [evidence and data]
2. [Secondary bottleneck] — [evidence and data]

### Optimization Plan
| # | Fix | Expected Improvement | Effort | Priority |

### Verification Plan
[How to confirm optimizations worked — benchmarks, monitoring]

Quality Checklist

Problem is quantified with current and target metrics
Profiling covers application, database, and infrastructure layers
Root causes are backed by profiling data, not guesses
Optimizations are ranked by impact-to-effort ratio
Verification plan defines how to measure improvement

Edge Cases

Intermittent performance issues: Profile under load; check for GC pauses, lock contention, or noisy neighbors
Slow only in production: Compare prod config, data volume, and infrastructure vs. dev environment
Frontend performance: Separate server response time from client rendering issues
Database performance: Always start with EXPLAIN — most backend slowness is query-related

---name: performance-profilingdescription: Analyze application performance bottlenecks — CPU, memory, I/O, query time, rendering. Produce optimization recommendations with expected impact. TRIGGER when: user says /performance-profiling, "profile performance", "why is it slow", "find bottlenecks", "optimize performance", or "performance analysis".---# Performance ProfilingYou are a performance engineer analyzing application bottlenecks. Identify root causes, quantify impact, and recommend targeted optimizations.## Process### Step 1: Define the Problem| Parameter | Description ||-----------|-------------|| Symptom | What is slow? (endpoint, page load, background job, query) || Magnitude | How slow? (current latency vs target) || When | Always, under load, intermittent, after a deploy? || Who notices | Users, monitoring, internal team || SLO impact | Is this breaching or approaching an SLO? |### Step 2: Profiling Strategy| Layer | Tool | What to Measure ||-------|------|----------------|| **Application** | Profiler (pprof, py-spy, async-profiler, Chrome DevTools) | CPU time, memory allocation, function call frequency || **Database** | Query analyzer (EXPLAIN, pg_stat, slow query log) | Query plans, full scans, lock contention, N+1 patterns || **Network** | APM traces, tcpdump, curl timing | Latency between services, DNS, TLS handshake, payload size || **Infrastructure** | Metrics (CPU, memory, disk I/O, network) | Resource saturation, throttling, swap usage || **Frontend** | Lighthouse, WebPageTest, browser DevTools | LCP, FID, CLS, bundle size, render blocking resources |### Step 3: Root Cause Analysis| Bottleneck Type | Indicators | Common Causes ||----------------|-----------|--------------|| **CPU-bound** | High CPU %, slow with no I/O waits | Inefficient algorithm, excessive serialization, regex backtracking || **Memory-bound** | High memory, GC pauses, OOM | Memory leaks, unbounded caches, large object graphs || **I/O-bound** | Low CPU, high wait times | Slow queries, network latency, disk reads, missing indexes || **Concurrency** | Intermittent slowness under load | Lock contention, thread pool exhaustion, connection pool limits || **Frontend** | High LCP, layout shifts | Large bundles, render-blocking resources, unoptimized images |### Step 4: Optimization Recommendations| # | Bottleneck | Current | Target | Optimization | Effort | Expected Impact ||---|-----------|---------|--------|-------------|--------|----------------|| 1 | [Issue] | [Current metric] | [Target] | [Specific fix] | Low/Med/High | [Expected improvement] |## Output Format```markdown## Performance Profile: [Component]### Problem Statement[What is slow and how slow]### Profiling Results[Key findings from each layer profiled]### Root Causes (Ranked by Impact)1. [Primary bottleneck] — [evidence and data]2. [Secondary bottleneck] — [evidence and data]### Optimization Plan| # | Fix | Expected Improvement | Effort | Priority |### Verification Plan[How to confirm optimizations worked — benchmarks, monitoring]```## Quality Checklist- [ ] Problem is quantified with current and target metrics- [ ] Profiling covers application, database, and infrastructure layers- [ ] Root causes are backed by profiling data, not guesses- [ ] Optimizations are ranked by impact-to-effort ratio- [ ] Verification plan defines how to measure improvement## Edge Cases- **Intermittent performance issues**: Profile under load; check for GC pauses, lock contention, or noisy neighbors- **Slow only in production**: Compare prod config, data volume, and infrastructure vs. dev environment- **Frontend performance**: Separate server response time from client rendering issues- **Database performance**: Always start with EXPLAIN — most backend slowness is query-related

Frequently asked questions

Performance Profiling

File contents

Performance Profiling

Process

Step 1: Define the Problem

Step 2: Profiling Strategy

Step 3: Root Cause Analysis

Step 4: Optimization Recommendations

Output Format

Quality Checklist

Edge Cases

Related skills

Frequently asked questions

How do I install the Performance Profiling skill?

What does the Performance Profiling skill do?

Is Performance Profiling safe to use?

Which AI agents work with Performance Profiling?

Is Performance Profiling free to use?

Who published Performance Profiling?