Performance Profiler
Systematic performance profiling for Node.js, Python, and Go applications. Identifies CPU bottlenecks with flamegraphs, detects memory leaks with heap snapshots, analyzes bundle sizes, optimizes database queries, detects N+1 patterns, and runs load tests with k6 and Artillery. Enforces a measure-first methodology: establish baseline, identify bottleneck, fix, and verify improvement.
Golden Rule — Measure First: Profile → Confirm bottleneck → Fix → Measure again → Verify improvement. Every optimization needs baseline metrics, profiler evidence, the fix, post-fix metrics, and a delta. Full rule in references/cpu-and-memory-profiling.md.
Core Capabilities
- CPU profiling — Clinic.js/V8 flamegraphs (Node), py-spy/cProfile/scalene (Python), pprof (Go), Chrome DevTools (browser).
- Memory profiling — heap snapshots and before/after comparison, GC pressure analysis, leak detection, retained object graphs.
- Database optimization — EXPLAIN ANALYZE plan reading, N+1 detection and batching, slow query logs, missing-index identification, connection pool sizing.
- Bundle analysis — webpack/Next.js analyzers, tree-shaking, dynamic imports, heavy dependency identification.
- Load testing — k6 ramp-up scripts, SLA threshold enforcement in CI, P50/P95/P99 latency tracking, concurrent user simulation.
When to Use
- App is slow and you do not know where the bottleneck is.
- P99 latency exceeds SLA before a release.
- Memory usage grows over time (suspected leak).
- Bundle size increased after adding dependencies.
- Preparing for a traffic spike (load test before launch).
- Database queries taking >100ms, or verifying no regressions after a dependency upgrade.
Clarify First
Before profiling, confirm these inputs. If any is unknown or vague, ASK — do not assume:
Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.
Tools
| Tool |
Purpose |
Command |
benchmark_reporter.py |
Parse benchmark results and report regressions/improvements vs thresholds |
python scripts/benchmark_reporter.py results.json --fail-on-regression |
bottleneck_detector.py |
Analyze logs/traces to flag slow latency, queries, and spans |
python scripts/bottleneck_detector.py trace.json --latency-threshold 200 |
resource_analyzer.py |
Analyze CPU/memory/disk usage data and flag anomalies and trends |
python scripts/resource_analyzer.py metrics.json --cpu-threshold 80 |
All three accept a file path or - for stdin and support --json.
References
Load the reference that matches the task — keep this file lean and pull detail on demand:
- references/cpu-and-memory-profiling.md — the full Measure-First rule, Node.js CPU profiling (Clinic.js flamegraphs, V8 CPU profiles), and memory leak detection (Node heap snapshots, Python memray/tracemalloc). Read when chasing CPU or memory issues.
- references/database-and-bundle.md — EXPLAIN ANALYZE workflow, N+1 detection patterns and middleware script, Next.js bundle analyzer setup, quick size checks, and the common-bundle-wins table. Read when optimizing queries or bundle size.
- references/load-testing-and-methodology.md — full k6 load-test script, the before/after measurement template, quick-win optimization checklist, common pitfalls, best practices, troubleshooting table, and success criteria. Read when load testing or documenting a win.
Scope & Limitations
This skill covers:
- CPU and memory profiling for Node.js, Python, and Go applications using flamegraphs and heap snapshots
- Database query optimization including EXPLAIN ANALYZE interpretation, N+1 detection, and index recommendations
- Frontend bundle analysis and size reduction strategies for webpack and Next.js projects
- Load testing methodology with k6 including ramp-up patterns, threshold enforcement, and CI integration
This skill does NOT cover:
- Application Performance Monitoring (APM) platform setup and configuration (Datadog, New Relic, Grafana) — see
engineering/observability-designer
- Infrastructure-level performance tuning (kernel parameters, network stack, container resource limits) — see
engineering/senior-devops
- Security-focused performance concerns such as DDoS mitigation or rate limiting — see
engineering/senior-security
- Mobile application profiling (iOS Instruments, Android Profiler) — see
engineering/senior-mobile
Integration Points
| Skill |
Integration |
Data Flow |
engineering/observability-designer |
Performance profiling findings feed into observability dashboard design; alerting thresholds derived from profiling baselines |
Profiler baselines and SLA thresholds → Prometheus/Grafana alert rules and dashboard panels |
engineering/ci-cd-pipeline-builder |
k6 load tests and bundle size checks integrate as CI pipeline gates |
k6 threshold configs and bundle budget scripts → CI pipeline stage definitions |
engineering/database-designer |
Query optimization recommendations inform schema design decisions; index suggestions feed back to schema migrations |
EXPLAIN ANALYZE findings and index recommendations → schema migration files and index definitions |
engineering/senior-backend |
Backend architecture decisions incorporate profiling data; connection pool sizing and caching strategies validated by load tests |
Profiling reports and load test results → architecture decision records and implementation guidance |
engineering/tech-debt-tracker |
Performance regressions and unresolved bottlenecks are tracked as technical debt items with measured impact |
Before/after measurement reports and unresolved findings → tech debt backlog with quantified cost |
engineering/senior-frontend |
Bundle analysis results drive frontend optimization work; code-splitting and lazy-loading decisions backed by profiler data |
Bundle analyzer output and Lighthouse scores → frontend optimization tasks and component refactoring plans |
1---
2name: performance-profiler
3description: >
4 Performance profiling for Node.js, Python, and Go: CPU flamegraphs, memory leak detection,
5 bundle analysis, query optimization, and k6 load testing. Use when diagnosing slow
6 endpoints, memory growth, large bundles, or traffic spikes.
7license: MIT + Commons Clause
8metadata:
9 version: 1.1.0
10 author: borghei
11 category: engineering
12 domain: performance-engineering
13 tier: POWERFUL
14 updated: 2026-06-17
15 frameworks: clinic, py-spy, pprof, k6, webpack-bundle-analyzer
16---
17
18# Performance Profiler
19
20Systematic performance profiling for Node.js, Python, and Go applications. Identifies CPU bottlenecks with flamegraphs, detects memory leaks with heap snapshots, analyzes bundle sizes, optimizes database queries, detects N+1 patterns, and runs load tests with k6 and Artillery. Enforces a measure-first methodology: establish baseline, identify bottleneck, fix, and verify improvement.
21
22**Golden Rule — Measure First:** Profile → Confirm bottleneck → Fix → Measure again → Verify improvement. Every optimization needs baseline metrics, profiler evidence, the fix, post-fix metrics, and a delta. Full rule in `references/cpu-and-memory-profiling.md`.
23
24## Core Capabilities
25
26- **CPU profiling** — Clinic.js/V8 flamegraphs (Node), py-spy/cProfile/scalene (Python), pprof (Go), Chrome DevTools (browser).
27- **Memory profiling** — heap snapshots and before/after comparison, GC pressure analysis, leak detection, retained object graphs.
28- **Database optimization** — EXPLAIN ANALYZE plan reading, N+1 detection and batching, slow query logs, missing-index identification, connection pool sizing.
29- **Bundle analysis** — webpack/Next.js analyzers, tree-shaking, dynamic imports, heavy dependency identification.
30- **Load testing** — k6 ramp-up scripts, SLA threshold enforcement in CI, P50/P95/P99 latency tracking, concurrent user simulation.
31
32## When to Use
33
34- App is slow and you do not know where the bottleneck is.
35- P99 latency exceeds SLA before a release.
36- Memory usage grows over time (suspected leak).
37- Bundle size increased after adding dependencies.
38- Preparing for a traffic spike (load test before launch).
39- Database queries taking >100ms, or verifying no regressions after a dependency upgrade.
40
41## Clarify First
42
43Before profiling, confirm these inputs. If any is unknown or vague, ASK — do not assume:
44
45- [ ] **Runtime & symptom** — Node / Python / Go and CPU / memory / bundle / query / load-spike (selects the profiler and toolchain)
46- [ ] **Baseline + SLA target** — current numbers and the P95/P99 (or size) threshold to beat (measure-first needs both to verify a delta)
47- [ ] **Environment** — local / staging / prod determines the safe profiling method and whether load testing is allowed
48
49Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.
50
51## Tools
52
53| Tool | Purpose | Command |
54|------|---------|---------|
55| `benchmark_reporter.py` | Parse benchmark results and report regressions/improvements vs thresholds | `python scripts/benchmark_reporter.py results.json --fail-on-regression` |
56| `bottleneck_detector.py` | Analyze logs/traces to flag slow latency, queries, and spans | `python scripts/bottleneck_detector.py trace.json --latency-threshold 200` |
57| `resource_analyzer.py` | Analyze CPU/memory/disk usage data and flag anomalies and trends | `python scripts/resource_analyzer.py metrics.json --cpu-threshold 80` |
58
59All three accept a file path or `-` for stdin and support `--json`.
60
61## References
62
63Load the reference that matches the task — keep this file lean and pull detail on demand:
64
65- **[references/cpu-and-memory-profiling.md](references/cpu-and-memory-profiling.md)** — the full Measure-First rule, Node.js CPU profiling (Clinic.js flamegraphs, V8 CPU profiles), and memory leak detection (Node heap snapshots, Python memray/tracemalloc). Read when chasing CPU or memory issues.
66- **[references/database-and-bundle.md](references/database-and-bundle.md)** — EXPLAIN ANALYZE workflow, N+1 detection patterns and middleware script, Next.js bundle analyzer setup, quick size checks, and the common-bundle-wins table. Read when optimizing queries or bundle size.
67- **[references/load-testing-and-methodology.md](references/load-testing-and-methodology.md)** — full k6 load-test script, the before/after measurement template, quick-win optimization checklist, common pitfalls, best practices, troubleshooting table, and success criteria. Read when load testing or documenting a win.
68
69## Scope & Limitations
70
71**This skill covers:**
72- CPU and memory profiling for Node.js, Python, and Go applications using flamegraphs and heap snapshots
73- Database query optimization including EXPLAIN ANALYZE interpretation, N+1 detection, and index recommendations
74- Frontend bundle analysis and size reduction strategies for webpack and Next.js projects
75- Load testing methodology with k6 including ramp-up patterns, threshold enforcement, and CI integration
76
77**This skill does NOT cover:**
78- Application Performance Monitoring (APM) platform setup and configuration (Datadog, New Relic, Grafana) — see `engineering/observability-designer`
79- Infrastructure-level performance tuning (kernel parameters, network stack, container resource limits) — see `engineering/senior-devops`
80- Security-focused performance concerns such as DDoS mitigation or rate limiting — see `engineering/senior-security`
81- Mobile application profiling (iOS Instruments, Android Profiler) — see `engineering/senior-mobile`
82
83## Integration Points
84
85| Skill | Integration | Data Flow |
86|-------|-------------|-----------|
87| `engineering/observability-designer` | Performance profiling findings feed into observability dashboard design; alerting thresholds derived from profiling baselines | Profiler baselines and SLA thresholds → Prometheus/Grafana alert rules and dashboard panels |
88| `engineering/ci-cd-pipeline-builder` | k6 load tests and bundle size checks integrate as CI pipeline gates | k6 threshold configs and bundle budget scripts → CI pipeline stage definitions |
89| `engineering/database-designer` | Query optimization recommendations inform schema design decisions; index suggestions feed back to schema migrations | EXPLAIN ANALYZE findings and index recommendations → schema migration files and index definitions |
90| `engineering/senior-backend` | Backend architecture decisions incorporate profiling data; connection pool sizing and caching strategies validated by load tests | Profiling reports and load test results → architecture decision records and implementation guidance |
91| `engineering/tech-debt-tracker` | Performance regressions and unresolved bottlenecks are tracked as technical debt items with measured impact | Before/after measurement reports and unresolved findings → tech debt backlog with quantified cost |
92| `engineering/senior-frontend` | Bundle analysis results drive frontend optimization work; code-splitting and lazy-loading decisions backed by profiler data | Bundle analyzer output and Lighthouse scores → frontend optimization tasks and component refactoring plans |
borghei