Performance Profiler
alirezarezvani/claude-skills
Systematic performance profiling for Node.js, Python, and Go applications.
Performance profiling for Node.js, Python, and Go: CPU flamegraphs, memory leak detection, bundle analysis, query optimization, and k6 load testing.
Install Performance Profiler
$ npx skills add borghei/Claude-Skills --skill performance-profiler -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install borghei/Claude-Skills performance-profiler --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/borghei/Claude-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/engineering/performance-profiler .claude/skills/performance-profiler && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "performance-profiler" agent skill from https://github.com/borghei/Claude-Skills/tree/main/engineering/performance-profiler into .claude/skills/performance-profiler/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "performance-profiler", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/borghei/Claude-Skills/tree/main/engineering/performance-profilerType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add borghei/Claude-Skills --skill performance-profiler -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install borghei/Claude-Skills performance-profiler --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/borghei/Claude-Skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/engineering/performance-profiler .agents/skills/performance-profiler && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "performance-profiler" agent skill from https://github.com/borghei/Claude-Skills/tree/main/engineering/performance-profiler into .agents/skills/performance-profiler/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "performance-profiler", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add borghei/Claude-Skills --skill performance-profiler -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install borghei/Claude-Skills performance-profiler --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/borghei/Claude-Skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/engineering/performance-profiler .cursor/skills/performance-profiler && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "performance-profiler" agent skill from https://github.com/borghei/Claude-Skills/tree/main/engineering/performance-profiler into .cursor/skills/performance-profiler/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "performance-profiler", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/borghei/Claude-Skills.git --path engineering/performance-profiler--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add borghei/Claude-Skills --skill performance-profiler -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install borghei/Claude-Skills performance-profiler --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/borghei/Claude-Skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/engineering/performance-profiler .gemini/skills/performance-profiler && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "performance-profiler" agent skill from https://github.com/borghei/Claude-Skills/tree/main/engineering/performance-profiler into .gemini/skills/performance-profiler/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "performance-profiler", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install borghei/Claude-Skills performance-profilerInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add borghei/Claude-Skills --skill performance-profiler -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/borghei/Claude-Skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/engineering/performance-profiler .github/skills/performance-profiler && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "performance-profiler" agent skill from https://github.com/borghei/Claude-Skills/tree/main/engineering/performance-profiler into .github/skills/performance-profiler/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "performance-profiler", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add borghei/Claude-Skills --skill performance-profiler -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install borghei/Claude-Skills performance-profiler --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/borghei/Claude-Skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/engineering/performance-profiler .opencode/skills/performance-profiler && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "performance-profiler" agent skill from https://github.com/borghei/Claude-Skills/tree/main/engineering/performance-profiler into .opencode/skills/performance-profiler/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "performance-profiler", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
performance-profiler- GitHub stars
- 874
- Token cost
- ~1.8k tokens
- SKILL.md length
- 746 words
- Files
- 7 (incl. scripts, references)
- Skills in repo
- 364
- Repo updated
- First seen
- Licence
- MIT
At a glance
Performance profiling for Node.js, Python, and Go: CPU flamegraphs, memory leak detection, bundle analysis, query optimization, and k6 load testing.
- Diagnosing slow endpoints
- SKILL.md covers Core Capabilities, When to Use, Clarify First and Tools, plus 3 more sections
- Runs Python scripts from its folder; calls python
- Tasks that involve Performance optimization
What it does
Performance Profiler is an agent skill from borghei/Claude-Skills. Performance profiling for Node.js, Python, and Go: CPU flamegraphs, memory leak detection, bundle analysis, query optimization, and k6 load testing. Use when diagnosing slow endpoints, memory growth, large bundles, or traffic spikes.
Its SKILL.md is about 1.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 8 other files, including scripts and reference files (for example `references/cpu-and-memory-profiling.md`, `references/database-and-bundle.md` and `references/load-testing-and-methodology.md`).
It sits in Testing & QA, covering Performance optimization, Load testing and Query optimization. It works with Python and Node.js. The repository describes itself as: 385 AI skills, 77 expert agents, and 900 stdlib Python tools for every team: engineering, PM, marketing, C-level, compliance, business ops, research, and a LinkedIn toolkit… The licence is MIT.
When your agent uses it
- Diagnosing slow endpoints
- Tasks that involve Performance optimization
- Tasks that involve Load testing
Example prompts
- “/performance-profiler”
Requirements
- Python 3
- Node.js
What it can do on your machine
Read from SKILL.md and the folder at commit c9a1487. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 3 files in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
python
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Performance Profiler loads about 1.8k tokens when it runs, and up to ~5.2k if it reads all its reference files. Until then it costs about 64 tokens; SKILL.md has 746 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
SKILL.md
The full file from borghei/Claude-Skills at commit c9a1487, republished under its MIT licence (© borghei). 746 words, ~1,789 tokens.
Download SKILL.mdSave it as
.claude/skills/performance-profiler/SKILL.md (or your agent's skills folder). This skill also uses 6 other files; get the full folder from GitHub.- name
- performance-profiler
- description
- Performance profiling for Node.js, Python, and Go: CPU flamegraphs, memory leak detection, bundle analysis, query optimization, and k6 load testing. Use when diagnosing slow endpoints, memory growth, large bundles, or traffic spikes.
- license
- MIT + Commons Clause
- metadata.version
- 1.1.0
- metadata.author
- borghei
- metadata.category
- engineering
- metadata.domain
- performance-engineering
- metadata.tier
- POWERFUL
- metadata.updated
- 2026-06-17
- metadata.frameworks
- clinic, py-spy, pprof, k6, webpack-bundle-analyzer
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:
- Runtime & symptom — Node / Python / Go and CPU / memory / bundle / query / load-spike (selects the profiler and toolchain)
- Baseline + SLA target — current numbers and the P95/P99 (or size) threshold to beat (measure-first needs both to verify a delta)
- Environment — local / staging / prod determines the safe profiling method and whether load testing is allowed
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.
Show full SKILL.md (277 more words)Show less
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 |
© borghei, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 6 other files (scripts, references) in engineering/performance-profiler of borghei/Claude-Skills.
- SKILL.md
- references/cpu-and-memory-profiling.md
- references/database-and-bundle.md
- references/load-testing-and-methodology.md
- scripts/benchmark_reporter.py
- scripts/bottleneck_detector.py
- scripts/resource_analyzer.py
Open the folder on GitHubat commit c9a1487
Compare with similar skills
Performance Profiler next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Performance Profiler this skillborghei/Claude-Skills | 874 | — | ~1.8k | Automated safety check: Pass | MIT | |
| Performance Profileralirezarezvani/claude-skills | 28k | — | ~684 | Automated safety check: Pass | MIT | |
| Afrexai Performance EngineeringLeoYeAI/openclaw-master-skills | 2.2k | — | ~7.1k | Automated safety check: Pass | MIT | |
| Django Filter Benchmarksaleor/saleor | 23k | — | ~2.3k | Automated safety check: Pass | BSD-3-Clause | |
| Phy Memory Leak DetectorLeoYeAI/openclaw-master-skills | 2.2k | — | ~4.3k | Automated safety check: Pass | Apache-2.0 | |
| Nestjs Expertdavila7/claude-code-templates | 32k | 6 repos | ~5.3k | Automated safety check: Notes | MIT |
Similar skills
Afrexai Performance Engineering
LeoYeAI/openclaw-master-skills
Complete performance engineering system — profiling, optimization, load testing, capacity planning, and performance culture.
Django Filter Benchmark
saleor/saleor
Benchmarks Django ORM filters in Saleor by generating bulk data, extracting the SQL and running EXPLAIN ANALYZE to check index usage.
Phy Memory Leak Detector
LeoYeAI/openclaw-master-skills
Static memory leak pattern scanner for Node.js, Python, Go, and Java.
Nestjs Expert
davila7/claude-code-templates
Nest.js framework expert specializing in module architecture, dependency injection, middleware, guards, interceptors, testing with Jest/Supertest, TypeORM/Mongoose integration, and Passport.js…
HTTP Load Profiler
zebbern/claude-code-guide
Run stepped HTTP load tests with ab/wrk, ramping concurrency levels to collect p50/p90/p99 latency, detect performance inflection points, and recommend optimal concurrency.
More from borghei/Claude-Skills
All 364 skills in this repoAgents In The Team
borghei/Claude-Skills
Run delivery when AI coding and ops agents take tickets. An agent skill from borghei/Claude-Skills.
AI Content Disclosure
borghei/Claude-Skills
Check AI-generated marketing content and reviews for required disclosures under the EU AI Act, FTC rules and platform AI-label policies.
AI Prototyping
borghei/Claude-Skills
Idea to AI-generated prototype to customer validation to engineering handoff.
Analytics Engineer
borghei/Claude-Skills
Analytics engineering across data modeling, dbt, transformation, and semantic layers.
Ansoff Matrix
borghei/Claude-Skills
Ansoff Matrix — 4-quadrant framework for growth options: market penetration, market/product development, and diversification.
Brainstorm Okrs
borghei/Claude-Skills
OKR brainstorming and validation using the Radical Focus framework — outcome objectives, measurable key results, counter-metrics.
Related
Categories
Questions about Performance Profiler
What does Performance Profiler do?
Performance profiling for Node.js, Python, and Go: CPU flamegraphs, memory leak detection, bundle analysis, query optimization, and k6 load testing. Performance Profiler is an agent skill from borghei/Claude-Skills.js, Python, and Go: CPU flamegraphs, memory leak detection, bundle analysis, query optimization, and k6 load testing.
When should I use Performance Profiler?
Performance Profiler fits situations like: diagnosing slow endpoints; tasks that involve Performance optimization; tasks that involve Load testing.
How do I install Performance Profiler in Claude Code?
Run `npx skills add borghei/Claude-Skills --skill performance-profiler -a claude-code`. Or copy the skill folder (engineering/performance-profiler in borghei/Claude-Skills) into .claude/skills/performance-profiler in your project. Claude Code loads it when a task matches its description.
How do I install Performance Profiler in Codex?
Run `npx skills add borghei/Claude-Skills --skill performance-profiler -a codex`. Or copy the skill folder (engineering/performance-profiler in borghei/Claude-Skills) into .agents/skills/performance-profiler in your project. Codex loads it when a task matches its description.
Can I use Performance Profiler in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add borghei/Claude-Skills --skill performance-profiler -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/performance-profiler, .gemini/skills/performance-profiler, .github/skills/performance-profiler and .opencode/skills/performance-profiler in your project.
What does Performance Profiler need to run?
Going by SKILL.md and its folder, Performance Profiler needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3; Node.js.
Does Performance Profiler access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Performance Profiler safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Performance Profiler use?
Performance Profiler is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Performance Profiler use?
About 1.8k tokens (SKILL.md is roughly 7.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 3.4k tokens, read only when the agent opens those files.
What are the alternatives to Performance Profiler?
Skills that share tags, products or a category with Performance Profiler: Performance Profiler (alirezarezvani/claude-skills, 28k stars), Afrexai Performance Engineering (LeoYeAI/openclaw-master-skills, 2.2k stars), Django Filter Benchmark (saleor/saleor, 23k stars) and Phy Memory Leak Detector (LeoYeAI/openclaw-master-skills, 2.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Performance Profiler?
borghei (a GitHub user) maintains it in borghei/Claude-Skills, which has 874 GitHub stars. The repository holds 364 skills in this directory. The repository was last updated on October 7, 2026.
Source: borghei/Claude-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.