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Agent skill

Common Performance Engineering

by HoangNguyen0403 in HoangNguyen0403/agent-skills-standard

Enforce universal standards for high-performance development.

MITAuto-check passedDevelopment

Install Common Performance Engineering

skills CLI
$ npx skills add HoangNguyen0403/agent-skills-standard --skill common-performance-engineering -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install HoangNguyen0403/agent-skills-standard common-performance-engineering --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/HoangNguyen0403/agent-skills-standard.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/common/common-performance-engineering .claude/skills/common-performance-engineering && rm -rf skills-src

Use ~/.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/

Facts

Skill name
common-performance-engineering
GitHub stars
572
Token cost
~762 tokens
SKILL.md length
281 words
Files
3 (incl. references)
Skills in repo
211
Repo updated
First seen
Licence
MIT

At a glance

Enforce universal standards for high-performance development.

  • Works in 4 steps: Baseline: Profile before changing… → Identify: Find top bottleneck (N+1… → Fix: Apply targeted optimization from… → …
  • Profiling bottlenecks
  • SKILL.md covers Priority: P0 (CRITICAL), Workflow, Resource Management and Network & I/O, plus 5 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Common Performance Engineering is an agent skill from HoangNguyen0403/agent-skills-standard. Enforce universal standards for high-performance development. Use when profiling bottlenecks, reducing latency, fixing memory leaks, improving throughput, or optimizing algorithm complexity in any language.

Its SKILL.md is about 760 tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including reference files (for example `evals/evals.json` and `references/implementation.md`).

It sits in Development, covering Performance optimization. The repository describes itself as: A collection of Agent Skills Standard and Best Practice for Programming Languages, Frameworks that help our AI Agent follow best practies on frameworks and programming laguages. The licence is MIT.

When your agent uses it

  • Profiling bottlenecks
  • Reducing latency
  • Fixing memory leaks
  • Improving throughput

Example prompts

  • “/common-performance-engineering”

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  1. Baseline: Profile before changing anything — measure CPU, memory, and latency.
  2. Identify: Find top bottleneck (N+1 query, hot loop, memory leak).
  3. Fix: Apply targeted optimization from sections below.
  4. Verify: Re-profile to confirm improvement and check for regressions.

What it can do on your machine

Read from SKILL.md and the folder at commit b529c2d. 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

    No scripts in the folder and no shell commands in SKILL.md.

    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

Common Performance Engineering loads about 762 tokens when it runs, and up to ~893 if it reads all its reference files. Until then it costs about 59 tokens; SKILL.md has 281 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~59
When it runs · the whole SKILL.md, loaded when a task matches
~762
With references · SKILL.md plus every file in references/, read only if the agent opens them
~893

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); files beside SKILL.md are not scanned.

SKILL.md

The full file from HoangNguyen0403/agent-skills-standard at commit b529c2d, republished under its MIT licence (© HoangNguyen0403). 281 words, ~762 tokens.

Download SKILL.mdSave it as .claude/skills/common-performance-engineering/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
common-performance-engineering
description
Enforce universal standards for high-performance development. Use when profiling bottlenecks, reducing latency, fixing memory leaks, improving throughput, or optimizing algorithm complexity in any language.

Performance Engineering Standards

Priority: P0 (CRITICAL)

Workflow

  1. Baseline: Profile before changing anything — measure CPU, memory, and latency.
  2. Identify: Find top bottleneck (N+1 query, hot loop, memory leak).
  3. Fix: Apply targeted optimization from sections below.
  4. Verify: Re-profile to confirm improvement and check for regressions.

Resource Management

  • Memory Efficiency:
  • Avoid memory leaks: explicit cleanup of listeners, observers, and streams.
  • Optimize data structures: Set for lookups, List for iteration.
  • Lazy Initialization: Initialize expensive objects only when needed.
  • CPU Optimization:
  • Aim for O(1) or O(n); avoid O(n^2) in critical paths.
  • Offload heavy computations to background threads or workers.
  • Memoize pure, expensive functions.

See implementation examples for memoization and batching patterns.

Network & I/O

  • Payload Reduction: Use efficient serialization (Protobuf, JSON minification) and compression (gzip/br).
  • Batching: Group multiple small requests into single bulk operations.
  • Caching: Implement multi-level caching (Memory -> Storage -> Network) with appropriate TTL and invalidation.
  • Non-blocking I/O: Always use asynchronous operations for file system and network access.

UI/UX Performance

  • Minimize Main Thread Work: Keep animations and interactions fluid by offloading to workers.
  • Virtualization: Use lazy loading or virtualization for long lists/large datasets.
  • Tree Shaking: Ensure build tools remove unused code and dependencies.

Monitoring & Testing

  • Benchmarking: Write micro-benchmarks for performance-critical functions.
  • SLIs/SLOs: Define Service Level Indicators (latency, throughput) and Objectives.
  • Load Testing: Test system behavior under peak and stress conditions.

Anti-Patterns

  • No premature optimization: Profile first, fix proven bottlenecks only.
  • No N+1 queries: Always batch and paginate data-access operations.
  • No synchronous I/O on main thread: Async all file/network access.

References

Canonical response anchors

When this skill applies, preserve the following domain terminology or equivalent concrete examples in the answer when relevant:

  • lazy

  • Additional task-grounded exact anchors: premature

© HoangNguyen0403, 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 2 other files (references) in skills/common/common-performance-engineering of HoangNguyen0403/agent-skills-standard.

  • SKILL.md
  • evals/evals.json
  • references/implementation.md

Open the folder on GitHubat commit b529c2d

Compare with similar skills

Common Performance Engineering 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.

Common Performance Engineering compared with similar skills
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LLM Torch Profiler Analysissgl-project/sglang37k2 repos~6.4kAutomated safety check: PassApache-2.0
Pycrazyguitar/pysheeet8.2k—~886Automated safety check: PassMIT
Cmux Debugging Guidemanaflow-ai/cmux28k1 repos~1.1kAutomated safety check: PassCustom licence
Analyzing .NET Performancedotnet/skills5.6k3 repos~3.1kAutomated safety check: PassMIT

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Categories

Questions about Common Performance Engineering

What does Common Performance Engineering do?

Enforce universal standards for high-performance development. Common Performance Engineering is an agent skill from HoangNguyen0403/agent-skills-standard. Enforce universal standards for high-performance development.

When should I use Common Performance Engineering?

Common Performance Engineering fits situations like: profiling bottlenecks; reducing latency; fixing memory leaks; improving throughput.

How do I install Common Performance Engineering in Claude Code?

Run `npx skills add HoangNguyen0403/agent-skills-standard --skill common-performance-engineering -a claude-code`. Or copy the skill folder (skills/common/common-performance-engineering in HoangNguyen0403/agent-skills-standard) into .claude/skills/common-performance-engineering in your project. Claude Code loads it when a task matches its description.

How do I install Common Performance Engineering in Codex?

Run `npx skills add HoangNguyen0403/agent-skills-standard --skill common-performance-engineering -a codex`. Or copy the skill folder (skills/common/common-performance-engineering in HoangNguyen0403/agent-skills-standard) into .agents/skills/common-performance-engineering in your project. Codex loads it when a task matches its description.

Can I use Common Performance Engineering 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 HoangNguyen0403/agent-skills-standard --skill common-performance-engineering -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/common-performance-engineering, .gemini/skills/common-performance-engineering, .github/skills/common-performance-engineering and .opencode/skills/common-performance-engineering in your project.

What does Common Performance Engineering need to run?

SKILL.md names no scripts, command-line tools or credentials: Common Performance Engineering is instructions for the agent only.

Does Common Performance Engineering 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 Common Performance Engineering 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. Review the folder before installing.

What licence does Common Performance Engineering use?

Common Performance Engineering is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Common Performance Engineering use?

About 762 tokens (SKILL.md is roughly 3k 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 131 tokens, read only when the agent opens those files.

What are the alternatives to Common Performance Engineering?

Skills that share tags, products or a category with Common Performance Engineering: Code Review Checklist (shareAI-lab/learn-claude-code, 78k stars), LLM Torch Profiler Analysis (sgl-project/sglang, 37k stars), Py (crazyguitar/pysheeet, 8.2k stars) and Cmux Debugging Guide (manaflow-ai/cmux, 28k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Common Performance Engineering?

HoangNguyen0403 (a GitHub user) maintains it in HoangNguyen0403/agent-skills-standard, which has 572 GitHub stars. The repository holds 211 skills in this directory. The repository was last updated on October 9, 2026.

Source: HoangNguyen0403/agent-skills-standard on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.