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Nm Parseltongue Python Performance

by athola · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ✓ Security Clean
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Install in OpenClaw
/install nm-parseltongue-python-performance
Description
Python performance profiling and optimization: bottleneck detection, memory tuning, benchmarking
README (SKILL.md)

Night Market Skill — ported from claude-night-market/parseltongue. For the full experience with agents, hooks, and commands, install the Claude Code plugin.

Python Performance Optimization

Profiling and optimization patterns for Python code.

Table of Contents

  1. Quick Start

Quick Start

# Basic timing
import timeit
time = timeit.timeit("sum(range(1000000))", number=100)
print(f"Average: {time/100:.6f}s")

Verification: Run the command with --help flag to verify availability.

When To Use

  • Identifying performance bottlenecks
  • Reducing application latency
  • Optimizing CPU-intensive operations
  • Reducing memory consumption
  • Profiling production applications
  • Improving database query performance

When NOT To Use

  • Async concurrency - use python-async instead
  • CPU/GPU system monitoring - use conservation:cpu-gpu-performance
  • Async concurrency - use python-async instead
  • CPU/GPU system monitoring - use conservation:cpu-gpu-performance

Modules

This skill is organized into focused modules for progressive loading:

profiling-tools

CPU profiling with cProfile, line profiling, memory profiling, and production profiling with py-spy. Essential for identifying where your code spends time and memory.

optimization-patterns

Ten proven optimization patterns including list comprehensions, generators, caching, string concatenation, data structures, NumPy, multiprocessing, and database operations.

memory-management

Memory optimization techniques including leak tracking with tracemalloc and weak references for caches. Depends on profiling-tools.

benchmarking-tools

Benchmarking tools including custom decorators and pytest-benchmark for verifying performance improvements.

best-practices

Best practices, common pitfalls, and exit criteria for performance optimization work. Synthesizes guidance from profiling-tools and optimization-patterns.

Exit Criteria

  • Profiled code to identify bottlenecks
  • Applied appropriate optimization patterns
  • Verified improvements with benchmarks
  • Memory usage acceptable
  • No performance regressions

Troubleshooting

Common Issues

Command not found Ensure all dependencies are installed and in PATH

Permission errors Check file permissions and run with appropriate privileges

Unexpected behavior Enable verbose logging with --verbose flag

Usage Guidance
This skill is an instructional collection of profiling and optimization patterns and appears internally consistent. Before using: (1) be aware that following the guide may lead you to pip-install third-party packages—only install packages you trust from PyPI; (2) tools like py-spy require permissions to inspect running processes, so avoid profiling sensitive production workloads without authorization; (3) the snippets are examples, not production-ready code—review and adapt them for your environment. Overall this looks safe and appropriate for its stated purpose.
Capability Analysis
Type: OpenClaw Skill Name: nm-parseltongue-python-performance Version: 1.0.0 The skill bundle provides standard Python performance profiling and optimization techniques, including code snippets for cProfile, tracemalloc, NumPy, and multiprocessing. All content across SKILL.md and the module files is educational and aligned with the stated purpose of performance tuning, with no evidence of malicious intent, data exfiltration, or harmful prompt injection.
Capability Assessment
Purpose & Capability
Name, description, and included module files all focus on Python profiling, memory tuning, benchmarking, and optimization patterns. There are no unrelated credential or binary requirements listed.
Instruction Scope
SKILL.md and module files provide code snippets and guidance for cProfile, tracemalloc, py-spy, memory-profiler, line-profiler, pytest-benchmark, etc. Instructions do not ask the agent to read unrelated files, exfiltrate data, or contact external endpoints; suggested commands are limited to profiling/benchmarking.
Install Mechanism
This is instruction-only with no install spec. The modules suggest installing common pip packages (e.g., memory-profiler, py-spy, pytest-benchmark), which is proportionate and expected for a profiling guide.
Credentials
No environment variables, credentials, or config paths are requested. The guidance does not reference accessing secrets or unrelated system configuration.
Persistence & Privilege
Skill is not always-enabled and does not request elevated persistence. Normal autonomous invocation is allowed (platform default) and appropriate for a utility skill; there is no indication it modifies other skills or system-wide settings.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install nm-parseltongue-python-performance
  3. After installation, invoke the skill by name or use /nm-parseltongue-python-performance
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
Python performance profiling and optimization tools are now available as a skill. - Detect and address code bottlenecks, reduce latency, and optimize memory usage. - Includes modules for CPU/memory profiling, benchmarking, and practical optimization techniques. - Module structure enables progressive learning, with best practices and troubleshooting guidance. - See the [project homepage](https://github.com/athola/claude-night-market/tree/master/plugins/parseltongue) for more info.
Metadata
Slug nm-parseltongue-python-performance
Version 1.0.0
License MIT-0
All-time Installs 1
Active Installs 1
Total Versions 1
Frequently Asked Questions

What is Nm Parseltongue Python Performance?

Python performance profiling and optimization: bottleneck detection, memory tuning, benchmarking. It is an AI Agent Skill for Claude Code / OpenClaw, with 88 downloads so far.

How do I install Nm Parseltongue Python Performance?

Run "/install nm-parseltongue-python-performance" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is Nm Parseltongue Python Performance free?

Yes, Nm Parseltongue Python Performance is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does Nm Parseltongue Python Performance support?

Nm Parseltongue Python Performance is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created Nm Parseltongue Python Performance?

It is built and maintained by athola (@athola); the current version is v1.0.0.

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