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在 OpenClaw 中安装
/install parallel-tfidf-search-memory-optimization
功能描述
Optimize Python code for reduced memory usage and improved memory efficiency. Use when asked to reduce memory footprint, fix memory leaks, optimize data stru...
安全使用建议
This skill is an advisory guide — it contains safe, standard suggestions for reducing Python memory usage. Before using it: ensure the required Python libraries (numpy, pandas, pympler, memory_profiler, ijson, scipy, etc.) are installed from trusted package sources; run transformed code in a test environment (verify correctness and performance); be cautious about operating on sensitive files (processing large local files may expose sensitive data if output is shared); and review any automatic code changes the agent proposes before applying them. The skill itself doesn't request credentials or download code, but following its examples may require installing third‑party packages and reading local data files.
功能分析
Type: OpenClaw Skill
Name: parallel-tfidf-search-memory-optimization
Version: 0.1.0
The skill bundle provides legitimate educational content and code patterns for Python memory optimization, covering techniques such as `__slots__`, generators, memory-mapped files, and Pandas data type downcasting. The instructions in SKILL.md and references/advanced_techniques.md are strictly aligned with the stated purpose of improving memory efficiency and contain no evidence of malicious intent, data exfiltration, or prompt injection.
能力评估
Purpose & Capability
Name/description match the provided content: SKILL.md and references contain patterns, profiling commands, and transformations that are appropriate for reducing Python memory usage. Nothing in the files asks for access to unrelated services or credentials.
Instruction Scope
Instructions operate on local Python data and files (CSV, binary, arrays) and recommend standard profiling and transformation patterns. They reference third‑party Python libraries (numpy, pandas, pympler, memory_profiler, ijson, scipy) but the skill declares no install requirements — callers will need those libraries available to run examples. The document does not instruct reading unrelated system secrets or exfiltrating data.
Install Mechanism
No install spec and no code files — instruction-only skill. This minimizes installation risk because nothing is downloaded or written by the skill itself.
Credentials
No environment variables, credentials, or config paths are requested. The recommended techniques do not require secret access and the skill does not attempt to access unrelated system configuration.
Persistence & Privilege
always is false and the skill does not request persistent or elevated platform privileges. It does not modify other skills or system-wide agent settings.
如何使用
- 确保已安装 OpenClaw（本地或 Docker 部署）
- 在对话框中输入安装命令：
/install parallel-tfidf-search-memory-optimization - 安装完成后，直接呼叫该 Skill 的名称或使用
/parallel-tfidf-search-memory-optimization触发 - 根据 Skill 的参数说明提供必要输入，即可获得结构化输出
版本历史
v0.1.0
Bulk publish from all-task-skills-dedup
元数据
常见问题
memory-optimization 是什么？
Optimize Python code for reduced memory usage and improved memory efficiency. Use when asked to reduce memory footprint, fix memory leaks, optimize data stru... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 86 次。
如何安装 memory-optimization？
在 OpenClaw 或 Claude Code 对话框中运行命令「/install parallel-tfidf-search-memory-optimization」即可一键安装，无需额外配置。
memory-optimization 是免费的吗？
是的，memory-optimization 完全免费，采用 MIT-0 许可证，可自由下载、安装和使用。
memory-optimization 支持哪些平台？
memory-optimization 跨平台运行，可在任意部署了 OpenClaw / Claude Code 的环境中使用（cross-platform）。
谁开发了 memory-optimization？
由 lnj22（@lnj22）开发并维护，当前版本 v0.1.0。
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