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在 OpenClaw 中安装
/install performance-mastery
功能描述
全栈性能工程师 — Linux 系统与编程语言性能分析调优专家。覆盖 CPU、内存、磁盘 I/O、网络、内核参数、编译优化、eBPF 追踪、基准测试、容器/K8s。触发场景： (1) 系统变慢/卡顿/负载高/load average 高 (2) 内存不足/OOM Kill/Swap 高/内存泄漏 (3) CPU...
安全使用建议
This skill appears to be a legitimate performance troubleshooting toolkit, but review before running: 1) Inspect scripts locally (collect_snapshot.sh, perf_monitor.sh, bench-compare.sh) so you understand what system files they read and what commands they run. They intentionally read kernel logs and /proc and may require root for some checks. 2) Be cautious with example commands that echo into /etc/sysctl.d or write to /sys: these make persistent system-wide changes — test in staging and back up configs before applying. 3) The repository contains scripts/run-evals.py which can call an OpenAI-compatible API if you provide OPENAI_API_KEY / OPENAI_BASE_URL; the skill manifest did not declare any required credentials. If you do not intend to send diagnostic data externally, do not run run-evals.py with an API key and/or remove that script. 4) Avoid running any of these scripts unattended or under an autonomous agent with network access and secrets available; require explicit human confirmation before executing privileged or networked operations. 5) If you will use the eval/LLM integration, audit the data sent and consider sanitizing snapshots to avoid leaking sensitive information.
能力评估
Purpose & Capability
The name/description and the included scripts (collect_snapshot.sh, perf_monitor.sh, bench-compare.sh, python-perf-test.py) align with a Linux performance-engineering skill: they collect /proc/sys data, run diagnostics, and offer tuning commands. This is expected for the stated purpose. One mismatch: scripts/run-evals.py is an LLM evaluation runner (calls OpenAI-compatible APIs) which is not mentioned in the skill manifest's requirements (no env vars declared). That file is plausibly for test/eval purposes, but its presence is unexpected relative to the declared manifest.
Instruction Scope
SKILL.md instructs the agent/user to run local collection and monitoring scripts that read kernel logs, /proc, sysctl, dmesg, and other system state — all reasonable for performance analysis. The guidance also includes example one-line commands that persist changes (echo into /etc/sysctl.d, write to /sys/kernel/mm/... etc.). Those are coherent with tuning tasks but are high-impact (require root and modify system-wide configuration). The repo also contains an eval script that can send prompts/data to external LLM endpoints if run with an API key — SKILL.md does not explicitly instruct uploading snapshots to external services, so this external-call capability is an unexpected side-channel.
Install Mechanism
No install spec; this is an instruction-plus-scripts skill with no remote download or archive extraction. Files are local and nothing in the manifest attempts to fetch arbitrary code at install time — lower install risk.
Credentials
The declared requirements list no environment variables or primary credential, yet scripts/run-evals.py references OPENAI_API_KEY, OPENAI_BASE_URL, and EVAL_MODEL (and comments show pip deps like openai/pyyaml). That is an undeclared credential requirement which is surprising. Aside from that, the scripts use standard runtime environment variables (TMPDIR) and require typical system tools; they do not otherwise request unrelated cloud or secret credentials. The undeclared OpenAI API usage is the main proportionality mismatch.
Persistence & Privilege
always:false and no automatic model-disable flags — no forced-install privilege. However the content includes explicit example commands that persist kernel/sysctl changes and advice that writes to /etc/sysctl.d and other system paths; these require root and can change system behavior permanently. The skill itself does not declare it will autonomously make such changes, but a user or an automated agent running the provided commands/script could perform privileged modifications.
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 performance-mastery - 安装完成后，直接呼叫该 Skill 的名称或使用
/performance-mastery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
v3.8.1
No changes detected in this version.
- update skill.md
v3.8.0
Initial release of Performance Mastery, a comprehensive full-stack Linux and application performance engineering skill.
- Integrates system diagnosis, root cause analysis, optimization implementation, benchmarking, and rollback for Linux environments and popular programming languages (Go, Python, Java, Rust, C/C++, Node.js).
- Provides step-by-step methodology, quick diagnostic decision tree, and clear bottleneck identification tables (system and language layers).
- Includes ready-to-use scripts and manual commands for baseline collection, common problem diagnosis, and fast fixes for CPU, memory, disk I/O, networking, and kernel parameters.
- Deliverable analysis report and best practices template for documenting and tracking performance actions.
- Covers container/K8s, eBPF tracing, compilation tuning, benchmarking, and language-specific profiling and optimization techniques.
元数据
常见问题
performance-mastery 是什么？
全栈性能工程师 — Linux 系统与编程语言性能分析调优专家。覆盖 CPU、内存、磁盘 I/O、网络、内核参数、编译优化、eBPF 追踪、基准测试、容器/K8s。触发场景： (1) 系统变慢/卡顿/负载高/load average 高 (2) 内存不足/OOM Kill/Swap 高/内存泄漏 (3) CPU... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 109 次。
如何安装 performance-mastery？
在 OpenClaw 或 Claude Code 对话框中运行命令「/install performance-mastery」即可一键安装，无需额外配置。
performance-mastery 是免费的吗？
是的，performance-mastery 完全免费，采用 MIT-0 许可证，可自由下载、安装和使用。
performance-mastery 支持哪些平台？
performance-mastery 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 performance-mastery？
由 joeytao（@husttsq）开发并维护，当前版本 v3.8.1。
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