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在 OpenClaw 中安装
/install model-resource-profiler
功能描述
Analyze model training or inference resource behavior from profiler artifacts, with focus on GPU memory (VRAM) and CPU hotspots. Uses JSON/JSON.GZ artifacts...
安全使用建议
This skill appears coherent and local-only: it analyzes JSON/JSON.GZ profiler artifacts with the bundled script and does not ask for credentials or remote installs. Before installing, review the full scripts/analyze_profile.py file (the provided SKILL.md and snippet are consistent but the shipped code should be inspected end-to-end) and only run conversions (the torch example) inside your own trusted environment. Do not provide pickle or other binary serialized artifacts to the skill; follow its guidance to re-export as JSON in your environment to avoid executing untrusted code. If you need higher assurance, run the analyzer in an isolated environment (air-gapped or with limited network) and inspect the generated markdown/JSON outputs for any unexpected content.
功能分析
Type: OpenClaw Skill
Name: model-resource-profiler
Version: 0.1.1
The skill bundle is benign. Both the `SKILL.md` and `scripts/analyze_profile.py` demonstrate a strong focus on security. The `SKILL.md` explicitly defines safety boundaries, instructing the AI agent to never deserialize pickle files, execute remote code, or access files beyond user-provided local paths. The Python script reinforces this by using safe `json.load` for parsing and explicitly raising a `SystemExit` if a `--memory-pickle` argument is provided, preventing unsafe deserialization. There is no evidence of data exfiltration, malicious execution, persistence, or prompt injection designed to bypass safety measures.
能力评估
Purpose & Capability
Name/description (model resource profiling focused on GPU memory and CPU hotspots) match the included analyzer script and SKILL.md. No extra binaries, credentials, or unrelated config paths are requested.
Instruction Scope
SKILL.md restricts analysis to user-provided local JSON/JSON.GZ artifacts, explicitly forbids pickle/executable deserialization, remote code fetching, or executing commands embedded in artifacts, and instructs use of the local scripts/analyze_profile.py implementation. Example conversion code is provided but is explicitly intended for the user's trusted environment.
Install Mechanism
No install spec; this is instruction-only with a bundled analyzer script. The script uses only standard Python libs (gzip, json, pathlib, collections, math) and does not pull remote artifacts or install packages.
Credentials
The skill requests no environment variables, credentials, or config paths. The SKILL.md guidance to re-export pickles in a trusted environment is appropriate and keeps credential exposure minimal.
Persistence & Privilege
always is false and the skill is user-invocable; it does not request permanent presence or elevated system privileges and does not indicate modifying other skills or agent-wide 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 model-resource-profiler - 安装完成后，直接呼叫该 Skill 的名称或使用
/model-resource-profiler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
v0.1.1
**Safer format: Now supports only JSON-based artifacts, disabling pickle support for improved security.**
- Only accepts profiler and memory snapshot artifacts in JSON/JSON.GZ; pickle input is no longer supported.
- Updated documentation and commands to reflect JSON-only usage.
- Added explicit workflow and safety instructions, including handling for users with legacy pickle files.
- Analysis logic remains unchanged; only data ingestion methods are different.
v0.1.0
Initial release of model-resource-profiler skill:
- Analyze PyTorch training/inference resources from memory snapshot pickles and profiler trace files.
- Automatically diagnoses GPU memory usage, fragmentation, and CPU bottlenecks.
- Provides actionable optimization suggestions, ranked by impact and confidence.
- Accepts both CPU and memory profiling data; produces markdown and JSON reports.
- Workflow includes artifact confirmation, analysis, rubric-based interpretation, and prioritized recommendations.
元数据
常见问题
Model Resource Profiler 是什么？
Analyze model training or inference resource behavior from profiler artifacts, with focus on GPU memory (VRAM) and CPU hotspots. Uses JSON/JSON.GZ artifacts... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 341 次。
如何安装 Model Resource Profiler？
在 OpenClaw 或 Claude Code 对话框中运行命令「/install model-resource-profiler」即可一键安装，无需额外配置。
Model Resource Profiler 是免费的吗？
是的，Model Resource Profiler 完全免费（开源免费），可自由下载、安装和使用。
Model Resource Profiler 支持哪些平台？
Model Resource Profiler 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 Model Resource Profiler？
由 daiwk（@daiwk）开发并维护，当前版本 v0.1.1。
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