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在 OpenClaw 中安装
/install glm-lake-mendota-glm-basics
功能描述
Basic usage of the General Lake Model (GLM) for lake temperature simulation. Use when you need to run GLM, understand input files, or modify configuration pa...
使用说明 (SKILL.md)
GLM Basics Guide
Overview
GLM (General Lake Model) is a 1D hydrodynamic model that simulates vertical temperature and mixing dynamics in lakes. It reads configuration from a namelist file and produces NetCDF output.
Running GLM
cd /root
glm
GLM reads glm3.nml in the current directory and produces output in output/output.nc.
Input File Structure
| File | Description |
|---|---|
glm3.nml |
Main configuration file (Fortran namelist format) |
bcs/*.csv |
Boundary condition files (meteorology, inflows, outflows) |
Configuration File Format
glm3.nml uses Fortran namelist format with multiple sections:
&glm_setup
sim_name = 'LakeName'
max_layers = 500
/
&light
Kw = 0.3
/
&mixing
coef_mix_hyp = 0.5
/
&meteorology
meteo_fl = 'bcs/meteo.csv'
wind_factor = 1
lw_factor = 1
ch = 0.0013
/
&inflow
inflow_fl = 'bcs/inflow1.csv','bcs/inflow2.csv'
/
&outflow
outflow_fl = 'bcs/outflow.csv'
/
Modifying Parameters with Python
import re
def modify_nml(nml_path, params):
with open(nml_path, 'r') as f:
content = f.read()
for param, value in params.items():
pattern = rf"({param}\s*=\s*)[\d\.\-e]+"
replacement = rf"\g\x3C1>{value}"
content = re.sub(pattern, replacement, content)
with open(nml_path, 'w') as f:
f.write(content)
# Example usage
modify_nml('glm3.nml', {'Kw': 0.25, 'wind_factor': 0.9})
Common Issues
| Issue | Cause | Solution |
|---|---|---|
| GLM fails to start | Missing input files | Check bcs/ directory |
| No output generated | Invalid nml syntax | Check namelist format |
| Simulation crashes | Unrealistic parameters | Use values within valid ranges |
Best Practices
- Always backup
glm3.nmlbefore modifying - Run GLM after each parameter change to verify it works
- Check
output/directory for results after each run
安全使用建议
This skill appears to be a simple GLM usage guide, but proceed cautiously. Key points to consider before installing/using:
- The SKILL.md tells the agent to cd /root — change that to the working/project directory (or remove it) unless you intentionally want to operate in /root; running in /root can expose unrelated files and assumes elevated environment.
- The provided Python namelist editor uses a naive regex that may modify unintended numeric tokens and could corrupt glm3.nml. Prefer using a proper Fortran namelist parser (e.g., f90nml or a library) or ensure strong, parameter-scoped replacements and always keep backups.
- Because there's no install information, verify that the glm binary on your system is the official/trusted GLM distribution before running it. If glm is missing, obtain it from the official project/release page rather than installing from an unknown source.
- Before running automated edits, back up glm3.nml and review diffs; avoid running the skill with access to system directories you don't want modified.
If the maintainers can (a) remove or explain the /root directive, (b) replace the regex approach with a proper namelist parser, and (c) include guidance or a reference to the official GLM distribution, my concerns would be largely resolved.
功能分析
Type: OpenClaw Skill
Name: glm-lake-mendota-glm-basics
Version: 0.1.0
The skill bundle provides documentation and a Python utility for managing the General Lake Model (GLM). The content in SKILL.md is consistent with its stated purpose of lake temperature simulation, and the provided code snippet for modifying configuration files via regex contains no malicious logic or data exfiltration attempts.
能力评估
Purpose & Capability
Name/description match the instructions: running GLM, editing glm3.nml, and reading bcs/*.csv are expected. However, the SKILL.md instructs cd /root which is not necessary for running GLM and is an odd choice that exposes or assumes access to a privileged user directory.
Instruction Scope
Instructions tell the agent to change into /root and run the glm binary and to edit glm3.nml in-place. The /root path is unrelated to the stated purpose and may cause the agent to read or write files in a system user home. The provided Python modifier uses a simplistic regex that can accidentally change unintended numeric tokens in the namelist (risk of corrupting config).
Install Mechanism
There is no install spec (instruction-only), so nothing will be downloaded or written to disk by the skill itself. That is low-risk, but it also means the guide assumes the glm binary is already present; the SKILL.md does not tell the user where to obtain a trusted glm binary.
Credentials
The skill requests no environment variables, credentials, or config paths. That is proportionate for a local GLM usage guide.
Persistence & Privilege
always is false and there is no install step that persists files or modifies agent/system configuration. The skill does not request elevated platform privileges in its metadata.
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 glm-lake-mendota-glm-basics - 安装完成后，直接呼叫该 Skill 的名称或使用
/glm-lake-mendota-glm-basics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
glm-basics 是什么？
Basic usage of the General Lake Model (GLM) for lake temperature simulation. Use when you need to run GLM, understand input files, or modify configuration pa... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 76 次。
如何安装 glm-basics？
在 OpenClaw 或 Claude Code 对话框中运行命令「/install glm-lake-mendota-glm-basics」即可一键安装，无需额外配置。
glm-basics 是免费的吗？
是的，glm-basics 完全免费，采用 MIT-0 许可证，可自由下载、安装和使用。
glm-basics 支持哪些平台？
glm-basics 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 glm-basics？
由 wu-uk（@wu-uk）开发并维护，当前版本 v0.1.0。
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