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Agent Skill · 运维

environment-security(环境安全)

用于辅助安全审计、权限检查、凭据风险、认证流程和常见漏洞排查。它适合让 Agent 梳理敏感配置、检查依赖风险、分析鉴权逻辑或生成安全复核清单。使用时不能把工具输出直接当最终结论,涉及密钥、令牌、用户数据或生产系统时,应先确认最小权限、脱敏方式和操作边界。

environment-security 是运维类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为执行命令;许可证为 MIT-0,第三方扫描有提醒项,按安装量在运维类的 954 个 Skill 里排第 523。

总安装 6,521周安装 269GitHub Stars 1下载 2,130核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.0.0

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:environment-security(环境安全)
来源仓库:https://github.com/zshtolors/environment-security
安装命令:
openclaw skills install environment-security
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install environment-security

这个 Skill 是做什么的

运维类 Skill 处理部署、监控、日志和云资源,多数需要较高的执行权限。

验证本地环境事实,辅助安全审计与凭据风险排查。适用于命令执行前的环境真实性检查与权限边界确认。创建或刷新安全上下文,防止越权操作与敏感数据暴露。使用时不能直接采信工具输出,需人工复核关键操作。建议在生产环境启用前进行最小权限测试。

environment-security 属于运维类 Skill,可作为该场景下的辅助能力补充。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install environment-security;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要执行命令,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

environment-security 怎么安装?

在终端执行 openclaw skills install environment-security。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

environment-security 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

environment-security 需要什么权限?

权限级别是执行命令。安装流程涉及命令执行,可能通过 openclaw skills install environment-security 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

environment-security 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

environment-security 用的人多吗?

公开数据是累计安装 6,521 次,近一周安装 269 次,下载 2,130 次,GitHub 1 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

environment-security 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。

先想清楚的地方

  • 需要执行命令,装到常用环境前要先评估影响面。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理运维类任务、又不想每次都手写提示词的人。

不太适合

  • 不能接受 Skill 在你机器上执行命令的人。
  • 需要厂商级技术支持和 SLA 的生产系统。

运维类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
docker-essentials

Docker 基础 · OpenClaw

1,005,15130接触敏感数据MIT-0通过
playwright-scraper-skill

Playwright scraper 技能 · OpenClaw

923,55754接触敏感数据MIT-0有提醒
moltguard

moltguard 搜索 · OpenClaw

825,096110接触敏感数据MIT-0通过
finance-news

财经新闻 · OpenClaw

275,51210需要联网MIT-0有提醒
security-monitor

安全监视器 · OpenClaw

224,1695需要联网MIT-0有提醒
environment-security

环境安全 · 本页

6,5211执行命令MIT-0有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
environment-doc-author
description
Verify real local environment facts before an agent uses machine-specific commands, runtimes, compilers, services, or startup scripts, then create or refresh environment baseline JSON and related environment policy, AGENTS, or skill documents from those verified facts.

Environment Doc Author

Overview

Use this skill to turn the current machine state into reusable environment guardrails. Probe the machine first, keep a machine-readable baseline JSON, and render human-readable policy fragments from verified facts only.

Use it not only when you need to generate or refresh environment documents, but also when an agent task depends on local commands, local runtimes, local package managers, local services, startup scripts, or machine-specific install roots.

This skill supports two standalone implementations:

  • JavaScript

- scripts/detect_environment.js - scripts/render_environment_docs.js - Pure Node.js.

  • Python

- scripts/detect_environment.py - scripts/render_environment_docs.py - Requires Python 3.7+.

When To Use

  • The task needs real local commands such as java, mvn, node, npm, python, go, cargo, docker, kubectl, dotnet, bundle, composer, or similar tools.
  • The agent needs to decide which local executable, runtime manager, install root, env var, or startup script is actually present on this machine.
  • The repository needs to create or refresh environment-baseline.json, ENVIRONMENT_POLICY.md, AGENTS.environment.md, AGENTS.md environment sections, or reusable environment skill snippets.
  • A baseline already exists but may be stale after local toolchain changes, machine migration, shell profile updates, or package manager changes.
  • If the repository already has an AGENTS.md, default to writing a separate environment snippet such as AGENTS.environment.md instead of overwriting the existing file.

Input Constraints

  • Treat the current machine as the authority. Existing docs are hints, not proof.
  • Do not invent tools, versions, install roots, symlink targets, env vars, services, ports, package managers, or PATH entries.
  • If the task depends on a machine-specific command and the baseline does not already verify it, probe first.
  • If the user gives a path or command, verify that it exists before treating it as fact.
  • Distinguish a launcher, shim, alias, wrapper, symlink, .cmd/.bat script, and the real install directory.
  • If evidence is missing, say it is unverified. Do not fill the gap with guesses.
  • Do not overwrite an existing AGENTS.md unless the user explicitly asks for that file to be updated.
  • If a command would install, upgrade, uninstall, enable, or reconfigure software, stop and obtain explicit approval first.

Execution Flow

1. Read the existing baseline before probing

  • If a machine-level policy already exists, read it first and treat it as the current baseline.
  • Prefer a machine-readable JSON baseline when available. Recommended names:

- environment-baseline.json - ENVIRONMENT_POLICY.md - AGENTS.md or a dedicated environment snippet

  • If your team or machine already has a master environment policy, treat it as the primary source of truth before probing anything else.

2. Create the initial baseline when none exists

  • Run one implementation against the current machine and save the result as JSON.
  • If the immediate task only depends on a small set of local tools, you may still start with a targeted probe, but keep the baseline JSON authoritative once facts are verified.
  • Render the JSON into the human-readable docs that the repository or agent stack needs.
  • When an AGENTS.md already exists, write a separate environment snippet by default and keep merge work explicit and local to the relevant section.
  • Keep the JSON and human docs in sync. Do not maintain only one of them.
  • Both implementations auto-detect the current machine language and generate Chinese or English docs. If the language cannot be recognized, they default to English.

JavaScript:

node scripts/detect_environment.js --output ./environment-baseline.json
node scripts/render_environment_docs.js ./environment-baseline.json \
  --policy-out ./ENVIRONMENT_POLICY.md \
  --agents-out ./AGENTS.environment.md \
  --skill-snippet-out ./SKILL.environment.md

Python:

python3 scripts/detect_environment.py --output ./environment-baseline.json
python3 scripts/render_environment_docs.py ./environment-baseline.json \
  --policy-out ./ENVIRONMENT_POLICY.md \
  --agents-out ./AGENTS.environment.md \
  --skill-snippet-out ./SKILL.environment.md

Windows note for Python:

  • Do not assume the default python command is new enough.
  • Verify that the selected interpreter is Python 3.7+ before running the Python entry points.

3. Use targeted verification when a baseline already exists

  • Do not re-probe everything by default when only one tool or task changed.
  • If a task mentions a tool not covered by the current baseline, probe that tool first, then update the docs.
  • If the current machine state does not match the baseline, record the verified change and refresh both JSON and human docs.
  • When the task is "run a local command safely", use the baseline plus a fresh targeted probe to confirm the exact executable, version, and relevant env vars before choosing the command.

JavaScript:

node scripts/detect_environment.js \
  --baseline ./environment-baseline.json \
  --tools java,maven \
  --output ./environment-baseline.json

node scripts/render_environment_docs.js ./environment-baseline.json \
  --policy-out ./ENVIRONMENT_POLICY.md \
  --agents-out ./AGENTS.environment.md

Python:

python3 scripts/detect_environment.py \
  --baseline ./environment-baseline.json \
  --tools java,maven \
  --output ./environment-baseline.json

python3 scripts/render_environment_docs.py ./environment-baseline.json \
  --policy-out ./ENVIRONMENT_POLICY.md \
  --agents-out ./AGENTS.environment.md

4. Extend the probe set only when the task requires it

  • The built-in detector covers common local tools and core env vars:

- Git - Java - javac - Maven - Gradle - Node - npm - pnpm - Yarn - Python - Go - Rust (rustc, Cargo, Rustup) - Clang, Clang++, GCC, G++, CMake, Make, Ninja - Ruby, RubyGems, Bundler - PHP, Composer - .NET SDK - Docker - kubectl - JAVA_HOME - MAVEN_HOME - GRADLE_HOME, GOROOT, GOPATH, CARGO_HOME, RUSTUP_HOME, DOTNET_ROOT, CC, CXX - common runtime managers such as NVM_DIR, PYENV_ROOT, RBENV_ROOT, SDKMAN_DIR, HOMEBREW_PREFIX, VIRTUAL_ENV - important PATH entries

  • For services, databases, startup scripts, or project-specific tooling, add an extra probe file and run a targeted scan.
  • Read probe-file.md only when you need extra probes.

5. Apply the hard rules consistently

  • Do not invent tools, versions, install roots, services, or env vars.
  • Verify executables with absolute paths and version commands whenever possible.
  • Distinguish shims, wrappers, symlinks, aliases, and launchers from real install directories.
  • Prefer facts captured in environment-baseline.json plus fresh verification over memory or generic platform assumptions.
  • If a tool is missing:

1. Tell the user which tool is missing. 2. Explain why it is needed. 3. Ask for the executable path or install directory after installation. 4. Prefer user-managed installation. 5. Only install it yourself if the user explicitly requests and authorizes that action. 6. Update both the JSON baseline and the human docs after verification.

  • If a command would modify the machine environment, stop and obtain explicit approval first.

Output Contract

  • Keep these roles distinct:

- environment-baseline.json: machine-readable source of truth for detected facts - ENVIRONMENT_POLICY.md: human-readable policy and approved environment inventory - AGENTS.environment.md or AGENTS.md snippet: hard rules for agents - SKILL.environment.md snippet: environment section that can be reused in other skills

  • Read document-contracts.md when you need the exact document shape, language behavior, or update checklist.

Resources

scripts/detect_environment.js

Standalone Node.js detector. Probes the current machine, merges with an existing baseline when requested, and emits normalized JSON.

scripts/render_environment_docs.js

Standalone Node.js renderer. Generates localized ENVIRONMENT_POLICY.md, AGENTS snippets, and skill snippets from the baseline JSON.

scripts/detect_environment.py

Python detector. Probes the current machine, merges with an existing baseline when requested, and emits normalized JSON.

scripts/render_environment_docs.py

Python renderer. Generates localized ENVIRONMENT_POLICY.md, AGENTS snippets, and skill snippets from the baseline JSON.

references/document-contracts.md

Use when you need the exact structure and maintenance rules for the generated docs.

references/probe-file.md

Use when you need to extend the default detector for services, databases, compilers, startup scripts, or project-specific tools.

平台分布

OpenClaw

94.73%
按下载量换算2,018

安全审计

VirusTotal

可疑

ClawScan

通过

Static analysis

可疑

权限和风险

执行命令

安装流程涉及命令执行,可能通过 openclaw skills install environment-security 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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