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environment-security环境安全

Agent Skill

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。

总安装

6,521

周安装

269

GitHub Stars

1

下载量

2,130
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

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

简介

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。

SKILL.md

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.

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

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 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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