Agent Setup Survey

运行本地 Python 脚本检测操作系统、架构、容器与虚拟机启发式信号，向用户展示 JSON 并询问可选技能数量与名称，随后提交至外部研究调查；失败时可保存负载到本地 outbox。该 Skill 只上报环境信息，不修改环境。

agent-deployments

热度 82.01

最近更新 2026-05-11 17:27:47

分类：自动化依赖：代码执行 / 读写文件 / 第三方 API应用场景：自动化脚本 / 研究 / 报告撰写

安装技能

复制下面的话术发给你的 Agent，它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill： npx secure-skills-cli add agent-setup-survey

Metadata

name: agent-setup-survey
description: Detect and report the AI agent execution environment (sandboxed or bare metal and optionally installed agent skills) to an open research survey. Use when the user wants to contribute anonymous telemetry about their agent's runtime environment to the research project at `https://internetwarte.eu/agentsetup`.
compatibility: Requires Python 3. One time network access needed for live submission (DNS resolution and POST request); results can be saved locally on failure.
metadata: {"author":"internetwarte","version":"1.0"}

Agent Environment Survey

Overview

This skill runs a local detection script, shows the user the collected data, then submits it to the open research survey at internetwarte.eu/agentsetup. No personally identifying information is collected or transmitted.

What is collected

Field	Description
`os`	OS family: `linux` / `windows` / `macos` / `other`
`arch`	CPU architecture (e.g. `x86_64`, `arm64`)
`container.is_container`	Whether a container runtime was detected
`container.type`	`docker` / `podman` / `lxc` / `none` / `hint`
`vm.is_vm`	Whether a hypervisor was detected
`vm.type`	`kvm` / `vmware` / `virtualbox` / `none` / `hint`
`signals.*`	Five boolean detection indicators (see Privacy section)

The backend additionally records the submitting IP address anyonmized on a /24 network level. The User-Agent header is set by the agent to identify itself (e.g. Claude Code, OpenAI Codex, or others) + Model version (sonnet 4.6, gpt-5.3, or others) used that was able to submit the survey.

Steps

1. Run detection

RESULT_FILE="$(mktemp outbox/agent-setup-survey-result.XXXXXX.json)"
python3 scripts/environment-survey.py > "$RESULT_FILE"

Show the JSON output to the user, ask about metadata before submitting.

2. Collect skill metadata

Ask the user if he desires to share more information about installed skills:

"How many skills does your agent have available?" (0, 1-10, >10) - Default 0
"Which skill names would you like to share?" (prompt user with comma-separated list of skills - exclude the survey skill, let user decide which ones to submit)

3. Submit

POST the payload with the metadata extension as JSON to https://internetwarte.eu/submit. Set User-Agent to the name of the executing agent (e.g. Claude Code, OpenAI Codex, Gemini CLI, ...).

Default Payload:

{
  "os": "linux/windows/macOS",
  "arch": "<from_survey>",
  "container": { "is_container": true/false, "type": "<from_survey>" },
  "vm": { "is_vm": true/false, "type": "<from_survey>" },
  "signals": {
    "has_dockerenv": true/false,
    "has_containerenv": true/false,
    "cgroup_hints": true/false,
    "cpuinfo_hypervisor_flag": true/false,
    "dmi_vm_hints": true/false
  }
 "skill_disclosure": {
    "skills_added": "1-10",
    "skills_names": ["<skill_name1>", "<skill_name2>"]
}

On failure, save the payload to outbox/ for manual upload.

Privacy disclosure

Signals collected:

has_dockerenv - /.dockerenv file present
has_containerenv - /run/.containerenv file present
cgroup_hints - cgroup paths mention docker/kubepods/lxc/…
cpuinfo_hypervisor_flag - /proc/cpuinfo contains hypervisor
dmi_vm_hints - DMI strings match VM vendor keywords (raw strings are NOT sent)

View results

Dashboard: https://internetwarte.eu/agentsetup