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Agent Setup Survey

作者 Agent-Deployments · GitHub ↗ · v1.0.0
cross-platform ✓ 安全检测通过
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在 OpenClaw 中安装
/install agent-setup-survey
功能描述
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...
使用说明 (SKILL.md)

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": "\x3Cfrom_survey>",
  "container": { "is_container": true/false, "type": "\x3Cfrom_survey>" },
  "vm": { "is_vm": true/false, "type": "\x3Cfrom_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": ["\x3Cskill_name1>", "\x3Cskill_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

安全使用建议
This skill appears to do what it says: run a local detection script and (with your approval) POST non-PII environment signals to a research endpoint. Before using it: (1) run the script and carefully review the JSON it prints — do not proceed until you are comfortable with every field; (2) be cautious about submitting installed skill names or giving a precise User-Agent string, as those can make your agent more identifiable; (3) verify the research endpoint (https://internetwarte.eu) and decide whether you trust it — if unsure, save the payload locally and upload it manually later; (4) note that network submission will reveal your IP to the receiver (they claim to anonymize to /24). If you want stricter privacy, decline to share skill names and avoid customizing the User-Agent.
功能分析
Type: OpenClaw Skill Name: agent-setup-survey Version: 1.0.0 The skill `agent-setup-survey` is designed to detect and report the AI agent's execution environment to an open research survey at `https://internetwarte.eu/agentsetup`. The `SKILL.md` clearly outlines the purpose, the data collected (system environment details, user-opt-in skill names), and the external endpoint for submission. The `scripts/environment-survey.py` script performs system introspection (e.g., checking `/proc`, `/sys`, running `systemd-detect-virt`, `ioreg`, `powershell`) to gather environment data, but it explicitly avoids collecting personally identifying information or raw sensitive strings, instead extracting boolean flags or normalized types. The instructions to the agent are transparent, requiring the agent to show the collected data to the user before submission and to ask for user consent for sharing skill names. There is no evidence of malicious intent, unauthorized data exfiltration, persistence mechanisms, or harmful prompt injection attempts against the agent; all actions align with the stated research survey purpose.
能力评估
Purpose & Capability
Name/description match the observed behavior: the included Python script inspects local OS files, /proc, DMI/sysfs on Linux, runs standard detection commands (systemd-detect-virt, virt-what, sysctl, ioreg, PowerShell queries) and emits a small JSON describing os/arch/container/vm signals. Those operations are expected and proportionate for an environment-detection survey.
Instruction Scope
SKILL.md instructs the agent to run the local script, show results to the user, optionally collect user-approved skill-name metadata, and POST the JSON to https://internetwarte.eu/submit with a custom User-Agent. That network submission is explicit and outside the local system; the user is supposed to review data before sending. Note: the instructions ask the agent to set the User-Agent header (which may include agent name/model) and optionally submit installed skill names — both can increase identifiability.
Install Mechanism
Instruction-only skill with no install spec and only a small Python script included. No downloads, package installs, or archive extraction. Risk from install mechanism is low.
Credentials
The skill requests no environment variables or credentials. The script reads local system files (/proc, /sys/class/dmi, /.dockerenv, /run/.containerenv) and may invoke system utilities — this is consistent with detection goals. However the submission step transmits: the detection booleans, OS/arch, and optionally user-supplied skill names and a User-Agent header; those elements plus the submitting IP (they state it is /24-anonymized) can deanonymize or fingerprint an agent. User consent is required and documented in SKILL.md, but users should be aware of this.
Persistence & Privilege
The skill is not always-enabled, does not request system-wide config changes or credentials, and does not attempt to persist itself. It only creates an output file (suggested outbox) and suggests saving failed payloads locally; this is within expected scope.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install agent-setup-survey
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /agent-setup-survey 触发
  4. 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
v1.0.0
- Initial release of agent-setup-survey. - Detects AI agent execution environment (OS, architecture, container/VM presence, detection signals). - Optionally collects anonymized metadata about installed agent skills with user consent. - Shows collected data for review before submitting anonymously to an open research survey. - Submits via HTTPS, saving data locally on network failure for later upload. - No personally identifying information collected or transmitted.
元数据
Slug agent-setup-survey
版本 1.0.0
许可证
累计安装 0
当前安装数 0
历史版本数 1
常见问题

Agent Setup Survey 是什么?

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... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 338 次。

如何安装 Agent Setup Survey?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install agent-setup-survey」即可一键安装,无需额外配置。

Agent Setup Survey 是免费的吗?

是的,Agent Setup Survey 完全免费(开源免费),可自由下载、安装和使用。

Agent Setup Survey 支持哪些平台?

Agent Setup Survey 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 Agent Setup Survey?

由 Agent-Deployments(@agent-deployments)开发并维护,当前版本 v1.0.0。

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