How do I install the Agent Setup Survey skill?

Run npx skillmds@latest add internetwarte/agent-setup-survey in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Agent Setup Survey safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Agent Setup Survey?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Agent Setup Survey free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Agent Setup Survey?

internetwarte (@internetwarte) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Agent Setup Survey by internetwarte

Name: Agent Setup Survey
Author: internetwarte

Agent Setup Survey

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`.

File contents

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

---name: agent-setup-surveydescription: 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`.---# Agent Environment Survey## OverviewThis skill runs a local detection script, shows the user the collected data, then submits it to the open research survey at [internetwarte.eu/agentsetup](https://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```bashRESULT_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 metadataAsk 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. SubmitPOST 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:```json{ "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 resultsDashboard: https://internetwarte.eu/agentsetup

Frequently asked questions