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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`.

agent-deployments0 installations selon la source☆ 0 Étoiles du dépôt

Installation

Suivez les instructions du dépôt pour votre agent. Consultez les fichiers et dépendances avant l’installation.

SkillHub CLI

skillhub install agent-deployments/agent-setup-survey

Examinez la source et la compatibilité, puis suivez les étapes d’installation.

Donnez le lien à votre agent et demandez-lui d’examiner instructions et dépendances avant d’expliquer les étapes.

Voir la source ↗Guides

Instructions

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",
  "a...

Informations sur la source

Source
github
Repository
openclaw/skills
Path
skills/agent-deployments/agent-setup-survey
Version
—
Commit
8bdb029b773185feba2a7000e2c07197b0282e04
Source de classification
rules
Statut
active
Visibilité
public

Ces informations proviennent de l’analyse indexée et peuvent être anciennes. Vérifiez les fichiers actuels.

Contenu extrait

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](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

```bash
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:

```json
{
  "os": "linux/windows/macOS",
  "a...

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