How are Codex, Claude Code, OpenClaw and other agents deployed in practice?
Share anonymous configuration details and compare deployments worldwide.
Two simple options:
Run via Agent Skill (Recommended) - less than 2 minutes
Install the skill from internetwarte/agent-setup-survey.
~/.codex/skills/git clone https://github.com/internetwarte/agent-setup-survey cp -r agent-setup-survey ~/.codex/skills/
Execute: Claude /agent-setup-survey or Codex $agent-setup-survey. The agent asks for consent and submits anonymized results automatically.
Manual questionnaire (less than 30 seconds).
Help build an open dataset on how local and user-controlled AI agents are deployed (local machines, self-hosted servers, VPS).
This data contributes to an ongoing research study on real-world AI agent execution environments.
More details in the footer.
Agent skills are small extensions that teach your AI agent a new task.
A skill usually contains:
SKILL.md file with instructions the agent can followIn this case, the skill teaches your agent how to collect a few high-level configuration details and submit them anonymously.
You install the skill in your agent’s skill folder (e.g., ~/.codex/skills or ~/.claude/skills) and then execute it like any other command.
For the full technical specification, see: agentskills.io/specification
| Agent | Global Skill Folder |
|---|---|
| Amp / Kimi Code CLI / Replit / Universal | ~/.config/agents/skills/ |
| Antigravity | ~/.gemini/antigravity/skills/ |
| Augment | ~/.augment/skills/ |
| Claude Code | ~/.claude/skills/ |
| OpenClaw | ~/.openclaw/skills/ |
| Cline | ~/.cline/skills/ |
| CodeBuddy | ~/.codebuddy/skills/ |
| Codex | ~/.codex/skills/ |
| Command Code | ~/.commandcode/skills/ |
| Continue | ~/.continue/skills/ |
| Cortex Code | ~/.snowflake/cortex/skills/ |
| Crush | ~/.config/crush/skills/ |
| Cursor | ~/.cursor/skills/ |
| Droid | ~/.factory/skills/ |
| Gemini CLI | ~/.gemini/skills/ |
| GitHub Copilot | ~/.copilot/skills/ |
| Goose | ~/.config/goose/skills/ |
| Junie | ~/.junie/skills/ |
| iFlow CLI | ~/.iflow/skills/ |
| Kilo Code | ~/.kilocode/skills/ |
| Kiro CLI | ~/.kiro/skills/ |
| Kode | ~/.kode/skills/ |
| MCPJam | ~/.mcpjam/skills/ |
| Mistral Vibe | ~/.vibe/skills/ |
| Mux | ~/.mux/skills/ |
| OpenCode | ~/.config/opencode/skills/ |
| OpenHands | ~/.openhands/skills/ |
| Pi | ~/.pi/agent/skills/ |
| Qoder | ~/.qoder/skills/ |
| Qwen Code | ~/.qwen/skills/ |
| Roo Code | ~/.roo/skills/ |
| Trae | ~/.trae/skills/ |
| Trae CN | ~/.trae-cn/skills/ |
| Windsurf | ~/.codeium/windsurf/skills/ |
| Zencoder | ~/.zencoder/skills/ |
| Neovate | ~/.neovate/skills/ |
| Pochi | ~/.pochi/skills/ |
| AdaL | ~/.adal/skills/ |
Research imprint
This study is conducted by researchers at
IT:U Austria
(Interdisciplinary Transformation University Austria)
in collaboration with the
University of Vienna.
The goal is to understand how AI coding agents are deployed across the broader developer community. Only user-agent and coarse network prefixes (/24 for IPv4, /64 for IPv6) are stored for rate-limiting and aggregation, alongside submitted survey answers. Aggregated results are shared publicly.
The endpoint is hosted by Hetzner in Germany. If you have questions about the study, please open an issue on GitHub.
Technologies
| Area | Technology |
|---|---|
| Agent skills | agentskills.io |
| Visualizations | Apache ECharts |
| Backend/API | FastAPI + Nginx |
| Datasets | IPinfo Lite, Pyasn, RouteViews, CAIDA AS-Org |