{
 "schema_version": 1,
 "generated": "2026-10-26",
 "id": "mll-lab-nu-embodied-first-exam--run-core-exam-with-coding-agent",
 "repo": "mll-lab-nu/embodied-first-exam",
 "repo_url": "https://github.com/mll-lab-nu/embodied-first-exam",
 "repo_slug": "mll-lab-nu-embodied-first-exam",
 "title": "Run the core exam with a coding agent",
 "page_title": "How to run the core exam with a coding agent with Embodied First Exam",
 "summary": "Score a coding agent on the 449-task core set of robot tasks in one Harbor command.",
 "who": "A robotics researcher who wants a baseline score for a code agent on physical-world tasks before comparing models.",
 "problem": "Code agents are tested on digital tasks, and there is no standard way to see how they perform when writing code that drives a robot in a simulated scene.",
 "outcome": "A scored run of the 449 core tasks, each answered with a program and checked in a separate sandbox.",
 "steps": [
  "Clone the repository with its submodules: `git clone --recurse-submodules https://github.com/mll-lab-nu/embodied-first-exam.git`",
  "Enter the folder and install Harbor with this repository's flags: `uv tool install --force -e . --with-executables-from harbor`",
  "Copy the example environment file and add your model API key to `.env`: `cp .env.example .env`",
  "Run the core set in its default mode: `harbor run -d embodied-first-exam -a codex -m openai/gpt-6-astra`"
 ],
 "agent_prompt": "Use the repo mll-lab-nu/embodied-first-exam to run the core set with the agent <agent name> and model <model id>. Report the number of tasks completed and the overall score from the Harbor output. Verify that the run used 449 tasks before you report the result.",
 "agents": [
  "codex",
  "claude-code"
 ],
 "difficulty": "Intermediate",
 "time": "1 hour",
 "basis": "readme",
 "caveat": "The first run pulls benchmark images, and some suites need an RTX-class GPU, so check the README table of GPU and disk needs before starting the full set.",
 "category": "Research",
 "tags": [
  "Research"
 ],
 "lang": "python",
 "requirements": [
  "Linux x86_64 host with git, uv, and Docker Engine with Compose v2 and BuildKit (or rootless podman with -e podman)",
  "NVIDIA driver and NVIDIA Container Toolkit for any GPU task",
  "An API key for the agent's model in .env, or a ChatGPT or Claude subscription",
  "Enough free disk for the benchmark images (1 to 150 GB depending on the suite)"
 ],
 "risk": "none",
 "seen_in_the_wild": false,
 "mentioned_publicly": false,
 "risks": [],
 "best": true,
 "score": 1.0,
 "evidence": [],
 "url": "https://dedupedai.com/use-cases/mll-lab-nu-embodied-first-exam/run-core-exam-with-coding-agent/",
 "md": "https://dedupedai.com/use-cases/mll-lab-nu-embodied-first-exam/run-core-exam-with-coding-agent/index.md",
 "disclaimer": "Deduped lists open-source repositories that belong to other people. We do not own, maintain, audit, or vouch for any of them, and a repo appearing here is not an endorsement or a safety check. The use cases, steps, and prompts are written by AI and may be incomplete, outdated, or just wrong. Where a use case is marked \"Seen in the wild\", we link to public posts where someone described something similar, but we have not verified those posts and the use case itself is still our AI's own write-up. Use all of this at your own risk. Only run code, commands, or agents on systems and accounts you are authorized to use, and read the code and every command before you run it, especially anything that touches credentials, files, or the network. Nothing here is professional, security, legal, or financial advice. All trademarks and project names belong to their respective owners, and their use here does not imply affiliation. Content is provided as is, without warranties of any kind. Found a mistake, a safety problem, or something you want removed? Email hello@dedupedai.com and we will look at it promptly.",
 "api": "https://dedupedai.com/api/v1/usecases/mll-lab-nu-embodied-first-exam--run-core-exam-with-coding-agent.json",
 "api_version": "v1"
}