Generate Ors Env

What it does

When your agent uses it

• Someone asks to scaffold an ORS env
• Port to OpenReward
• Add per-tool-call rewards
• Deploy to OpenReward.ai

Example prompts

• “wrap my env in ORS”
• “make an OpenReward env for X”
• “add per-call reward to my env”
• “/generate-ors-env”

Requirements

• Python 3
• Docker
• A credential in E2B_API_KEY

Workflow steps

5 steps, taken from the step headings in SKILL.md.

1. 1Tasks file — tasks.py
2. 2The Environment subclass — server.py
3. 3Server entry point — server.py main
4. 4Rollout
5. 5Dockerfiles

What it can do on your machine

Read from SKILL.md and the folder at commit 5e0b46e. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  Shell commands in SKILL.md call:

  • uv
  • curl
  • jq

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  Links to these hosts (documentation or services it may open):

  • openrewardstandard.io
  • docs.openreward.ai
  • pypi.org
  • github.com

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names these keys or tokens, usually read from environment variables:

  • E2B_API_KEY

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Generate Ors Env loads about 2.3k tokens when it runs, and up to ~3.5k if it reads all its reference files. Until then it costs about 191 tokens; SKILL.md has 580 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~191

When it runs · the whole SKILL.md, loaded when a task matches

~2.3k

With references · SKILL.md plus every file in references/, read only if the agent opens them

~3.5k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

• NoteMentions a .env fileSKILL.md:178

  bles — they survive rebuilds. The local `.env` file should not be uploaded.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from adithya-s-k/FineEnvs at commit 5e0b46e, republished under its Apache-2.0 licence (© adithya-s-k). 580 words, ~2,288 tokens.

name

generate-ors-env

description

Builds an Open Reward Standard (ORS) variant of an RL environment using the official `openreward` Python package. Use whenever someone asks to scaffold an ORS env, port to OpenReward, add per-tool-call rewards, deploy to OpenReward.ai, or wrap an existing env in the ORS protocol. ORS is the right framework when the user wants HTTP+REST+SSE, rewards arriving inline with each tool call (not post-episode), task-spec-driven sessions, splits (train/val/test), or deployment to OpenReward.ai or HF Spaces. Output is a runnable `<env_dir>/ors/` folder with `server.py`, `tasks.py`, `pyproject.toml`, `Dockerfile.spaces`, and `rollout.py`. Use for prompts like "wrap my env in ORS", "make an OpenReward env for X", or "add per-call reward to my env".

from openreward.environments import (
    Environment, Server, tool, ToolOutput, TextBlock, Split, ImageBlock,
)

from openreward import EnvironmentsAPI
api = EnvironmentsAPI(base_url=URL, api_key="")
env = api.get(ENV_NAME)

<env_dir>/ors/
├── pyproject.toml         # openreward>=0.1.33 + e2b-* (if needed) + pydantic
├── __init__.py
├── Dockerfile             # local dev image
├── Dockerfile.spaces      # HF Space (port 7860, single-stage pip install)
├── README.spaces.md       # HF Space frontmatter
├── server.py              # the Environment subclass + main()
├── tasks.py               # list of dicts (task_spec for each task)
├── rollout.py             # or rollout_openai.py + rollout_qwen.py
└── README.md              # one-page dev README

TASKS = [
    {"answer": "apple", "task": "Guess the 5-letter word."},
    # ...
]

from pydantic import BaseModel
from openreward.environments import Environment, Server, tool, ToolOutput, TextBlock, Split

class GuessInput(BaseModel):
    word: str

class WordleORS(Environment):
    def __init__(self, task_spec=None, secrets=None, **kw):
        super().__init__(task_spec=task_spec or {}, secrets=secrets or {})
        self._game = None

    def setup(self):                      # called on first tool invocation
        self._game = WordleGame(self.task_spec.get("answer"))

    def teardown(self):                   # called on session delete
        self._game = None

    @classmethod
    def list_splits(cls): return [Split(name="train", type="train")]

    @classmethod
    def list_tasks(cls, split): return TASKS

    def get_prompt(self):
        return [TextBlock(text="Play Wordle. Guess the 5-letter word.")]

    @tool
    def guess(self, params: GuessInput) -> ToolOutput:
        feedback = self._game.guess(params.word)
        return ToolOutput(
            blocks=[TextBlock(text=feedback)],
            reward=self._game.reward,
            finished=self._game.done,
        )

def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("--port", type=int, default=8080)
    parser.add_argument("--host", type=str, default="0.0.0.0")
    args = parser.parse_args()
    Server([WordleORS]).run(host=args.host, port=args.port)

api = EnvironmentsAPI(base_url=ENV_URL, api_key="")
env = api.get("wordleors")
tasks = env.list_tasks("train")
tools = env.list_tools(format="openai")     # built-in OpenAI tool-schema converter
with env.session(task=tasks[0]) as session:
    prompt = session.get_prompt()
    result = session.call_tool("guess", {"word": "crane"})
    # result.blocks, result.reward, result.finished

FROM python:3.11-slim
RUN useradd -m -u 1000 user
RUN pip install --no-cache-dir openreward pydantic <other-deps>
USER user
ENV HOME=/home/user PATH=/home/user/.local/bin:$PATH
WORKDIR $HOME/app
COPY --chown=user . $HOME/app
EXPOSE 7860
CMD ["python", "server.py", "--host", "0.0.0.0", "--port", "7860"]

---
title: My Env ORS
emoji: 🎯
colorFrom: pink
colorTo: indigo
sdk: docker
app_port: 7860
tags: [ors, openreward]
---

api.add_space_secret(repo_id="<owner>/<env>-ors", key="E2B_API_KEY", value="...")
api.upload_file(path_or_fileobj="Dockerfile.spaces", path_in_repo="Dockerfile", repo_id=...)
api.upload_file(path_or_fileobj="README.spaces.md", path_in_repo="README.md", repo_id=...)
# upload server.py, tasks.py, __init__.py, pyproject.toml

© adithya-s-k, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file (references) in .claude/skills/generate-ors-env of adithya-s-k/FineEnvs.

• SKILL.md
• references/architecture.md

Open the folder on GitHubat commit 5e0b46e