Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/huggingface/openenv/generate-openenv-envnpx skills add huggingface/OpenEnv --skill generate-openenv-envgit clone --depth 1 https://github.com/huggingface/OpenEnvWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/huggingface/openenv/generate-openenv-env)<a href="https://agentmods.dev/skills/huggingface/openenv/generate-openenv-env"><img src="https://agentmods.dev/badge/skills/huggingface/openenv/generate-openenv-env.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00079 | $0.01591 |
| Opus 5 | $0.00039 | $0.00796 |
| Sonnet 5 | $0.00016 | $0.00318 |
| Haiku 4.5 | $0.00008 | $0.00159 |
Grade A, and why
generate-openenv-env scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl http://localhost:8000/health How it starts
The opening of the file, as written. The whole thing — 165 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/generate-openenv-env
Build a production-ready OpenEnv environment from a use-case prompt.
Execute Workflow
When invoked, execute this workflow end-to-end.
1. Parse the use case and name the environment
Derive a repo path in the form envs/<name>_env/.
- Normalize to snake_case.
- Keep names short and domain-specific.
- Example: "generate an env for the library textarena" ->
envs/textarena_env/.
2. Research the target library/API before coding
Gather the minimum interface facts needed to implement reset, step, and state serialization.
- Search local docs/examples first.
- Search upstream docs/repo for the target library when local context is insufficient.
- Extract only implementation-critical details:
- installation/dependency requirements
- environment creation API
- action format
- observation format
- reward and done semantics
- special setup (model files, downloads, auth, etc.)
3. Mine matching OpenEnv examples
Select 2-3 existing environments as implementation templates.
- Always read
references/openenv-tutorial-01-environments.md(Part 10) andreferences/openenv-docs-environment-builder.md. - Prefer
envs/textarena_envfor external-library wrappers with richer state. - Add one simpler baseline (for example
envs/snake_envorenvs/echo_env) to keep the implementation minimal. - Follow patterns, do not copy blindly.
- Exclude generated or vendored files when mining examples (
.venv/,build/,site-packages/,__pycache__/).
For a compact checklist and mapping, read references/env-generation-checklist.md.
4. Ask focused implementation questions
Ask only the questions that materially affect architecture. Use the question bank in references/env-generation-checklist.md.
Cover at least:
- action space contract
- observation fields needed by agents
- reward design and terminal conditions
- episode/session configuration knobs
- deployment target and dependency constraints
If answers are unavailable, proceed with explicit assumptions and document them.
What ships with it
16 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- agents/openai.yaml 230 B
- assets/openenv_env_template/__init__.py 479 B runs code
- assets/openenv_env_template/.dockerignore 94 B
- assets/openenv_env_template/client.py 3.2 KB runs code
- assets/openenv_env_template/models.py 938 B runs code
- assets/openenv_env_template/openenv.yaml 96 B
- assets/openenv_env_template/pyproject.toml 1.3 KB
- assets/openenv_env_template/README.md 7.8 KB
- assets/openenv_env_template/server/__ENV_NAME___environment.py 3.4 KB runs code
- assets/openenv_env_template/server/__init__.py 379 B runs code
- assets/openenv_env_template/server/app.py 2.6 KB runs code
- assets/openenv_env_template/server/Dockerfile 2.6 KB
- assets/openenv_env_template/server/requirements.txt 48 B
- references/env-generation-checklist.md 3.5 KB
- references/openenv-docs-environment-builder.md 15 KB
- references/openenv-tutorial-01-environments.md 39 KB
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 3d ago First seen · 165 lines · 79 tokens per session scan A 852d7d2d65fa
generate-openenv-env is a skill published in the GitHub repository huggingface/OpenEnv (2,531 stars, last pushed 5d ago), licensed BSD-3-Clause. It adds 79 tokens to every session and 1,591 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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