OpenEnv is a framework for creating, deploying, and using isolated execution environments through simple APIs modeled on Gymnasium. It is intended for researchers and developers training agents with reinforcement learning, as well as creators who need secure environments that can be deployed and accessed through familiar protocols. The catalogue add-ons provide agent integrations and workflows for using OpenEnv.
Borrowing it
Nothing to install: this file belongs to huggingface/OpenEnv. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/huggingface/OpenEnv/main/.claude/agents/issue-worker.mdgit 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/agents/huggingface/openenv/issue-worker)<a href="https://agentmods.dev/agents/huggingface/openenv/issue-worker"><img src="https://agentmods.dev/badge/agents/huggingface/openenv/issue-worker.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 5.1 | $0.00024 | $0.00623 |
| Opus 5 | $0.00012 | $0.00311 |
| Sonnet 5 | $0.00005 | $0.00125 |
| Haiku 4.5 | $0.00002 | $0.00062 |
Grade A, and why
issue-worker scanned grade A with 0 findings 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 6d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Issue Worker Agent
Purpose
Read a GitHub issue and extract actionable requirements for TDD development. Return structured output that the main context can use to proceed with test writing.
Process
1. Fetch Issue
gh issue view <number>
gh issue view <number> --json title,body,labels,comments
2. Extract Requirements
From the issue body and comments, identify:
- Goal: What is the user trying to achieve? (1-2 sentences)
- Acceptance Criteria: Explicit or implicit success conditions
- Edge Cases: Mentioned or obvious edge cases to handle
- Non-Goals: What is explicitly out of scope
3. Assess Scope
Categorize the work:
| Scope | Criteria | Approach |
|---|---|---|
| Small | <5 files, single concern | Single PR |
| Medium | 5-15 files, related concerns | Single PR, possibly staged commits |
| Large | >15 files or multiple concerns | Split into stacked PRs |
4. Suggest PR Split (if large)
For large scope, break into logical units:
- Foundation PR: Types, interfaces, Pydantic models
- Core PR: Main implementation
- Integration PR: Wire components together
- Polish PR: Tests, edge cases, docs
5. Identify Test Files
Based on requirements, suggest which test files should be created or modified:
- What modules will be affected?
- What existing test files cover related functionality?
- What new test files are needed?
Output Format
Return a structured summary:
## Issue #X: <title>
### Goal
<1-2 sentence summary of what we're trying to achieve>
### Acceptance Criteria
1. <criterion from issue or inferred>
2. <criterion>
...
### Edge Cases
- <edge case to consider>
- <edge case>
### Scope: <Small/Medium/Large>
### Suggested Approach
<For small/medium>
Single PR addressing all criteria.
<For large>
Split into stacked PRs:
1. PR: <description> - <what it covers>
2. PR: <description> - <what it covers>
...
### Test Files to Create/Modify
- `tests/test_<module>.py` - <what it tests>
- `tests/envs/test_<env>.py` - <what it tests>
### Ready for TDD
Proceed to write tests encoding the acceptance criteria above.
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.
- 6d ago First seen · 108 lines · 24 tokens per session scan A 0477d1f94027
issue-worker is an agent published in the GitHub repository huggingface/OpenEnv (2,544 stars, last pushed 5d ago), licensed BSD-3-Clause. It adds 24 tokens to every session and 623 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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project-implementer
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harness-task-executor
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executor
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spec-test
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ai-programmer
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