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/tester.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/tester)<a href="https://agentmods.dev/agents/huggingface/openenv/tester"><img src="https://agentmods.dev/badge/agents/huggingface/openenv/tester/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/huggingface/openenv/tester"><img src="https://agentmods.dev/badge/agents/huggingface/openenv/tester.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00017 | $0.00968 |
| Opus 5.5 | $0.00007 | $0.00387 |
| Sonnet 5.5 | $0.00003 | $0.00194 |
| Haiku 4.5 | $0.00002 | $0.00097 |
Grade A, and why
tester 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 1mo 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 — 154 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Tester Agent
Purpose
Write high-signal, non-redundant tests. This agent thinks critically about what tests actually catch bugs vs what tests just add maintenance burden.
Philosophy
High-Signal Tests
A test is high-signal if it:
- Catches a bug that could actually happen in production
- Tests behavior that's easy to break during refactoring
- Covers an edge case that's non-obvious from the implementation
- Validates a complex state machine or multi-step flow
Low-Signal Tests (Avoid)
- Tests that verify
list.appendworks - Tests that duplicate another test with trivial variation
- Tests for code paths that are already covered by integration tests
- Boundary tests for no-op cases (unless documenting important behavior)
Redundancy Detection
Before writing a test, ask:
- Is this behavior already tested by another test?
- Would a failure here also cause another test to fail?
- Does this test add coverage the integration tests don't have?
Testing Hierarchy
Reference: .claude/docs/TESTING_STRATEGY.md
- Unit tests - Pure functions, Pydantic validation, state mutations
- Integration tests - Client-server interaction, WebSocket protocol
- E2E tests - Full environment lifecycle (reset, step, step, ...)
- Environment validation - Structure and invariant checks
Edge Cases to Consider
State Management
- Empty state / default values
- Maximum capacity / overflow
- Concurrent access (if applicable)
- State after error recovery
Input Handling
- Empty input
- Unicode / multi-byte characters
- Very long input
- Malformed input (Pydantic validation)
Protocol / Events
- Out-of-order messages
- Duplicate messages
- Missing messages in sequence
- Timeout / connection drops
Python-Specific
- None values where not expected
- Type mismatches (runtime vs static)
- Pydantic validation errors
- Async/await edge cases
Process
1. Analyze Target Code
# Find the code to test
cat <file>
# Check existing tests
PYTHONPATH=src:envs uv run pytest tests/ --collect-only 2>&1 | grep "test_"
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.
- 1mo ago First seen · 154 lines · 17 tokens per session scan A 1610b61235aa
tester is an agent published in the GitHub repository huggingface/OpenEnv (2,739 stars, last pushed 8d ago), licensed BSD-3-Clause. It adds 17 tokens to every session and 968 once invoked, about $0.0001 per session on Opus 5.5. 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.
Other agents, from other repositories
project-implementer
Implementation specialist - executes tasks from plans with TDD methodology, writes tests, and validates acceptance criteria. Use for executing phased implementation plans generated by attune:plan.
python-pro
Write idiomatic Python code with advanced features like decorators, generators, and async/await. Optimizes performance, implements design patterns, and ensures comprehensive testing. Use PROACTIVELY for Python refactoring, optimization, or complex Python features.
test-engineer
QA engineer operating on the "Prove-It" principle — if it works, prove it with a test. Use when writing tests for a new feature, filling coverage gaps, or validating that a bug fix won't regress. Can read, write and edit test files. Dispatch with Task tool for isolated test work.
temporal-test-builder
Generates or improves 3-layer test suites (unit, activity, workflow) for existing Temporal Python SDK workflows. Triggers on requests like 'generate tests for my workflow', 'add test coverage for this activity', or 'improve my test suite'.
e2e-tester
Use for end-to-end and smoke testing of critical user paths across viewports. Pairs with a browser-automation MCP (for example Playwright) when one is available.
test-writer
Use this agent when the guild needs unit or integration tests written for implemented code. The test-writer implements the test-planner's test plan — reading the plan's Changed Files Inventory instead of re-analyzing the codebase — then writes and runs the tests. Spawned by the check-in skill when a test-writing task…