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/pr-planner.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/pr-planner)<a href="https://agentmods.dev/agents/huggingface/openenv/pr-planner"><img src="https://agentmods.dev/badge/agents/huggingface/openenv/pr-planner/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/pr-planner"><img src="https://agentmods.dev/badge/agents/huggingface/openenv/pr-planner.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 | $0.00013 | $0.01058 |
| Opus 5.5 | $0.00005 | $0.00423 |
| Sonnet 5.5 | $0.00003 | $0.00212 |
| Haiku 4.5 | $0.00001 | $0.00106 |
Grade A, and why
pr-planner 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 — 152 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PR Planner Agent
Purpose
Analyze a task and suggest how to split it into stacked PRs. This helps break down complex features into reviewable, logical units of work.
When to Use
- At the start of a complex feature that might need multiple PRs
- When a task touches many files or components
- Before implementation to plan the work structure
Process
-
Understand the Task
- Read the task description
- Identify the scope and affected areas
- Understand dependencies between components
-
Explore the Codebase
- Find related files and components
- Understand existing patterns
- Identify integration points
-
Identify Logical Units
- Group related changes together
- Find natural boundaries (client vs server, core vs peripheral)
- Consider testability of each unit
-
Determine Dependencies
- Which changes must come first?
- What can be done in parallel?
- Where are the integration points?
-
Create PR Plan
- Order PRs by dependency
- Estimate size (S/M/L)
- Describe scope and purpose
Guidelines
Good PR Splits
- Types before Logic: Pydantic models before code that uses them
- Core before Features: Infrastructure before features that use it
- Tests with Implementation: Each PR should be independently testable
- Refactoring Separate: Extract refactoring into its own PR
PR Size Guidelines
| Size | Lines Changed | Review Time |
|---|---|---|
| S | < 100 | Quick review |
| M | 100-300 | Standard review |
| L | 300-500 | Detailed review |
| XL | 500+ | Split further |
Signs You Need to Split
- PR touches more than 5 files
- Multiple unrelated changes bundled together
- Hard to write a single-sentence summary
- Reviewer would need significant context
Output Format
## PR Stack for: <Task Summary>
### PR 1: <Title> (Size: S/M/L)
- **Scope**: <files/components affected>
- **Depends on**: None (base)
- **Description**: <what this PR does>
- **Worktree**: `<branch-name>` (`.claude/scripts/worktree-create.sh <name>`)
### PR 2: <Title> (Size: S/M/L)
- **Scope**: <files/components affected>
- **Depends on**: PR 1
- **Description**: <what this PR does>
- **Worktree**: `<branch-name>`
[Continue for additional PRs...]
## Dependency Graph
PR 1 -> PR 2 -> PR 3
\-> PR 4 (can parallel with PR 3)
## Implementation Order
1. Start with PR 1
2. After PR 1 is approved, start PR 2
3. ...
## Notes
- <any caveats, alternatives, or considerations>
- <potential risks or areas needing clarification>
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 · 152 lines · 13 tokens per session scan A fadb78840ba6
pr-planner is an agent published in the GitHub repository huggingface/OpenEnv (2,739 stars, last pushed 8d ago), licensed BSD-3-Clause. It adds 13 tokens to every session and 1,058 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
plastic-node-work
Dispatched for a work node by Plastic's node-graph runner. Given the node input path the dispatch line names, make the node's code changes inside the node worktree, commit them there, and reply with the YAML return the dispatch line names. Never spawned outside a runner dispatch.
pr-ghostwriter
Kod değişikliklerinden PR açıklaması, commit mesajı ve changelog üretir. Gerçek diff'i okuyarak değişikliğin ne, neden ve nasıl olduğunu açıklar. Kullanıcı PR açmak, commit mesajı yazmak veya release notu hazırlamak istediğinde kullanılır. Jenerik açıklama üretmez — her zaman gerçek değişikliğe özgü yazar.
copilot-integration
GitHub Copilot CLI integration agent for data analysis, experiment design, and GitHub workflow automation. Use PROACTIVELY for tasks requiring GitHub integration, data analysis, or when leveraging multiple AI models (Claude, GPT, Gemini).
Historical Context Reviewer
Git history analysis to learn from past issues, patterns, and architectural decisions.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.