AReaL is an infrastructure system for training large language models with reinforcement learning, connecting model training to applications built around AI agents. Researchers and developers use it to train reasoning and agentic models through asynchronous workflows, and the catalogue add-ons support working with AReaL.
Borrowing it
Nothing to install: this file belongs to areal-project/AReaL. 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/areal-project/AReaL/main/.agents/skills/review-pr/SKILL.mdgit clone --depth 1 https://github.com/areal-project/AReaLWrote 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/areal-project/areal/review-pr)<a href="https://agentmods.dev/skills/areal-project/areal/review-pr"><img src="https://agentmods.dev/badge/skills/areal-project/areal/review-pr/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/skills/areal-project/areal/review-pr"><img src="https://agentmods.dev/badge/skills/areal-project/areal/review-pr.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00692 |
| Opus 5 | $0.00012 | $0.00346 |
| Sonnet 5 | $0.00005 | $0.00138 |
| Haiku 4.5 | $0.00002 | $0.00069 |
Grade A, and why
review-pr 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 10d 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 — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review Pull Request
Use this skill when the user asks for a PR review of the current branch or a specific PR.
Inputs
- Optional PR number
- Optional
--quickto stop after the change analysis phase
Hard Rules
- Stay read-only.
- Do not edit files, commit, push, rebase, or change GitHub state.
- Do not run build, install, or test commands that mutate the environment.
- Use
ghfor PR metadata and git diff retrieval.
Reference Files
references/review-pr-domains-and-signals.mdreferences/review-pr-templates.md
Workflow
Phase 1: Resolve PR context
- Use
gh pr viewto fetch PR title, body, state, draft status, and changed files. - If no PR exists, stop and report that clearly.
- If the PR is closed, stop.
- Record the branch name and changed file list.
Phase 2: Change analysis
- Classify changed files using
references/review-pr-domains-and-signals.md. - Determine the highest overall risk level:
CRITICAL,HIGH,MEDIUM, orLOW. - Build a
CHANGE_ANALYSIS_REPORTthat lists:- detected domains/signals
- risk level
- affected files
- related frameworks
- likely failure modes
If --quick is set, return the change analysis report and stop here.
Phase 3: Review planning
- Select the smallest useful set of review passes from
references/review-pr-templates.md. - Split by risk area, not by file count.
- Always include at least one general logic pass.
Phase 4: Expert consultation
Consult the matching Codex subagents registered in .codex/config.toml when relevant:
archon-expertfsdp-expertmegatron-expertalgorithm-expertlauncher-expert
If the Codex runtime supports parallel subagent execution, run independent review passes in parallel. Otherwise, execute them serially.
Phase 5: Final review
Produce findings first, ordered by severity:
CRITICALHIGHMEDIUMLOW
For every finding, include:
- file path
- line number when available
- why it is a bug, regression, or risk
- concrete fix direction
What ships with it
4 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.
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.
- 10d ago First seen · 117 lines · 24 tokens per session scan A 4a06d12b692f
review-pr is a skill published in the GitHub repository areal-project/AReaL (5,748 stars, last pushed today), licensed Apache-2.0. It adds 24 tokens to every session and 692 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 2026-08-30.
Other skills, from other repositories
github
GitHub via gh CLI: PRs, issues, reviews, repos, auth.
github
GitHub operations via gh CLI: issues, PRs, CI runs, code review, API queries. Use when: (1) checking PR status or CI, (2) creating/commenting on issues, (3) listing/filtering PRs or issues, (4) viewing run logs. NOT for: complex web UI interactions requiring manual browser flows (use browser tooling when available)…
issue-to-pr-resolver
Implement a GitHub issue end-to-end — create a worktree branch, implement the feature with tests, create a draft PR, then iteratively resolve all CI failures and review comments until the PR is clean. Use when you need to fully implement a GitHub issue from start to merge-ready. Triggers on "implement issue", "resolve…
my-pr-checker
Manage your own GitHub pull requests — check CI status, inline review comments, PR-level comments, resolve review threads, fix issues, and iterate until all checks pass and threads are resolved. Use for managing your own PRs (not external contributions). Triggers on "check my PR", "check PR", "/my-pr-checker ".
meta-codereview-current-diff
Read the current uncommitted diff, run three independent reviewers (safety + tests-coverage + style) in parallel, then arbitrate a single BLOCK / BLOCKWITHOVERRIDE / PASSWITHNOTES verdict. Use before commit when you want a multi-perspective second-opinion instead of a single-reviewer agent loop.
goal-pr
Drive a pull request to a clean state and merge it: run the review-pr skill, fix every mid-or-above finding, and repeat until no mid-or-above findings remain, then merge. Use when the user wants to finish a PR by reviewing, fixing, and merging it, or triggers on "the goal-pr skill".