Agent skill

Self Review

by verl-project in verl-project/verl-omni

Review your own verl-omni branch against the project rubric before opening or updating a PR.

Apache-2.0Auto-check passedDevelopment

Install Self Review

skills CLI
$ npx skills add verl-project/verl-omni --skill self-review -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install verl-project/verl-omni self-review --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/verl-project/verl-omni.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/code-review .claude/skills/self-review && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
self-review
GitHub stars
1.2k
Token cost
~2k tokens
SKILL.md length
934 words
Files
1
Skills in repo
7
Repo updated
First seen
Licence
Apache-2.0

At a glance

Review your own verl-omni branch against the project rubric before opening or updating a PR.

  • Works in 4 steps: Establish scope → Read the applicable rules → Review and report in four parts → …
  • Tasks that involve Code quality
  • SKILL.md covers 1. Establish scope, 2. Read the applicable rules, 3. Review and report in four… and 4. Handoff, not automatic…, plus 1 more section
  • Calls git

What it does

Self Review is an agent skill from verl-project/verl-omni. Review your own verl-omni branch against the project rubric before opening or updating a PR. Use before submitting a contribution, or whenever asked to self-review. Report-only: covers technical purpose, code quality, goal completeness and validation evidence, with a READY / NEEDS CHANGES verdict. Never edits files.

Its SKILL.md is about 2k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Development, covering Code quality and Quizzes and assessments. The repository describes itself as: Multimodal RL training framework for diffusion & omni models. The licence is Apache-2.0.

When your agent uses it

  • Tasks that involve Code quality
  • Tasks that involve Quizzes and assessments

Example prompts

  • “/self-review”

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Establish scope
  2. Read the applicable rules
  3. Review and report in four parts
  4. Handoff, not automatic publication

What it can do on your machine

Read from SKILL.md and the folder at commit 022b110. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • git

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • github.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Self Review loads about 2k tokens when it runs. Until then it costs about 82 tokens; SKILL.md has 934 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~82
When it runs · the whole SKILL.md, loaded when a task matches
~2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from verl-project/verl-omni at commit 022b110, republished under its Apache-2.0 licence (© verl-project). 934 words, ~2,018 tokens.

Download SKILL.mdSave it as .claude/skills/self-review/SKILL.md (or your agent's skills folder).
name
self-review
description
Review your own verl-omni branch against the project rubric before opening or updating a PR. Use before submitting a contribution, or whenever asked to self-review. Report-only: covers technical purpose, code quality, goal completeness and validation evidence, with a READY / NEEDS CHANGES verdict. Never edits files.

Self-review

Review the whole change, not only its latest commit. Keep the report proportional to the diff; a small fix does not need a technical essay.

Report-only — do not edit, commit, or push as part of reviewing. Once the contributor makes fixes, load commit-and-pr for the commit/PR conventions and run-cpu-tests for tests.

1. Establish scope

Identify the intended base and its remote; a stacked PR may not target main. Record the exact base and head SHAs, then inspect the whole three-dot diff:

bash
git remote -v
git fetch <base-remote> <base-branch>
git rev-parse <base-ref> HEAD
git diff <base-ref>...HEAD

Do not replace this with a last-commit review. A two-dot comparison against newer main includes main-only changes; check the merge base before calling those regressions. Use individual commits for history, not to omit parts of the final diff. For local uncommitted work, also inspect git diff --cached and git diff. Re-check affected evidence if the reviewed head changes.

2. Read the applicable rules

AGENTS.md is the top-level contract. Read the current guides, not a remembered copy. Formatting and project-specific conventions belong to these sources:

AreaGuide
code style, runtime boundaries, shell recipes.agents/rules/code-style.md
config dataclasses / generated YAML.agents/rules/config.md
diffusion pipelines / adapters.agents/rules/pipelines.md, docs/contributing/integrating_a_diffusion_model.md
diffusion algorithmadd-pipeline selects the policy-gradient or direct-preference guide
reward scorers.agents/rules/reward.md
tests.agents/rules/testing.md, docs/contributing/testing_guide.md
recurring trapsdocs/contributing/common_pitfalls.md
CI / GPU smokedocs/contributing/ci_cd.md, docs/contributing/gpu_smoke_tests.md

3. Review and report in four parts

A. Purpose and technical background

Summarize the problem, the mechanism being changed, and why the change is needed. Trace the relevant callers and consumers: e.g. config → allocation → worker, rollout → replay → loss, or adapter export → binding → forward. Check whether an existing implementation already solves it before recommending another abstraction.

B. Code quality and correctness

Inspect changed lines and their execution context, using the code-style rules. Classify findings by subject; these categories are not severity levels:

CategoryInspect
Correctnesstensor shape/dtype/device, gradients, wire compatibility, concurrency and resource lifetime
Performancehost/device synchronization, allocation, hot-loop overhead and measured workload equivalence
Maintainabilityownership, reuse, imports, naming and unnecessary abstractions
Styleclarity, useful comments/types and consistency beyond automated formatting
Processreproducible tests, dependency compatibility and evidence for the claimed scope

Mark an issue blocking or non-blocking from its impact, independently of category. Missing critical validation can block; a naming preference usually cannot. Do not invent findings to avoid saying the code is sound, or impose source-line limits, blanket bans on framework hooks, or formatter-only nits.

Project-specific checks:

  • Wire compatibility: for protocol-preserving refactors, keep valid prompt_token_ids, multi_modal_data and extra_fields intact end to end. Unsupported or conflicting fields must not disappear silently.
  • Media contracts: use DiffusionIOSpec / media_kind rather than guessing modality from ndim or a dimension equal to three.
  • Distributed paths: verify actual replica/rank allocation, sample ordering and loss normalization, not just a parsed config or a printed parallel degree.
  • Weight sync: trace export, name/shape/scaling conversion, binding and use. A tensor count or active adapter ID alone cannot prove value-correct binding.
  • Surgical scope: flag unrelated cleanup and orphaned code caused by this change; do not demand refactoring pre-existing debt unrelated to the goal.

Each finding needs: severity + category + evidence tag → path:line → triggering input/path → impact and why → concrete fix or next verification step.

C. Goal completeness

Compare the final diff with the issue and PR description. List unmet requirements, unsupported configurations, prerequisite PRs and changed defaults/compatibility. An intentional limitation is not a proven bug, but must not be presented as implemented or validated support. Check that examples and docs use the same contract as the code.

Show full SKILL.md (395 more words)Show less
D. Validation and accountability

Separate what you ran from author-reported evidence and untested paths. Choose the required layer from the testing guide; CPU tests, GPU trainer completion, performance and convergence are different claims, not interchangeable badges.

  • Tag findings [verified] (read exact source or reproduced), [likely] (inferred, with the missing check named), or [unchecked] (not inspected). Keep unchecked items as coverage gaps, not confirmed defects.
  • Give the exact command and result, or not run. Associate test logs with the tested SHA/config; a previous head's green check is not current-head evidence.
  • Check .github/*_pin.txt and the interpreter/worker source binding before attributing an import or runtime failure to the patch. Compare with the base when needed; a dependency mismatch is not automatically a code regression.
  • Read the automated gates' actual scope. CPU selection does not replace the config-doc, generated-config, device-API, DataProto or other sanity checks.
  • "Engine initialized" is not a completed GPU e2e. For timing claims identify the baseline, workload, hardware, warmup and all measured samples; distinguish actor-only from whole-training time and disclose contention.
  • Assess concrete problems such as swallowed errors, unnecessary fallbacks or unsupported claims; do not infer AI authorship from code style. Follow AGENTS.md for disclosure and human accountability, and never check off human review on someone else's behalf.

End with READY / NEEDS CHANGES, blocking actions, and residual risks. READY means no blockers for the stated scope, not proof of untested GPU behavior or convergence. If required evidence is missing, explain why it blocks. If there are no findings, say what supports that conclusion rather than just "LGTM".

4. Handoff, not automatic publication

Iterate after the contributor fixes findings. Keep review notes out of the code diff. Suggest updating the PR description when scope or evidence changes.

Publishing follows the accountability rules in AGENTS.md: draft review comments and replies for the user, and post only what they asked for and approved word for word.

When asked to suggest reviewers, use docs/community/governance.md for ownership and .github/CODEOWNERS for path routing (last matching rule wins). Suggest at most two relevant owners; do not automatically tag them.

Template provenance

The four-part review structure is adapted from Code Review Style Guide. Project rules take precedence: categories are separate from severity, abstraction is contextual, and evidence replaces speculative AI-code detection.

<!--
MAINTAINER GUIDE — Keep rule details in .agents/rules/ and procedures in docs/.
Recheck this rubric when review permissions, wire contracts or CI gates change.
-->

© verl-project, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .agents/skills/code-review of verl-project/verl-omni.

Open the folder on GitHubat commit 022b110

Compare with similar skills

Self Review next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Self Review compared with similar skills
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Skill Doctoralirezarezvani/claude-skills28k—~1.5kAutomated safety check: PassMIT
LobeHub Alint Rule Set Maintenancelobehub/lobehub83k—~1.9kAutomated safety check: PassCustom licence
Review PRmicrosoft/vscode-containers141—~900Automated safety check: PassCustom licence
RAG Code Reviewlyonzin/knowledge-rag292—~1.8kAutomated safety check: PassMIT

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Questions about Self Review

What does Self Review do?

Review your own verl-omni branch against the project rubric before opening or updating a PR. Self Review is an agent skill from verl-project/verl-omni. Review your own verl-omni branch against the project rubric before opening or updating a PR.

When should I use Self Review?

Self Review fits situations like: tasks that involve Code quality; tasks that involve Quizzes and assessments.

How do I install Self Review in Claude Code?

Run `npx skills add verl-project/verl-omni --skill self-review -a claude-code`. Or copy the skill folder (.agents/skills/code-review in verl-project/verl-omni) into .claude/skills/self-review in your project. Claude Code loads it when a task matches its description.

How do I install Self Review in Codex?

Run `npx skills add verl-project/verl-omni --skill self-review -a codex`. Or copy the skill folder (.agents/skills/code-review in verl-project/verl-omni) into .agents/skills/self-review in your project. Codex loads it when a task matches its description.

Can I use Self Review in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add verl-project/verl-omni --skill self-review -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/self-review, .gemini/skills/self-review, .github/skills/self-review and .opencode/skills/self-review in your project.

What does Self Review need to run?

Going by SKILL.md and its folder, Self Review needs the command-line tools its instructions call (git).

Does Self Review access the network?

SKILL.md names 1 domain. As links in the text: github.com. This is read from the text; nothing was executed.

Is Self Review safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Self Review use?

Self Review is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Self Review use?

About 2k tokens (SKILL.md is roughly 8.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Self Review?

Skills that share tags, products or a category with Self Review: Thermo Nuclear Code Quality Review (nicknisi/claude-plugins, 114 stars), Skill Doctor (alirezarezvani/claude-skills, 28k stars), LobeHub Alint Rule Set Maintenance (lobehub/lobehub, 83k stars) and Review PR (microsoft/vscode-containers, 141 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Self Review?

verl-project (a GitHub organization) maintains it in verl-project/verl-omni, which has 1,210 GitHub stars. The repository holds 7 skills in this directory. The repository was last updated on October 9, 2026.

Source: verl-project/verl-omni on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.