Peer Review
spacering-net/codeg
Structured manuscript/grant review with checklist-based evaluation.
A skill your agent uses when reasoning about how MLSys peer review works, covering the OpenReview workflow, the mixed ML-and-systems reviewer pool and how each half scores differently, the…
Install Mlsys Review Process
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill mlsys-review-process -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills mlsys-review-process --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/MLSys-Skills/skills/mlsys-review-process .claude/skills/mlsys-review-process && rm -rf skills-srcUse ~/.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/
Install the "mlsys-review-process" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/MLSys-Skills/skills/mlsys-review-process into .claude/skills/mlsys-review-process/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mlsys-review-process", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/MLSys-Skills/skills/mlsys-review-processType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill mlsys-review-process -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills mlsys-review-process --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/MLSys-Skills/skills/mlsys-review-process .agents/skills/mlsys-review-process && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "mlsys-review-process" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/MLSys-Skills/skills/mlsys-review-process into .agents/skills/mlsys-review-process/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mlsys-review-process", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill mlsys-review-process -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills mlsys-review-process --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/MLSys-Skills/skills/mlsys-review-process .cursor/skills/mlsys-review-process && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "mlsys-review-process" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/MLSys-Skills/skills/mlsys-review-process into .cursor/skills/mlsys-review-process/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mlsys-review-process", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/brycewang-stanford/Awesome-Journal-Skills.git --path MLSys-Skills/skills/mlsys-review-process--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill mlsys-review-process -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills mlsys-review-process --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/MLSys-Skills/skills/mlsys-review-process .gemini/skills/mlsys-review-process && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "mlsys-review-process" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/MLSys-Skills/skills/mlsys-review-process into .gemini/skills/mlsys-review-process/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mlsys-review-process", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills mlsys-review-processInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill mlsys-review-process -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/MLSys-Skills/skills/mlsys-review-process .github/skills/mlsys-review-process && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "mlsys-review-process" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/MLSys-Skills/skills/mlsys-review-process into .github/skills/mlsys-review-process/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mlsys-review-process", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill mlsys-review-process -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills mlsys-review-process --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/MLSys-Skills/skills/mlsys-review-process .opencode/skills/mlsys-review-process && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "mlsys-review-process" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/MLSys-Skills/skills/mlsys-review-process into .opencode/skills/mlsys-review-process/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mlsys-review-process", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
mlsys-review-process- GitHub stars
- 1.2k
- Token cost
- ~1.8k tokens
- SKILL.md length
- 817 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when reasoning about how MLSys peer review works, covering the OpenReview workflow, the mixed ML-and-systems reviewer pool and how each half scores differently, the…
- Reasoning about how MLSys peer review works
- SKILL.md covers The pipeline (2026 anchors), Who reviews here — the…, What decisions actually turn on and Reading a review packet, plus 5 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Covering the OpenReview workflow
What it does
Mlsys Review Process is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when reasoning about how MLSys peer review works, covering the OpenReview workflow, the mixed ML-and-systems reviewer pool and how each half scores differently, the compressed response window, industrial-track review expectations, decision dynamics, and what the post-acceptance artifact stage means for review strategy.
Its SKILL.md is about 1.8k 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 Research & Science, covering Peer review. The repository describes itself as: Journal-specific Claude Code/Codex skill packs covering mainstream journals — AER, QJE, Nature, Cell, 管理世界, 经济研究 & 200+ more — your fast track to getting published. ｜ 覆盖主流期刊的… The licence is MIT.
When your agent uses it
- Reasoning about how MLSys peer review works
- Covering the OpenReview workflow
- The mixed ML-and-systems reviewer pool and how each half scores differently
- The compressed response window
Example prompts
- “/mlsys-review-process”
What it can do on your machine
Read from SKILL.md and the folder at commit 932eb23. 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
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
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
Mlsys Review Process loads about 1.8k tokens when it runs. Until then it costs about 86 tokens; SKILL.md has 817 words of instructions outside code blocks.
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 brycewang-stanford/Awesome-Journal-Skills at commit 932eb23, republished under its MIT licence (© brycewang-stanford). 817 words, ~1,798 tokens.
Download SKILL.mdSave it as
.claude/skills/mlsys-review-process/SKILL.md (or your agent's skills folder).- name
- mlsys-review-process
- description
- Use when reasoning about how MLSys peer review works, covering the OpenReview workflow, the mixed ML-and-systems reviewer pool and how each half scores differently, the compressed response window, industrial-track review expectations, decision dynamics, and what the post-acceptance artifact stage means for review strategy.
MLSys Review Process
Use this to model what happens to a Conference on Machine Learning and Systems submission between upload and decision. Mechanics below are 2026-cycle anchors (verified 2026-07-08); the venue is young and still redesigns its process — 2026 alone added an entire track — so reopen the current CFP and OpenReview group before strategic decisions.
The pipeline (2026 anchors)
- Submission via OpenReview (
MLSys.org/2026/Conferencegroup) by October 30, 2025. - Double-blind review; arXiv posting allowed in parallel.
- Reviews released January 12, 2026; author responses due January 16; notifications
January 25-26. There is no long discussion phase to rescue a paper — the response is
a single, short shot (see
mlsys-author-response). - Accepted papers publish on proceedings.mlsys.org; artifact evaluation follows as a separate, optional, badge-awarding stage (March 8 - April 8 in 2026).
Who reviews here — the two-culture pool
MLSys program committees deliberately mix ML researchers with systems, architecture, and compiler people. The same paper is read through two different quality lenses:
| Dimension | ML-culture reviewer asks | Systems-culture reviewer asks |
|---|---|---|
| Contribution | Is the idea new relative to the ML literature? | Is there a reusable mechanism/abstraction, or just engineering? |
| Evidence | Are comparisons fair, seeds varied, quality preserved? | Is the workload realistic? Where are the bottleneck analysis and tails? |
| Skepticism trigger | Accuracy deltas without significance | "Up to Nx" speedups without workload context |
| Appendix habits | May check math and extra ablations | Rarely reads it; judges the 10 pages |
A submission that satisfies only one culture gets a split review set, and split reviews at a single-shot-response venue are dangerous: you have four days to convert one side. Write for both from the start — name the mechanism (systems lens) and show quality is preserved under the optimization (ML lens).
What decisions actually turn on
- Workload representativeness is the most common fatal objection: a system evaluated only on microbenchmarks or toy models loses both cultures at once.
- Baseline strength: comparing against an untuned or outdated system is treated as invalidating, not just weakening, the result — the field's baselines (serving engines, compilers, training frameworks) improve monthly.
- Claim-evidence scope match: a general claim ("for transformer inference") tested on one model family gets scoped down by reviewers if the authors did not scope it first.
- Honesty signals: reported non-wins, stated tradeoffs, and cost accounting raise trust scores disproportionately at this venue.
- Industrial-track papers are judged on different axes: scale realism, design methodology depth, and benchmark detail — explicitly not novelty (2026 track rules). A research-track-style novelty defense in an industrial-track response misses the actual bar, and vice versa; know which rubric your reviewers were given.
- The appendix asymmetry: since reviewers are not obliged to read the separate appendix, an objection already answered there is still a live objection — the review process treats the 10 pages as the paper, and responses must quote the appendix material into the reply rather than pointing at it indignantly.
Reading a review packet
Triage grid for an MLSys review set:
R1 (systems): workload not representative -> decision-critical, answerable
R2 (ML): missing significance on Table 2 -> decision-critical, cheap to fix
R3 (systems): "wish you compared against X" -> check X's publication date vs
your deadline; if after, say so
All: writing nits -> batch into two lines
Rank by (decision impact) x (answerability in 4 days); ignore tone.Meta-review synthesis rewards responses that resolve the shared objection across reviewers; three reviewers independently doubting the baseline is one problem, not three.
Show full SKILL.md (323 more words)Show less
Reading scores and reviewer signals
- A short review with a middling score from a systems reviewer usually means "plausible but I don't trust the evaluation" — the response should add measurement, not prose.
- A long, detailed negative review is often the most convertible: the reviewer engaged deeply enough to change their mind if the specific objections close.
- Confidence scores matter more here than at mega-conferences: with small, topically close panels, a high-confidence negative reviewer who is factually wrong is the highest-priority target, because the meta-reviewer will otherwise defer to them.
- Watch for the culture split masquerading as disagreement: R1 (ML) at accept and R3 (systems) at reject with non-overlapping objections is not noise — it means the paper currently serves one audience. Say explicitly in the response how each culture's concern is met.
- Do not read tone as signal; systems-review bluntness ("this evaluation is not credible") is genre convention, not a verdict on the idea.
Confidentiality and conduct
- Submissions are confidential to the review process; reviewers must not use or share them. Authors likewise must not fish for reviewer identities or contact PC members about their paper outside the platform.
- The double-blind-plus-arXiv model means a reviewer may recognize your preprint; policy treats good-faith anonymization by authors as the requirement, not reviewer ignorance. Do not exploit this by advertising the arXiv version at reviewers.
After the decision
- Rejected: MLSys reviews are unusually actionable (workload, baseline, and measurement
gaps are concrete); the annual-cycle question is whether to strengthen for next MLSys
or reroute to a systems venue with a nearer deadline — see
mlsys-topic-selection. - Accepted: review strategy hands off to camera-ready reconciliation and the artifact stage, where a different committee re-examines your evidence in executable form.
Cycle-volatility warnings
- Response-window length, discussion mechanics, reviewer-volunteer expectations for authors, and any AI-use policy in reviewing were not verifiable for 2026 beyond the dates above (待核实) — confirm on the live pages.
- Acceptance-rate folklore changes yearly and is omitted here deliberately.
Output format
[Stage] pre-submission / under review / response / decided
[Review-set shape] <systems vs ML objections, split or aligned>
[Decision-critical objection] <the one the meta-review will weigh>
[Response leverage] <answerable in window? with what evidence>
[Conduct checks] <anonymity/contact/confidentiality risks>
[Next move] <one action>© brycewang-stanford, MIT. 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 MLSys-Skills/skills/mlsys-review-process of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Mlsys Review Process 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Mlsys Review Process this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.8k | Automated safety check: Pass | MIT | |
| Peer Reviewspacering-net/codeg | 3.8k | 18 repos | ~5.9k | Automated safety check: Notes | MIT | |
| Scholar Evaluationspacering-net/codeg | 3.8k | 12 repos | ~3.2k | Automated safety check: Pass | MIT | |
| Academic Paper Writing PipelineImbad0202/academic-research-skills | 51k | — | ~16k | Automated safety check: Pass | Custom licence | |
| LLM Counciltenfoldmarc/llm-council-skill | 819 | 2 repos | ~4.2k | Automated safety check: Pass | None | |
| Academic Paper ReviewerImbad0202/academic-research-skills | 51k | — | ~11k | Automated safety check: Pass | Custom licence |
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Run any question, idea, or decision through a council of 5 AI advisors who independently analyze it, peer-review each other anonymously, and synthesize a final verdict.
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Categories
Questions about Mlsys Review Process
What does Mlsys Review Process do?
A skill your agent uses when reasoning about how MLSys peer review works, covering the OpenReview workflow, the mixed ML-and-systems reviewer pool and how each half scores differently, the…. Mlsys Review Process is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when reasoning about how MLSys peer review works, covering the OpenReview workflow, the mixed ML-and-systems reviewer pool and how each half scores differently, the compressed response window, industrial-track review expectations, decision dynamics, and what the post-acceptance artifact stage means for review strategy.
When should I use Mlsys Review Process?
Mlsys Review Process fits situations like: reasoning about how MLSys peer review works; covering the OpenReview workflow; the mixed ML-and-systems reviewer pool and how each half scores differently; the compressed response window.
How do I install Mlsys Review Process in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill mlsys-review-process -a claude-code`. Or copy the skill folder (MLSys-Skills/skills/mlsys-review-process in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/mlsys-review-process in your project. Claude Code loads it when a task matches its description.
How do I install Mlsys Review Process in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill mlsys-review-process -a codex`. Or copy the skill folder (MLSys-Skills/skills/mlsys-review-process in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/mlsys-review-process in your project. Codex loads it when a task matches its description.
Can I use Mlsys Review Process 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 brycewang-stanford/Awesome-Journal-Skills --skill mlsys-review-process -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/mlsys-review-process, .gemini/skills/mlsys-review-process, .github/skills/mlsys-review-process and .opencode/skills/mlsys-review-process in your project.
What does Mlsys Review Process need to run?
SKILL.md names no scripts, command-line tools or credentials: Mlsys Review Process is instructions for the agent only.
Does Mlsys Review Process access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Mlsys Review Process 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 Mlsys Review Process use?
Mlsys Review Process is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Mlsys Review Process use?
About 1.8k tokens (SKILL.md is roughly 7.2k 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 Mlsys Review Process?
Skills that share tags, products or a category with Mlsys Review Process: Peer Review (spacering-net/codeg, 3.8k stars), Scholar Evaluation (spacering-net/codeg, 3.8k stars), Academic Paper Writing Pipeline (Imbad0202/academic-research-skills, 51k stars) and LLM Council (tenfoldmarc/llm-council-skill, 819 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Mlsys Review Process?
brycewang-stanford (a GitHub user) maintains it in brycewang-stanford/Awesome-Journal-Skills, which has 1,219 GitHub stars. The repository holds 2,387 skills in this directory. The repository was last updated on September 27, 2026.
Source: brycewang-stanford/Awesome-Journal-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.