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Agent skill

Mlsys Author Response

by brycewang-stanford in brycewang-stanford/Awesome-Journal-Skills

A skill your agent uses when drafting MLSys author responses on OpenReview under the venue's compressed rebuttal window, prioritizing systems-reviewer objections about workload representativeness…

MITAuto-check passed

Install Mlsys Author Response

skills CLI
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill mlsys-author-response -a claude-code

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

GitHub CLI
$ gh skill install brycewang-stanford/Awesome-Journal-Skills mlsys-author-response --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/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/MLSys-Skills/skills/mlsys-author-response .claude/skills/mlsys-author-response && 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
mlsys-author-response
GitHub stars
1.2k
Token cost
~1.7k tokens
SKILL.md length
861 words
Files
1
Skills in repo
2,387
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when drafting MLSys author responses on OpenReview under the venue's compressed rebuttal window, prioritizing systems-reviewer objections about workload representativeness…

  • Works in 4 steps: A factual misreading by the most… → A missing measurement that two or more… → The workload-representativeness… → …
  • Drafting MLSys author responses on OpenReview under the venues compressed rebuttal window
  • SKILL.md covers Hour-zero triage (the four-day…, What systems reviewers…, Reply skeleton per point and Rules and boundaries, plus 5 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Mlsys Author Response is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when drafting MLSys author responses on OpenReview under the venue's compressed rebuttal window, prioritizing systems-reviewer objections about workload representativeness, baseline tuning, and missing measurements, deciding whether to run new experiments in days, and keeping replies anonymous and decision-focused.

Its SKILL.md is about 1.7k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

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

  • Drafting MLSys author responses on OpenReview under the venues compressed rebuttal window
  • Prioritizing systems-reviewer objections about workload representativeness
  • Baseline tuning
  • Missing measurements

Example prompts

  • “/mlsys-author-response”

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  1. A factual misreading by the most negative reviewer (cheapest score to recover).
  2. A missing measurement that two or more reviewers independently requested.
  3. The workload-representativeness objection, if raised.
  4. Everything else, in one compact "minor points" list.

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 Author Response loads about 1.7k tokens when it runs. Until then it costs about 86 tokens; SKILL.md has 861 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~86
When it runs · the whole SKILL.md, loaded when a task matches
~1.7k

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). 861 words, ~1,746 tokens.

Download SKILL.mdSave it as .claude/skills/mlsys-author-response/SKILL.md (or your agent's skills folder).
name
mlsys-author-response
description
Use when drafting MLSys author responses on OpenReview under the venue's compressed rebuttal window, prioritizing systems-reviewer objections about workload representativeness, baseline tuning, and missing measurements, deciding whether to run new experiments in days, and keeping replies anonymous and decision-focused.

MLSys Author Response

Use this when MLSys reviews land. The controlling fact about this venue's rebuttal is time: in the 2026 cycle, reviews were released January 12 and author responses were due January 16 — four days, including whatever new measurements you attempt. Verify the current window on the mlsys.org dates page the day reviews are scheduled, then plan hours, not weeks.

Hour-zero triage (the four-day clock)

WindowAction
Hours 0-4All authors read all reviews once without replying; classify each point as factual error / missing measurement / scope objection / preference
Hours 4-12Decide the at-most-two new experiments that are actually runnable; launch them immediately — machines, not prose, are the bottleneck
Day 2Draft skeleton replies per reviewer around the launched runs; resolve internal disagreements about concessions
Day 3Insert numbers as runs finish; cut everything that does not move a score
Final hoursAnonymity and tone pass; submit well before the cutoff — OpenReview traffic spikes at deadline

A run that cannot finish by day 3 does not exist for rebuttal purposes. Promise it for the camera-ready or artifact instead of gambling the reply on it.

What systems reviewers actually object to

  • "The workload is not representative." The most common MLSys objection. Answer with evidence the workload matches a public trace, a named benchmark family (reviewers know the MLPerf lineage), or a measured production characteristic — or concede the scope and say so in one sentence.
  • "The baseline is not tuned." Never answer defensively; state the tuning protocol (search space, budget, best configuration found) from the submission, or run the stronger configuration now and report it even if it narrows your win. A narrowed-but-honest gap gains more points than a defended-but-doubted one.
  • "Where is memory / p99 / cost / energy?" If it is in the appendix, quote the exact number and its location — remember reviewers were not obliged to read the appendix. If it was never measured, this is your best candidate for the two rebuttal runs.
  • "The gain will vanish on hardware X / at scale Y." Do not extrapolate. Give the mechanism-level argument for where the gain comes from, state the boundary honestly, and offer the analytical model or roofline reasoning if the paper has one.

Reply skeleton per point

text
> R2: "Throughput gains may come from batching, not the proposed scheduler."
We separated these in the submission: Table 4 (main paper, p.8) fixes batch size and
still shows 1.4x; the ablation with batching disabled entirely is Fig. 7.
New for this response: at R2's suggested batch=1 setting, the gain is 1.25x
(3 runs, std 0.02), consistent with the scheduler mechanism.
We will state the batch=1 result in Sec. 6.2.

Pattern: restate the objection in one line, anchor to a page/table in the submitted PDF, add at most one new number with its run count and variance, commit to a specific edit.

Rules and boundaries

  • Stay anonymous — research-track responses must not reveal company, cluster names, or internal system identities even when doing so would answer the question crisply.
  • Do not paste external links or point to updated arXiv versions unless the current cycle's instructions explicitly allow it; assume the submitted record is the whole record.
  • Do not restructure the paper in prose ("we will rewrite Sections 3-5") — meta-reviewers read that as an admission the submission was not ready.
  • Concede real limitations plainly. Systems reviewers reward "this does not help under uniform load, and we now say so" far more than a paragraph of deflection.
  • One reply block per reviewer, ordered by their objections' impact on the decision, not by the order they were written.
Show full SKILL.md (341 more words)Show less

Prioritization when everything is on fire

  1. A factual misreading by the most negative reviewer (cheapest score to recover).
  2. A missing measurement that two or more reviewers independently requested.
  3. The workload-representativeness objection, if raised.
  4. Everything else, in one compact "minor points" list.

Leave stylistic complaints unanswered rather than crowding out the decision-critical material; MLSys response formats are short, and the meta-reviewer skims for whether the central systems objection was met.

Worked triage: a typical MLSys review set

Fictional but representative packet for a serving-system paper, and the calls made:

  • R1 (systems, negative): "evaluation uses synthetic Poisson arrivals; real traffic is bursty." → Decision-critical, and a run is feasible: replay a public trace on the smallest model overnight. This becomes rebuttal experiment #1.
  • R2 (ML, lukewarm): "quality impact of request dropping never measured." → Second experiment: quality metric under the dropping policy, one model, three seeds.
  • R3 (systems, positive but doubting generality): "single GPU generation tested." → No hardware available in-window. Answer with the mechanism argument: which hardware property the gain depends on, plus a scoped-claim edit offer. No run promised.
  • All three: "writing dense in Section 4." → Two lines at the end: acknowledged, will split 4.2 and move the config table to the appendix.

The discipline: two launched runs, one mechanism argument, one batched concession — and nothing else. Six half-answered points lose to three closed ones.

Tone calibration

  • Systems reviewers respect being corrected with data and resent being lectured about their own field; delete any sentence explaining basics ("as is well known in serving systems...").
  • Thank reviewers once, globally, not per point — the response budget is too small for ritual.
  • Where two reviewers contradict each other (one wants more breadth, one more depth), say so explicitly and state your choice; meta-reviewers reward authors who notice.

Cycle-volatility warnings

  • The four-day window, the response format, and whether a discussion phase follows the response are all cycle-specific; the 2026 numbers here are anchors, not promises.
  • Whether reviewers may see updated PDFs during response varies — check the current OpenReview instructions rather than assuming.

Output format

text
[Deadline] <response due date + hours remaining>
[Runnable-by-deadline experiments] <at most two, with launch status>
[Reviewer map] <reviewer -> decision-critical objection -> anchor in submitted PDF>
[Concessions] <limitations to admit plainly>
[Draft reply] <per-reviewer text, anonymous, no links>

© 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-author-response of brycewang-stanford/Awesome-Journal-Skills.

Open the folder on GitHubat commit 932eb23

Compare with similar skills

Mlsys Author Response 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.

Mlsys Author Response compared with similar skills
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Mlsys Author Response this skillbrycewang-stanford/Awesome-Journal-Skills1.2k—~1.7kAutomated safety check: PassMIT
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Aistats Author Responsebrycewang-stanford/Awesome-Journal-Skills1.2k—~980Automated safety check: PassMIT
Acl Author Responsebrycewang-stanford/Awesome-Journal-Skills1.2k—~1.6kAutomated safety check: PassMIT
Icsme Author Responsebrycewang-stanford/Awesome-Journal-Skills1.2k—~1.3kAutomated safety check: PassMIT

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Questions about Mlsys Author Response

What does Mlsys Author Response do?

A skill your agent uses when drafting MLSys author responses on OpenReview under the venue's compressed rebuttal window, prioritizing systems-reviewer objections about workload representativeness…. Mlsys Author Response is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when drafting MLSys author responses on OpenReview under the venue's compressed rebuttal window, prioritizing systems-reviewer objections about workload representativeness, baseline tuning, and missing measurements, deciding whether to run new experiments in days, and keeping replies anonymous and decision-focused.

When should I use Mlsys Author Response?

Mlsys Author Response fits situations like: drafting MLSys author responses on OpenReview under the venues compressed rebuttal window; prioritizing systems-reviewer objections about workload representativeness; baseline tuning; missing measurements.

How do I install Mlsys Author Response in Claude Code?

Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill mlsys-author-response -a claude-code`. Or copy the skill folder (MLSys-Skills/skills/mlsys-author-response in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/mlsys-author-response in your project. Claude Code loads it when a task matches its description.

How do I install Mlsys Author Response in Codex?

Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill mlsys-author-response -a codex`. Or copy the skill folder (MLSys-Skills/skills/mlsys-author-response in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/mlsys-author-response in your project. Codex loads it when a task matches its description.

Can I use Mlsys Author Response 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-author-response -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-author-response, .gemini/skills/mlsys-author-response, .github/skills/mlsys-author-response and .opencode/skills/mlsys-author-response in your project.

What does Mlsys Author Response need to run?

SKILL.md names no scripts, command-line tools or credentials: Mlsys Author Response is instructions for the agent only.

Does Mlsys Author Response 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 Author Response 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 Author Response use?

Mlsys Author Response 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 Author Response use?

About 1.7k tokens (SKILL.md is roughly 7k 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 Author Response?

Skills that share tags, products or a category with Mlsys Author Response: Responsive Units (thedaviddias/Front-End-Checklist, 74k stars), Hermes Agent Skill Authoring (NousResearch/hermes-agent, 252k stars), Aistats Author Response (brycewang-stanford/Awesome-Journal-Skills, 1.2k stars) and Acl Author Response (brycewang-stanford/Awesome-Journal-Skills, 1.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Mlsys Author Response?

brycewang-stanford (a GitHub user) maintains it in brycewang-stanford/Awesome-Journal-Skills, which has 1,228 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.