Agent skill

Mlsys Related Work

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

A skill your agent uses when positioning an MLSys submission against the fast-moving ML-systems literature scattered across OSDI, SOSP, NSDI, ASPLOS, and ML venues, handling arXiv-first and…

MITAuto-check passedResearch & Science

Install Mlsys Related Work

skills CLI

$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill mlsys-related-work -a claude-code

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

GitHub CLI

$ gh skill install brycewang-stanford/Awesome-Journal-Skills mlsys-related-work --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-related-work .claude/skills/mlsys-related-work && 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/

Paste into Claude Code

Install the "mlsys-related-work" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/MLSys-Skills/skills/mlsys-related-work into .claude/skills/mlsys-related-work/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mlsys-related-work", 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

$skill-installer install https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/MLSys-Skills/skills/mlsys-related-work

Type 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.

skills CLI

$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill mlsys-related-work -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install brycewang-stanford/Awesome-Journal-Skills mlsys-related-work --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ 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-related-work .agents/skills/mlsys-related-work && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "mlsys-related-work" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/MLSys-Skills/skills/mlsys-related-work into .agents/skills/mlsys-related-work/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mlsys-related-work", 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.

skills CLI

$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill mlsys-related-work -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install brycewang-stanford/Awesome-Journal-Skills mlsys-related-work --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ 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-related-work .cursor/skills/mlsys-related-work && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "mlsys-related-work" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/MLSys-Skills/skills/mlsys-related-work into .cursor/skills/mlsys-related-work/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mlsys-related-work", 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

$ gemini skills install https://github.com/brycewang-stanford/Awesome-Journal-Skills.git --path MLSys-Skills/skills/mlsys-related-work

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill mlsys-related-work -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install brycewang-stanford/Awesome-Journal-Skills mlsys-related-work --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ 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-related-work .gemini/skills/mlsys-related-work && rm -rf skills-src

Use ~/.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/

Paste into Gemini CLI

Install the "mlsys-related-work" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/MLSys-Skills/skills/mlsys-related-work into .gemini/skills/mlsys-related-work/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mlsys-related-work", 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.

GitHub CLI

$ gh skill install brycewang-stanford/Awesome-Journal-Skills mlsys-related-work

Installs 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).

skills CLI

$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill mlsys-related-work -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ 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-related-work .github/skills/mlsys-related-work && rm -rf skills-src

Use ~/.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/

Paste into GitHub Copilot

Install the "mlsys-related-work" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/MLSys-Skills/skills/mlsys-related-work into .github/skills/mlsys-related-work/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mlsys-related-work", 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.

skills CLI

$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill mlsys-related-work -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install brycewang-stanford/Awesome-Journal-Skills mlsys-related-work --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ 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-related-work .opencode/skills/mlsys-related-work && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "mlsys-related-work" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/MLSys-Skills/skills/mlsys-related-work into .opencode/skills/mlsys-related-work/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mlsys-related-work", 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-related-work
GitHub stars: 1.2k
Token cost: ~1.8k tokens
SKILL.md length: 880 words
Files: 1
Skills in repo: 2,387
Repo updated: Sep 27, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

A skill your agent uses when positioning an MLSys submission against the fast-moving ML-systems literature scattered across OSDI, SOSP, NSDI, ASPLOS, and ML venues, handling arXiv-first and…

Positioning an MLSys submission against the fast-moving ML-systems literature scattered across OSDI
SKILL.md covers The five lanes to cover, The freshness problem, Comparing against work without… and Writing the delta statement, plus 5 more sections
Reaches github.com
Handling arXiv-first and open-source-first prior work

What it does

Mlsys Related Work is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when positioning an MLSys submission against the fast-moving ML-systems literature scattered across OSDI, SOSP, NSDI, ASPLOS, and ML venues, handling arXiv-first and open-source-first prior work, comparing against production systems that have no paper, and writing the delta statement two reviewer cultures will both accept.

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 Literature review, Academic paper search and Positioning and messaging. It works with arXiv. 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

Positioning an MLSys submission against the fast-moving ML-systems literature scattered across OSDI
Handling arXiv-first and open-source-first prior work
Comparing against production systems that have no paper
Writing the delta statement two reviewer cultures will both accept

Example prompts

“/mlsys-related-work”

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 (its code samples are bibtex).
From the folder's file list and the shell code blocks in SKILL.md.
Network
Hosts in commands or code, which the agent is likely to contact:
- 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

Mlsys Related Work loads about 1.8k tokens when it runs. Until then it costs about 87 tokens; SKILL.md has 880 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~87
When it runs · the whole SKILL.md, loaded when a task matches: ~1.8k

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). 880 words, ~1,808 tokens.

Download SKILL.mdSave it as .claude/skills/mlsys-related-work/SKILL.md (or your agent's skills folder).

name: mlsys-related-work
description: Use when positioning an MLSys submission against the fast-moving ML-systems literature scattered across OSDI, SOSP, NSDI, ASPLOS, and ML venues, handling arXiv-first and open-source-first prior work, comparing against production systems that have no paper, and writing the delta statement two reviewer cultures will both accept.

Use this to audit positioning and novelty. The core difficulty is that MLSys's literature does not live at MLSys: the field's landmark systems are spread across OS, architecture, networking, and ML conferences, plus arXiv reports and repositories that never became papers. A related-work section here is judged on whether it maps that whole territory, not one venue's proceedings.

The five lanes to cover

Lane	Where it publishes	What reviewers check
ML-systems venue work	MLSys proceedings (proceedings.mlsys.org)	Do you know this venue's own line on your topic?
Classical systems venues	OSDI, SOSP, NSDI, ASPLOS, ATC, EuroSys, SC	Is the nearest big-venue system compared or distinguished?
ML algorithm venues	NeurIPS, ICML, ICLR	Does the ML-side idea you accelerate/serve already have algorithmic competitors?
Industry systems	arXiv reports, engineering blogs, open-source repos	Are the systems practitioners actually use acknowledged?
Benchmarks/measurement	MLPerf/MLCommons line, characterization studies	Is your evaluation methodology situated, not invented?

A bibliography drawing on only one or two lanes signals to half the reviewer pool that their community's prior work is being rediscovered.

The freshness problem

ML-systems moves on a months-scale clock: serving engines, compilers, and quantization methods ship major improvements between your experiments and your reviews.

Re-run a literature sweep in the final month before the deadline (in the 2026 cycle: October), specifically for new arXiv postings and release notes of your baselines.
Pin the exact version/commit of every compared system in the paper; "we compare against X" without a version is unanswerable when X improves next month.
Reviews arrive months after submission (January, for the 2026 cycle). Expect "why not compare against Y?" where Y postdates your submission — you cannot pre-cite it, but you can pre-empt the mechanism: if Y's approach is a known design point, place it in your taxonomy even before a specific system ships.

Comparing against work without papers

Production and open-source systems are legitimate, expected comparison points here:

Cite repositories and technical reports with version/commit and access date; treat a README's performance claims as claims, not results.
If the practitioner-standard system is closed (a cloud provider's serving stack), say so and compare against the strongest open proxy, naming the gap honestly.
Never dismiss an unpublished system as "not peer-reviewed" — to a systems reviewer who runs it daily, that reads as evasion.

bibtex

@misc{vllm2025,
  title        = {vLLM (release v0.x.y)},
  howpublished = {\url{https://github.com/vllm-project/vllm}},
  note         = {Commit abc1234, accessed 2026-07-08; evaluated configuration in App.~B},
  year         = {2026}
}

Writing the delta statement

Both reviewer cultures must find their contrast:

Against the nearest system: what mechanism differs, and what measured behavior changes because of it — "unlike X's reactive swapping, we schedule migrations into predicted bubbles, which is why the p99 gap appears only under bursty load."
Against the nearest algorithm: what your systems constraints add — "batching-aware variants of this idea exist [7]; none address multi-tenant memory pressure."
Comparison tables (rows: systems; columns: capabilities) are venue-idiomatic and efficient, but every checkmark against a competitor must be defensible from their paper or code — reviewers include those systems' authors.

Show full SKILL.md (406 more words)Show less

Building the capability table honestly

The venue-idiomatic comparison table is powerful and dangerous. Rules that keep it defensible when the compared systems' authors review you:

Columns are capabilities the paper evaluates, not marketing attributes; a column never exercised in the evaluation does not belong in the table.
Every negative cell (✗ for a competitor) carries a citation or footnote to where that limitation is documented — their paper, their docs, or your measured attempt.
Version-stamp the table: a system's ✗ can become ✓ in next month's release, and a reviewer running the newer version will check.
Include the row where a competitor beats you, if the evaluation shows one; a table where the proposed system sweeps every column is read as curated, and curation in the comparison table contaminates trust in the results tables.

Worked contrast sentence, fictional serving-scheduler paper: "Orca-style iteration-level scheduling [4] and reactive expert swapping [9] both target utilization; the former assumes dense models, the latter pays migration on the critical path. Our planner addresses the MoE case the former excludes, using the bubble structure the latter ignores — Section 6.3 measures both boundaries." Two named systems, two mechanism-level gaps, one pointer to where the claims are tested.

Misattribution traps

Do not cite famous ML-systems work to the wrong venue (vLLM is SOSP, PipeDream is SOSP, FlashAttention is NeurIPS); this venue's reviewers notice. Verified MLSys-native exemplars live in ../../resources/exemplars/library.md.
Do not claim "first to X" in a field with a large gray literature; write "to our knowledge, the first published system that X" and let the evidence carry it.
Concurrent work that appeared during your project deserves a neutral sentence and a mechanism-level contrast, not silence — silence looks like either ignorance or fear.

Scoping the search itself

Search the venue's own archive first (proceedings.mlsys.org is small enough to scan a topic exhaustively in an hour) — missing an MLSys-native predecessor is the least forgivable gap at MLSys.
Then sweep the last two editions of each systems venue in your lane, then the ML-venue "efficient ML" tracks, then arXiv's recent months; breadth-first by community, not depth-first by citation chain, or one community's chain will crowd out the others.

Cite your own prior systems in third person, and be careful with self-identifying version lineages ("we extend our earlier scheduler" → "we extend the scheduler of [12]"). arXiv posting of the submission itself is permitted (2026 rule), but the related-work text must not link the submission to that preprint.

Output format

text

[Lane coverage] <mlsys-native/systems-venues/ML-venues/industry/benchmarks: present?>
[Nearest neighbors] <top 3 with venue + version/commit where applicable>
[Delta statement] <mechanism contrast + measured-behavior contrast>
[Freshness] <last sweep date; baselines pinned?>
[Misattribution/overlap risks] <wrong-venue cites, "first" claims, concurrent work>
[Fixes] <ordered list>

Files

Just SKILL.md in MLSys-Skills/skills/mlsys-related-work of brycewang-stanford/Awesome-Journal-Skills.

Open the folder on GitHubat commit 932eb23

Compare with similar skills

Mlsys Related Work 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 Related Work compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Mlsys Related Work this skillbrycewang-stanford/Awesome-Journal-Skills	1.2k	—	~1.8k	Automated safety check: Pass	MIT	13 days ago
Ijcai Related Workfranklee16/academic-research-skills	223	1 repos	~427	Automated safety check: Pass	None	22 days ago
Literature Reviewneflibata-feng/MyArxiv-Agent	126	20 repos	~5.9k	Automated safety check: Notes	MIT	yesterday
Systematic Literature Review Builderbytedance/deer-flow	84k	2 repos	~4.3k	Automated safety check: Pass	MIT	today
Paper Research on arXivXiaomiMiMo/MiMo-Code	14k	—	~1.5k	Automated safety check: Pass	MIT	yesterday
Literature Review AgentAr9av/PaperOrchestra	679	1 repos	~5.2k	Automated safety check: Pass	Custom licence	19 days ago

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Ijcai Related Work
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Works with

arXiv

Questions about Mlsys Related Work

What does Mlsys Related Work do?

A skill your agent uses when positioning an MLSys submission against the fast-moving ML-systems literature scattered across OSDI, SOSP, NSDI, ASPLOS, and ML venues, handling arXiv-first and…. Mlsys Related Work is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when positioning an MLSys submission against the fast-moving ML-systems literature scattered across OSDI, SOSP, NSDI, ASPLOS, and ML venues, handling arXiv-first and open-source-first prior work, comparing against production systems that have no paper, and writing the delta statement two reviewer cultures will both accept.

When should I use Mlsys Related Work?

Mlsys Related Work fits situations like: positioning an MLSys submission against the fast-moving ML-systems literature scattered across OSDI; handling arXiv-first and open-source-first prior work; comparing against production systems that have no paper; writing the delta statement two reviewer cultures will both accept.

How do I install Mlsys Related Work in Claude Code?

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

How do I install Mlsys Related Work in Codex?

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

Can I use Mlsys Related Work 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-related-work -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-related-work, .gemini/skills/mlsys-related-work, .github/skills/mlsys-related-work and .opencode/skills/mlsys-related-work in your project.

What does Mlsys Related Work need to run?

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

Does Mlsys Related Work access the network?

SKILL.md names 1 domain. In commands or code: github.com; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Mlsys Related Work 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 Related Work use?

Mlsys Related Work 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 Related Work 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 Related Work?

Skills that share tags, products or a category with Mlsys Related Work: Ijcai Related Work (franklee16/academic-research-skills, 223 stars), Literature Review (neflibata-feng/MyArxiv-Agent, 126 stars), Systematic Literature Review Builder (bytedance/deer-flow, 84k stars) and Paper Research on arXiv (XiaomiMiMo/MiMo-Code, 14k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Mlsys Related Work?

brycewang-stanford (a GitHub user) maintains it in brycewang-stanford/Awesome-Journal-Skills, which has 1,231 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.

Install Mlsys Related Work

Facts

At a glance

What it does

When your agent uses it

Example prompts

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md