Arize Evaluator
github/awesome-copilot
Handles LLM-as-judge evaluation workflows on Arize including creating/updating evaluators, running evaluations on spans or experiments, managing tasks, trigger-run operations, column mapping, and…
Agent skill
Mlsys Artifact Evaluation
by brycewang-stanford in brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when packaging an accepted MLSys paper's code, configs, and measurement scripts for the venue's post-acceptance artifact evaluation, targeting the Availability, Functional…
MITAuto-check passed
Install Mlsys Artifact Evaluation
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill mlsys-artifact-evaluation -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills mlsys-artifact-evaluation --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-artifact-evaluation .claude/skills/mlsys-artifact-evaluation && 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-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/MLSys-Skills/skills/mlsys-artifact-evaluation into .claude/skills/mlsys-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mlsys-artifact-evaluation", 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-artifact-evaluationType 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-artifact-evaluation -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills mlsys-artifact-evaluation --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-artifact-evaluation .agents/skills/mlsys-artifact-evaluation && 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-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/MLSys-Skills/skills/mlsys-artifact-evaluation into .agents/skills/mlsys-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mlsys-artifact-evaluation", 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-artifact-evaluation -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills mlsys-artifact-evaluation --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-artifact-evaluation .cursor/skills/mlsys-artifact-evaluation && 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-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/MLSys-Skills/skills/mlsys-artifact-evaluation into .cursor/skills/mlsys-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mlsys-artifact-evaluation", 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-artifact-evaluation--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-artifact-evaluation -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills mlsys-artifact-evaluation --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-artifact-evaluation .gemini/skills/mlsys-artifact-evaluation && 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-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/MLSys-Skills/skills/mlsys-artifact-evaluation into .gemini/skills/mlsys-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mlsys-artifact-evaluation", 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-artifact-evaluationInstalls 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-artifact-evaluation -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-artifact-evaluation .github/skills/mlsys-artifact-evaluation && 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-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/MLSys-Skills/skills/mlsys-artifact-evaluation into .github/skills/mlsys-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mlsys-artifact-evaluation", 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-artifact-evaluation -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-artifact-evaluation --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-artifact-evaluation .opencode/skills/mlsys-artifact-evaluation && 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-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/MLSys-Skills/skills/mlsys-artifact-evaluation into .opencode/skills/mlsys-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mlsys-artifact-evaluation", 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-artifact-evaluation- GitHub stars
- 1.2k
- Token cost
- ~1.7k tokens
- SKILL.md length
- 652 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when packaging an accepted MLSys paper's code, configs, and measurement scripts for the venue's post-acceptance artifact evaluation, targeting the Availability, Functional…
- Packaging an accepted MLSys papers code
- SKILL.md covers Badge strategy — decide the…, The hardware problem — MLSys…, Package skeleton evaluators… and Artifact Appendix skeleton, plus 4 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Measurement scripts for the venues post-acceptance artifact evaluation
What it does
Mlsys Artifact Evaluation is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when packaging an accepted MLSys paper's code, configs, and measurement scripts for the venue's post-acceptance artifact evaluation, targeting the Availability, Functional, and Reproducible badges, writing the Artifact Appendix, handling hardware that AE reviewers cannot access, and answering anonymous evaluator questions.
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
- Packaging an accepted MLSys papers code
- Measurement scripts for the venues post-acceptance artifact evaluation
- Targeting the Availability
- Reproducible badges
Example prompts
- “s code, configs, and measurement scripts for the venue”
- “/mlsys-artifact-evaluation”
Requirements
- Python 3
- Docker
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 dockerfile).
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 Artifact Evaluation loads about 1.7k tokens when it runs. Until then it costs about 89 tokens; SKILL.md has 652 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). 652 words, ~1,702 tokens.
Download SKILL.mdSave it as
.claude/skills/mlsys-artifact-evaluation/SKILL.md (or your agent's skills folder).- name
- mlsys-artifact-evaluation
- description
- Use when packaging an accepted MLSys paper's code, configs, and measurement scripts for the venue's post-acceptance artifact evaluation, targeting the Availability, Functional, and Reproducible badges, writing the Artifact Appendix, handling hardware that AE reviewers cannot access, and answering anonymous evaluator questions.
MLSys Artifact Evaluation
Use this after acceptance, when deciding whether and how to enter MLSys artifact evaluation. AE is one of this venue's defining institutions: a separate committee evaluates how well artifacts support the paper's claims and awards badges. In the 2026 cycle (verified 2026-07-08): submission of artifact abstract, paper PDF, and Artifact Appendix to a dedicated AE site by March 8; evaluation window March 8 - April 8; badges for Availability, Functional, and Reproducible; anonymous reviewer-author interaction during evaluation; and a small number of Distinguished Artifact Awards for exceptional packages. Reopen the current Call for Artifact Evaluations before relying on any of these mechanics.
Badge strategy — decide the target first
| Badge | What evaluators check | Typical cost to authors |
|---|---|---|
| Availability | Artifact is publicly and permanently retrievable (public GitHub-style link expected in the appendix) | Hours: license, public repo, archival snapshot |
| Functional | The artifact runs: documented, complete relative to the paper, exercisable end-to-end | Days: install path, smoke test, small-scale example |
| Reproducible | Evaluators regenerate the paper's key results following your instructions | Weeks: automation, hardware plan, tolerance definitions |
Claim badges honestly. Requesting Reproducible when the headline table needs a cluster the committee cannot access converts a friendly process into a failed one; requesting only Availability+Functional with a clear statement of why is respected.
The hardware problem — MLSys AE's hardest part
Most MLSys results are performance results on specific hardware. Solve this explicitly in the appendix rather than hoping:
- Tier the claims. Separate results reproducible on one commodity GPU (or CPU) from results needing an 8-GPU node from results needing a cluster. Ask for the Reproducible badge on the tiers evaluators can actually run.
- Scale-down targets. Provide a reduced configuration (smaller model, shorter trace) whose trend matches the paper — and state the expected numbers for that reduced configuration, since evaluators cannot compare against a table they cannot reproduce.
- Relative, not absolute, tolerances. Performance varies across machines; define success as "speedup over the included baseline within ±15%" rather than absolute throughput.
- If you can offer supervised access to your hardware, check whether the current AE call permits it; do not assume.
Package skeleton evaluators expect
# Dockerfile — pin the system layer, not just Python
FROM nvidia/cuda:12.4.1-devel-ubuntu22.04
RUN apt-get update && apt-get install -y git python3.11 python3-pip
COPY requirements.lock /w/requirements.lock # exact versions, hash-pinned
RUN pip install --no-cache-dir -r /w/requirements.lock
COPY . /w
WORKDIR /w
# One command per claim tier:
# make smoke (<10 min, any GPU) -> Functional
# make table3 (~1 h, 1x A100/H100) -> Reproducible tier 1
# make fig6-scaled (reduced trace, expected: 1.5-1.7x over baseline)
CMD ["make", "smoke"]- README with: claims-to-commands map, hardware/time requirements per command, and a "what should I see" block for every command.
- All baselines included at the versions and configurations used in the paper — a package that reproduces your system but not the comparison reproduces nothing.
- Logged outputs from your own runs, so evaluators can diff behavior before burning GPU-hours.
- Traces, datasets, or generators for the workloads; if a workload is proprietary, ship a synthetic surrogate and label the substitution honestly.
Show full SKILL.md (227 more words)Show less
Artifact Appendix skeleton
The appendix is the evaluator's map; in 2026 it was submitted with the artifact abstract and paper PDF to the AE site. Whatever template the current call mandates, cover:
A.1 Abstract what the artifact contains, one paragraph
A.2 Claims supported paper claim -> command -> expected output -> tolerance
A.3 Requirements hardware (per tier), software, disk, network access,
time-to-first-result and time-to-full-reproduction
A.4 Setup container build or install path; offline fallback for
anything fetched from the network at build time
A.5 Experiment map make targets / scripts per figure and table number
A.6 Known deviations results that vary by hardware and by how much;
proprietary workloads replaced by surrogates
A.7 Reusability how to point the artifact at new models/workloads —
this is what separates award-level artifactsWrite A.3 pessimistically: an evaluator who discovers an undeclared 200GB download or a hidden internet dependency mid-window will not restart with goodwill.
During the evaluation window
- Interaction is anonymous and mediated; respond within a day — the window is finite (one month in 2026) and a stuck evaluator is a failed badge.
- Treat every evaluator failure as a packaging bug to fix and re-push, not a user error to explain away; committees typically allow artifact updates during evaluation.
- Keep one author on call who can debug environment issues; the most common failure mode is driver/container mismatch on the evaluator's machine, not your code.
Why bother
Badges appear with the paper and signal to this community — where results are routinely questioned as hardware-specific — that a third party ran your code. The Distinguished Artifact Award exists because MLSys treats the artifact as part of the scholarship, and a badged artifact keeps producing citations after the conference ends.
Cycle-volatility warnings
- Badge names, the AE site, deadlines, and whether AE remains post-acceptance-optional are all per-cycle decisions; the 2026 mechanics above are anchors to verify.
- The Artifact Appendix template, if one is mandated, comes from the current AE call.
Output format
[Badges targeted] availability / +functional / +reproducible (per claim tier)
[Claim tiers] <claim -> hardware needed -> reproducible by AE? >
[Package status] <docker/README/baselines/workloads/logged-outputs>
[Scale-down plan] <reduced configs + expected numbers>
[On-call owner] <person for the evaluation window>
[Gaps before AE deadline] <ordered list>© 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-artifact-evaluation of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Mlsys Artifact Evaluation 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 Artifact Evaluation this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.7k | Automated safety check: Pass | MIT | |
| Arize Evaluatorgithub/awesome-copilot | 40k | 1 repos | ~8.1k | Automated safety check: Notes | MIT | |
| Micro Artifact Evaluationbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.7k | Automated safety check: Pass | MIT | |
| Ccs Artifact Evaluationbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~969 | Automated safety check: Pass | MIT | |
| Oopsla Artifact Evaluationbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.1k | Automated safety check: Pass | MIT | |
| Ppopp Artifact Evaluationbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.5k | Automated safety check: Pass | MIT |
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Questions about Mlsys Artifact Evaluation
What does Mlsys Artifact Evaluation do?
A skill your agent uses when packaging an accepted MLSys paper's code, configs, and measurement scripts for the venue's post-acceptance artifact evaluation, targeting the Availability, Functional…. Mlsys Artifact Evaluation is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when packaging an accepted MLSys paper's code, configs, and measurement scripts for the venue's post-acceptance artifact evaluation, targeting the Availability, Functional, and Reproducible badges, writing the Artifact Appendix, handling hardware that AE reviewers cannot access, and answering anonymous evaluator questions.
When should I use Mlsys Artifact Evaluation?
Mlsys Artifact Evaluation fits situations like: packaging an accepted MLSys papers code; measurement scripts for the venues post-acceptance artifact evaluation; targeting the Availability; reproducible badges.
How do I install Mlsys Artifact Evaluation in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill mlsys-artifact-evaluation -a claude-code`. Or copy the skill folder (MLSys-Skills/skills/mlsys-artifact-evaluation in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/mlsys-artifact-evaluation in your project. Claude Code loads it when a task matches its description.
How do I install Mlsys Artifact Evaluation in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill mlsys-artifact-evaluation -a codex`. Or copy the skill folder (MLSys-Skills/skills/mlsys-artifact-evaluation in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/mlsys-artifact-evaluation in your project. Codex loads it when a task matches its description.
Can I use Mlsys Artifact Evaluation 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-artifact-evaluation -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-artifact-evaluation, .gemini/skills/mlsys-artifact-evaluation, .github/skills/mlsys-artifact-evaluation and .opencode/skills/mlsys-artifact-evaluation in your project.
What does Mlsys Artifact Evaluation need to run?
SKILL.md names no scripts, command-line tools or credentials: Mlsys Artifact Evaluation is instructions for the agent only. Our summary lists: Python 3; Docker.
Does Mlsys Artifact Evaluation 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 Artifact Evaluation 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 Artifact Evaluation use?
Mlsys Artifact Evaluation 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 Artifact Evaluation use?
About 1.7k tokens (SKILL.md is roughly 6.8k 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 Artifact Evaluation?
Skills that share tags, products or a category with Mlsys Artifact Evaluation: Arize Evaluator (github/awesome-copilot, 40k stars), Micro Artifact Evaluation (brycewang-stanford/Awesome-Journal-Skills, 1.2k stars), Ccs Artifact Evaluation (brycewang-stanford/Awesome-Journal-Skills, 1.2k stars) and Oopsla Artifact Evaluation (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 Artifact Evaluation?
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.