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
Sigmetrics Artifact Evaluation
by brycewang-stanford in brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when packaging an ACM SIGMETRICS artifact for the ACM Artifact Review and Badging scheme (Artifacts Available, Evaluated Functional and Reusable, Results Reproduced)…
MITAuto-check passed
Install Sigmetrics Artifact Evaluation
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sigmetrics-artifact-evaluation -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sigmetrics-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/SIGMETRICS-Skills/skills/sigmetrics-artifact-evaluation .claude/skills/sigmetrics-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 "sigmetrics-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGMETRICS-Skills/skills/sigmetrics-artifact-evaluation into .claude/skills/sigmetrics-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sigmetrics-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/SIGMETRICS-Skills/skills/sigmetrics-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 sigmetrics-artifact-evaluation -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sigmetrics-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/SIGMETRICS-Skills/skills/sigmetrics-artifact-evaluation .agents/skills/sigmetrics-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 "sigmetrics-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGMETRICS-Skills/skills/sigmetrics-artifact-evaluation into .agents/skills/sigmetrics-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sigmetrics-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 sigmetrics-artifact-evaluation -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sigmetrics-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/SIGMETRICS-Skills/skills/sigmetrics-artifact-evaluation .cursor/skills/sigmetrics-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 "sigmetrics-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGMETRICS-Skills/skills/sigmetrics-artifact-evaluation into .cursor/skills/sigmetrics-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sigmetrics-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 SIGMETRICS-Skills/skills/sigmetrics-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 sigmetrics-artifact-evaluation -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sigmetrics-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/SIGMETRICS-Skills/skills/sigmetrics-artifact-evaluation .gemini/skills/sigmetrics-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 "sigmetrics-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGMETRICS-Skills/skills/sigmetrics-artifact-evaluation into .gemini/skills/sigmetrics-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sigmetrics-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 sigmetrics-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 sigmetrics-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/SIGMETRICS-Skills/skills/sigmetrics-artifact-evaluation .github/skills/sigmetrics-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 "sigmetrics-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGMETRICS-Skills/skills/sigmetrics-artifact-evaluation into .github/skills/sigmetrics-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sigmetrics-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 sigmetrics-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 sigmetrics-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/SIGMETRICS-Skills/skills/sigmetrics-artifact-evaluation .opencode/skills/sigmetrics-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 "sigmetrics-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGMETRICS-Skills/skills/sigmetrics-artifact-evaluation into .opencode/skills/sigmetrics-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sigmetrics-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
sigmetrics-artifact-evaluation- GitHub stars
- 1.2k
- Token cost
- ~1.4k tokens
- SKILL.md length
- 540 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 ACM SIGMETRICS artifact for the ACM Artifact Review and Badging scheme (Artifacts Available, Evaluated Functional and Reusable, Results Reproduced)…
- Packaging an ACM SIGMETRICS artifact for the ACM Artifact Review and Badging scheme (Artifacts Available
- SKILL.md covers The ACM badges (verify the…, What performance-evaluation…, Packaging plan and Anonymized review artifact vs.…, plus 3 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Evaluated Functional and Reusable
What it does
Sigmetrics Artifact Evaluation is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when packaging an ACM SIGMETRICS artifact for the ACM Artifact Review and Badging scheme (Artifacts Available, Evaluated Functional and Reusable, Results Reproduced), covering what performance-evaluation evaluators check first (does the simulation regenerate the figures and match the analysis?), DOI-issuing archives, evaluator-proof documentation, and confirming whether an artifact track runs this cycle.
Its SKILL.md is about 1.4k 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 ACM SIGMETRICS artifact for the ACM Artifact Review and Badging scheme (Artifacts Available
- Evaluated Functional and Reusable
- Results Reproduced)
- Covering what performance-evaluation evaluators check first (does the simulation regenerate the figures and match the analysis?)
Example prompts
- “/sigmetrics-artifact-evaluation”
Requirements
- 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.
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
Sigmetrics Artifact Evaluation loads about 1.4k tokens when it runs. Until then it costs about 111 tokens; SKILL.md has 540 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). 540 words, ~1,444 tokens.
Download SKILL.mdSave it as
.claude/skills/sigmetrics-artifact-evaluation/SKILL.md (or your agent's skills folder).- name
- sigmetrics-artifact-evaluation
- description
- Use when packaging an ACM SIGMETRICS artifact for the ACM Artifact Review and Badging scheme (Artifacts Available, Evaluated Functional and Reusable, Results Reproduced), covering what performance-evaluation evaluators check first (does the simulation regenerate the figures and match the analysis?), DOI-issuing archives, evaluator-proof documentation, and confirming whether an artifact track runs this cycle.
SIGMETRICS Artifact Evaluation
Use this for artifact/reproducibility packaging. SIGMETRICS sits in the ACM ecosystem and, where an artifact track runs, follows the ACM Artifact Review and Badging scheme. Two things to internalize: badges are earned by evaluators actually using your package, and — distinctively for SIGMETRICS — the package usually has to let an evaluator regenerate the figures from a seeded simulation and see them match the analytic prediction, not only run a tool. Confirm on the current cycle whether a formal artifact track exists and its timing (待核实).
The ACM badges (verify the current set and names)
| Badge | What it certifies | What earns it |
|---|---|---|
| Artifacts Available | The artifact is permanently, publicly retrievable | Deposit in a DOI-issuing archive (Zenodo, figshare, Software Heritage) |
| Artifacts Evaluated - Functional | The artifact runs and does what the paper says | A clean-machine install, a demo run of the simulator, documented expected outputs |
| Artifacts Evaluated - Reusable | Others can build on it | The Functional bar plus careful docs, structure, and licensing |
| Results Reproduced | An evaluator reproduced the paper's key results | A turnkey path from the artifact to the headline figures/numbers (and the analytic overlay) |
Available is a low-cost, high-value badge (archive the package); Functional/Reusable/Reproduced require the evaluator's own run to succeed, so the failure mode is "did not run / figures did not match on their machine," never "the theorem was weak."
What performance-evaluation evaluators open first
| Claim type | First thing inspected | Common failure caught |
|---|---|---|
| An analytic bound + simulation | The script that regenerates the analysis-vs-simulation figure | Figure hard-coded; simulator does not actually produce the plotted curve |
| A measurement study | The scripts that turn the trace into the paper's tables | Numbers in the PDF that no script reproduces; trace missing |
| A scheduling/queueing policy | The seeded simulator and its steady-state handling | Non-deterministic runs; no seeds; warm-up not handled |
| A learning-for-systems result | Code plotting empirical regret against the proven bound | Requires unavailable data; guarantee not empirically checked |
Assume an evaluator gives your package a bounded time budget on a clean machine. Design for the first ten minutes to succeed: a small demo that regenerates one headline figure quickly.
Show full SKILL.md (196 more words)Show less
Packaging plan
[Container] ship a Dockerfile or a pinned environment (requirements/lockfile); avoid
"install these 40 things by hand"
[README] one-screen orientation: what the model is, how to install, how to run the simulator
demo, how to regenerate each figure, expected runtime and outputs
[Mapping] an explicit table: paper claim/figure -> script -> expected result (incl. the
analytic overlay it should match)
[Simulator] seeded, steady-state-aware; a fast demo config and the full (slow) config
[Data] the processed trace/dataset (or documented access), not just the collection query
[Proofs] the derivation appendix, so the analytic side is checkable alongside the code
[License] an OSI-approved license so the artifact can be badged Reusable
[Archive] deposit in a DOI-issuing repository for the Available badgeAnonymized review artifact vs. badge artifact
- At submission: the artifact is anonymized for the paper's reviewers — no owner strings, cluster paths, group names, or identity-revealing trace provenance (Operational Systems Track excepted).
- After acceptance: replace anonymized placeholders with the public, licensed, DOI-issuing archive; this is the version the badges attach to and the POMACS camera-ready cites.
Worked vignette: packaging a scheduling-policy paper
A paper contributes a scheduling theorem and a trace-driven evaluation. To target Reusable and
Reproduced: ship a Docker image with the simulator pre-built; a run_demo.sh that regenerates the
analysis-vs-simulation figure on a small config in under a minute; a reproduce/ directory whose
scripts regenerate each table and the trace-driven comparison from logged runs; a
claim-to-figure-to-script mapping in the README; the processed trace with pinned provenance; the
proof appendix; and an MIT/Apache license. State honestly which figures are turnkey and which need
the full (slow) simulation sweep.
Calibration
- Whether an artifact/reproducibility track runs a given cycle, its badges, timing, and whether it is mandatory or optional — all cycle-volatile; confirm on the current call (待核实).
- For POMACS, badges are typically pursued around/after acceptance; do not conflate the artifact process with the paper's shepherding.
Output format
[Target badges] Available / Functional / Reusable / Reproduced
[Artifact role] anonymized review artifact / public badge artifact
[Contents] <model/simulator/trace/proofs/provenance/license>
[Ten-minute test] does install + demo regenerate one headline figure on a clean machine? yes/no
[Claim mapping] <claim/figure -> script -> expected result (matches analysis?) present? yes/no>
[Fixes before upload] <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 SIGMETRICS-Skills/skills/sigmetrics-artifact-evaluation of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Sigmetrics 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 |
|---|---|---|---|---|---|---|
| Sigmetrics Artifact Evaluation this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.4k | Automated safety check: Pass | MIT | |
| Arize Evaluatorgithub/awesome-copilot | 40k | 1 repos | ~8.1k | Automated safety check: Notes | MIT | |
| Ccs Artifact Evaluationbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~969 | Automated safety check: Pass | MIT | |
| Sigcomm Artifact Evaluationbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.1k | Automated safety check: Pass | MIT | |
| Socc Artifact Evaluationbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.5k | Automated safety check: Pass | MIT | |
| Mobisys Artifact Evaluationbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1k | Automated safety check: Pass | MIT |
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Questions about Sigmetrics Artifact Evaluation
What does Sigmetrics Artifact Evaluation do?
A skill your agent uses when packaging an ACM SIGMETRICS artifact for the ACM Artifact Review and Badging scheme (Artifacts Available, Evaluated Functional and Reusable, Results Reproduced)…. Sigmetrics Artifact Evaluation is an agent skill from brycewang-stanford/Awesome-Journal-Skills.), DOI-issuing archives, evaluator-proof documentation, and confirming whether an artifact track runs this cycle.
When should I use Sigmetrics Artifact Evaluation?
Sigmetrics Artifact Evaluation fits situations like: packaging an ACM SIGMETRICS artifact for the ACM Artifact Review and Badging scheme (Artifacts Available; evaluated Functional and Reusable; results Reproduced); covering what performance-evaluation evaluators check first (does the simulation regenerate the figures and match the analysis?).
How do I install Sigmetrics Artifact Evaluation in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sigmetrics-artifact-evaluation -a claude-code`. Or copy the skill folder (SIGMETRICS-Skills/skills/sigmetrics-artifact-evaluation in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/sigmetrics-artifact-evaluation in your project. Claude Code loads it when a task matches its description.
How do I install Sigmetrics Artifact Evaluation in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sigmetrics-artifact-evaluation -a codex`. Or copy the skill folder (SIGMETRICS-Skills/skills/sigmetrics-artifact-evaluation in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/sigmetrics-artifact-evaluation in your project. Codex loads it when a task matches its description.
Can I use Sigmetrics 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 sigmetrics-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/sigmetrics-artifact-evaluation, .gemini/skills/sigmetrics-artifact-evaluation, .github/skills/sigmetrics-artifact-evaluation and .opencode/skills/sigmetrics-artifact-evaluation in your project.
What does Sigmetrics Artifact Evaluation need to run?
SKILL.md names no scripts, command-line tools or credentials: Sigmetrics Artifact Evaluation is instructions for the agent only. Our summary lists: Docker.
Does Sigmetrics 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 Sigmetrics 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 Sigmetrics Artifact Evaluation use?
Sigmetrics 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 Sigmetrics Artifact Evaluation use?
About 1.4k tokens (SKILL.md is roughly 5.8k 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 Sigmetrics Artifact Evaluation?
Skills that share tags, products or a category with Sigmetrics Artifact Evaluation: Arize Evaluator (github/awesome-copilot, 40k stars), Ccs Artifact Evaluation (brycewang-stanford/Awesome-Journal-Skills, 1.2k stars), Sigcomm Artifact Evaluation (brycewang-stanford/Awesome-Journal-Skills, 1.2k stars) and Socc 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 Sigmetrics 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.