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
Fast Artifact Evaluation
by brycewang-stanford in brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when packaging a USENIX FAST artifact for the USENIX Artifact Evaluation scheme (Artifacts Available, Artifacts Functional, Results Reproduced), covering what a storage AEC…
MITAuto-check passed
Install Fast Artifact Evaluation
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill fast-artifact-evaluation -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills fast-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/FAST-Skills/skills/fast-artifact-evaluation .claude/skills/fast-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 "fast-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/FAST-Skills/skills/fast-artifact-evaluation into .claude/skills/fast-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fast-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/FAST-Skills/skills/fast-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 fast-artifact-evaluation -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills fast-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/FAST-Skills/skills/fast-artifact-evaluation .agents/skills/fast-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 "fast-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/FAST-Skills/skills/fast-artifact-evaluation into .agents/skills/fast-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fast-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 fast-artifact-evaluation -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills fast-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/FAST-Skills/skills/fast-artifact-evaluation .cursor/skills/fast-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 "fast-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/FAST-Skills/skills/fast-artifact-evaluation into .cursor/skills/fast-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fast-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 FAST-Skills/skills/fast-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 fast-artifact-evaluation -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills fast-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/FAST-Skills/skills/fast-artifact-evaluation .gemini/skills/fast-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 "fast-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/FAST-Skills/skills/fast-artifact-evaluation into .gemini/skills/fast-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fast-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 fast-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 fast-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/FAST-Skills/skills/fast-artifact-evaluation .github/skills/fast-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 "fast-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/FAST-Skills/skills/fast-artifact-evaluation into .github/skills/fast-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fast-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 fast-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 fast-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/FAST-Skills/skills/fast-artifact-evaluation .opencode/skills/fast-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 "fast-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/FAST-Skills/skills/fast-artifact-evaluation into .opencode/skills/fast-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fast-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
fast-artifact-evaluation- GitHub stars
- 1.2k
- Token cost
- ~1.6k tokens
- SKILL.md length
- 553 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when packaging a USENIX FAST artifact for the USENIX Artifact Evaluation scheme (Artifacts Available, Artifacts Functional, Results Reproduced), covering what a storage AEC…
- Packaging a USENIX FAST artifact for the USENIX Artifact Evaluation scheme (Artifacts Available
- SKILL.md covers The three USENIX badges…, The storage-artifact problem:…, What a storage AEC opens first and Packaging plan, plus 4 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Artifacts Functional
What it does
Fast Artifact Evaluation is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when packaging a USENIX FAST artifact for the USENIX Artifact Evaluation scheme (Artifacts Available, Artifacts Functional, Results Reproduced), covering what a storage AEC checks first, DOI-issuing archives, the artifact appendix, and the special challenges of storage artifacts that need specific devices, large traces, or long endurance runs on the separate post-acceptance timeline.
Its SKILL.md is about 1.6k 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 a USENIX FAST artifact for the USENIX Artifact Evaluation scheme (Artifacts Available
- Artifacts Functional
- Results Reproduced)
- Covering what a storage AEC checks first
Example prompts
- “/fast-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
Fast Artifact Evaluation loads about 1.6k tokens when it runs. Until then it costs about 104 tokens; SKILL.md has 553 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). 553 words, ~1,572 tokens.
Download SKILL.mdSave it as
.claude/skills/fast-artifact-evaluation/SKILL.md (or your agent's skills folder).- name
- fast-artifact-evaluation
- description
- Use when packaging a USENIX FAST artifact for the USENIX Artifact Evaluation scheme (Artifacts Available, Artifacts Functional, Results Reproduced), covering what a storage AEC checks first, DOI-issuing archives, the artifact appendix, and the special challenges of storage artifacts that need specific devices, large traces, or long endurance runs on the separate post-acceptance timeline.
FAST Artifact Evaluation
Use this for the artifact track. FAST follows the USENIX artifact-evaluation model, and evaluation is a separate, post-acceptance process with its own timeline and an Artifact Evaluation Committee (AEC). Two things to internalize: badges are earned by evaluators actually using your package, and the review-time artifact (anonymized, for the paper's reviewers) is not the same deliverable as the badge artifact (de-anonymized, permanently archived, with an artifact appendix).
The three USENIX 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 Functional | The artifact works and is useful for the paper's outcomes | A documented build/run, a demo, and expected outputs on the AEC's setup |
| Results Reproduced | An evaluator reproduced the paper's main results | A path from the artifact to the headline numbers |
Available is a low-cost, high-value badge (archive the package). Functional and Reproduced require the evaluator's own run to succeed — and for a storage paper that run may need specific hardware, which shapes everything below.
The storage-artifact problem: hardware dependence
A storage result often depends on a particular SSD/NVM device, firmware, or a large trace. Design the package so an AEC can get as far as possible without your exact drive:
[Tier the claims] which results are hardware-agnostic (analysis, plots from logs) vs. device-bound
(measured WA, tail latency)? Make the agnostic ones turnkey.
[Ship the logs] include the raw device-counter logs and the scripts that turn them into figures,
so "Reproduced" is achievable from data even when the device is unavailable
[Document devices] state exact models + firmware; where feasible, offer a smaller commodity-SSD path
the AEC can run to see the trend, and say the factor is device-specific
[Bound the runtime] long endurance/aging runs: provide a short demo + the full slow protocol, and
label which results need the full runWhat a storage AEC opens first
| Claim type | First thing inspected | Common failure caught |
|---|---|---|
| A storage system/tool | The README and one build+run command | Undocumented deps; only-builds-on-authors'-kernel |
| A measured storage cost (WA/latency) | The logs + the script from logs to the figure | Numbers in the PDF no script regenerates |
| A trace-driven study | The replay tool + the archived trace | Trace named, not shipped; replay settings missing |
| A crash-consistency claim | The fault-injection / replay harness | Only the FS code, no way to reproduce the test |
| A reliability field study | The (de-identified) dataset + analysis scripts | Aggregates only; nothing an evaluator can re-run |
Assume an evaluator gives your package a bounded time budget. Design for the first ten minutes (build
- a small demo + a figure-from-logs) to succeed.
Show full SKILL.md (217 more words)Show less
Packaging plan
[Container] ship a Dockerfile or a pinned environment; avoid "install these 40 things by hand"
[README] one screen: what it is, build, run the demo, reproduce each claim, expected runtime/outputs
[Mapping] an explicit table: paper claim -> script -> expected result (mark device-bound ones)
[Data] the archived traces and raw device-counter logs, not just names
[Provenance] device models + firmware, host/kernel, mkfs/mount options, seeds
[License] an OSI-approved license so the artifact can be evaluated toward Functional/Reproduced
[Archive] deposit in a DOI-issuing repository for the Available badge
[Appendix] write the artifact appendix; add the earned badges to the camera-ready appendix after AEAnonymized review artifact vs. badge artifact
- At submission: anonymized for the paper's reviewers — no owner strings, hostnames, cluster paths, serials in device dumps, lab names, or identity-revealing trace hosts.
- After acceptance: replace anonymized placeholders with the public, licensed, DOI-issuing archive; write the artifact appendix; this is the version the AEC badges and the camera-ready cites, with badges shown on the first page.
Worked vignette: packaging a KV-store + measurement study
A paper contributes an SSD-conscious KV store and a write-amplification study. To target Reproduced:
ship a Docker image with the store pre-built; a run_demo.sh that runs a short YCSB load on a
commodity SSD (or a loopback file) in minutes; a reproduce/ directory whose scripts regenerate each
figure from the shipped device-counter logs so the headline numbers reproduce even without the
exact datacenter SSD; a claim-to-script table marking which results are device-bound; the archived
trace with replay scripts; and an MIT/Apache license. State honestly which numbers are the specific
drive's and which are the trend.
Calibration
- The artifact timeline is after acceptance and independent of the camera-ready deadline; do not conflate them.
- Badge names, the exact set offered, and whether AE is single- or double-anonymous vary by cycle (待核实 for the precise FAST '27 AEC anonymity model) — confirm on the current Call for Artifacts.
Output format
[Target badges] Available / Functional / Reproduced
[Artifact role] anonymized review artifact / public badge artifact
[Contents] <system/traces/device-logs/scripts/provenance/license>
[Hardware tiering] <which claims turnkey-from-logs vs. device-bound; demo path present?>
[Ten-minute test] does build + demo + a figure-from-logs succeed on a clean machine? 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 FAST-Skills/skills/fast-artifact-evaluation of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Fast 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 |
|---|---|---|---|---|---|---|
| Fast Artifact Evaluation this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.6k | Automated safety check: Pass | MIT | |
| Arize Evaluatorgithub/awesome-copilot | 40k | 1 repos | ~8.1k | Automated safety check: Notes | MIT | |
| Artifacts Buildernexu-io/open-design | 100k | — | ~347 | Automated safety check: Pass | Apache-2.0 | |
| Ccs Artifact Evaluationbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~969 | Automated safety check: Pass | MIT | |
| Atc Artifact Evaluationbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.4k | 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 Fast Artifact Evaluation
What does Fast Artifact Evaluation do?
A skill your agent uses when packaging a USENIX FAST artifact for the USENIX Artifact Evaluation scheme (Artifacts Available, Artifacts Functional, Results Reproduced), covering what a storage AEC…. Fast Artifact Evaluation is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when packaging a USENIX FAST artifact for the USENIX Artifact Evaluation scheme (Artifacts Available, Artifacts Functional, Results Reproduced), covering what a storage AEC checks first, DOI-issuing archives, the artifact appendix, and the special challenges of storage artifacts that need specific devices, large traces, or long endurance runs on the separate post-acceptance timeline.
When should I use Fast Artifact Evaluation?
Fast Artifact Evaluation fits situations like: packaging a USENIX FAST artifact for the USENIX Artifact Evaluation scheme (Artifacts Available; artifacts Functional; results Reproduced); covering what a storage AEC checks first.
How do I install Fast Artifact Evaluation in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill fast-artifact-evaluation -a claude-code`. Or copy the skill folder (FAST-Skills/skills/fast-artifact-evaluation in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/fast-artifact-evaluation in your project. Claude Code loads it when a task matches its description.
How do I install Fast Artifact Evaluation in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill fast-artifact-evaluation -a codex`. Or copy the skill folder (FAST-Skills/skills/fast-artifact-evaluation in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/fast-artifact-evaluation in your project. Codex loads it when a task matches its description.
Can I use Fast 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 fast-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/fast-artifact-evaluation, .gemini/skills/fast-artifact-evaluation, .github/skills/fast-artifact-evaluation and .opencode/skills/fast-artifact-evaluation in your project.
What does Fast Artifact Evaluation need to run?
SKILL.md names no scripts, command-line tools or credentials: Fast Artifact Evaluation is instructions for the agent only. Our summary lists: Docker.
Does Fast 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 Fast 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 Fast Artifact Evaluation use?
Fast 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 Fast Artifact Evaluation use?
About 1.6k tokens (SKILL.md is roughly 6.3k 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 Fast Artifact Evaluation?
Skills that share tags, products or a category with Fast Artifact Evaluation: Arize Evaluator (github/awesome-copilot, 40k stars), Artifacts Builder (nexu-io/open-design, 100k stars), Ccs Artifact Evaluation (brycewang-stanford/Awesome-Journal-Skills, 1.2k stars) and Atc 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 Fast 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.