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
Wacv Artifact Evaluation
by brycewang-stanford in brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when packaging code, data, and models for a WACV paper, covering the anonymous review artifact versus the public post-acceptance release, reproducing constraint-based…
MITAuto-check passed
Install Wacv Artifact Evaluation
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill wacv-artifact-evaluation -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills wacv-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/WACV-Skills/skills/wacv-artifact-evaluation .claude/skills/wacv-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 "wacv-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/WACV-Skills/skills/wacv-artifact-evaluation into .claude/skills/wacv-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wacv-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/WACV-Skills/skills/wacv-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 wacv-artifact-evaluation -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills wacv-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/WACV-Skills/skills/wacv-artifact-evaluation .agents/skills/wacv-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 "wacv-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/WACV-Skills/skills/wacv-artifact-evaluation into .agents/skills/wacv-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wacv-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 wacv-artifact-evaluation -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills wacv-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/WACV-Skills/skills/wacv-artifact-evaluation .cursor/skills/wacv-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 "wacv-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/WACV-Skills/skills/wacv-artifact-evaluation into .cursor/skills/wacv-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wacv-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 WACV-Skills/skills/wacv-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 wacv-artifact-evaluation -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills wacv-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/WACV-Skills/skills/wacv-artifact-evaluation .gemini/skills/wacv-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 "wacv-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/WACV-Skills/skills/wacv-artifact-evaluation into .gemini/skills/wacv-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wacv-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 wacv-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 wacv-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/WACV-Skills/skills/wacv-artifact-evaluation .github/skills/wacv-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 "wacv-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/WACV-Skills/skills/wacv-artifact-evaluation into .github/skills/wacv-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wacv-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 wacv-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 wacv-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/WACV-Skills/skills/wacv-artifact-evaluation .opencode/skills/wacv-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 "wacv-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/WACV-Skills/skills/wacv-artifact-evaluation into .opencode/skills/wacv-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wacv-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
wacv-artifact-evaluation- GitHub stars
- 1.2k
- Token cost
- ~938 tokens
- SKILL.md length
- 370 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when packaging code, data, and models for a WACV paper, covering the anonymous review artifact versus the public post-acceptance release, reproducing constraint-based…
- Models for a WACV paper
- SKILL.md covers Two artifacts, two audiences, Reproduce the claim, not just…, Datasets and licensing and Sync across the two rounds, plus 2 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Covering the anonymous review artifact versus the public post-acceptance release
What it does
Wacv Artifact Evaluation is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when packaging code, data, and models for a WACV paper, covering the anonymous review artifact versus the public post-acceptance release, reproducing constraint-based applications claims (latency, power, robustness) not just accuracy, dataset licensing and release, and keeping the artifact in sync across the two-round Revise-and-Resubmit lap.
Its SKILL.md is about 940 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
- Models for a WACV paper
- Covering the anonymous review artifact versus the public post-acceptance release
- Reproducing constraint-based applications claims (latency
- Robustness) not just accuracy
Example prompts
- “/wacv-artifact-evaluation”
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
Wacv Artifact Evaluation loads about 938 tokens when it runs. Until then it costs about 93 tokens; SKILL.md has 370 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). 370 words, ~938 tokens.
Download SKILL.mdSave it as
.claude/skills/wacv-artifact-evaluation/SKILL.md (or your agent's skills folder).- name
- wacv-artifact-evaluation
- description
- Use when packaging code, data, and models for a WACV paper, covering the anonymous review artifact versus the public post-acceptance release, reproducing constraint-based applications claims (latency, power, robustness) not just accuracy, dataset licensing and release, and keeping the artifact in sync across the two-round Revise-and-Resubmit lap.
WACV Artifact Evaluation
Use this to build the two artifacts a WACV paper needs: a sealed anonymous package for review and a public release after acceptance. WACV's applications framing means the artifact must let a reviewer reproduce a deployed claim, and the two-round model means the artifact must survive a revision. Facts are the WACV 2026/2027 cycles as read on 2026-07-09.
Two artifacts, two audiences
| Anonymous review artifact | Public post-acceptance release | |
|---|---|---|
| Audience | Double-blind reviewers | The community, via CVF open access + IEEE Xplore paper |
| Identity | Fully anonymized: no author names, repos, or org strings | De-anonymized; real repo, license, and citation |
| Contents | Enough to reproduce the headline claims | Full code, weights, and dataset (or access instructions) |
| Timing | With the submission / supplement | By the camera-ready obligation (see wacv-camera-ready) |
Do not ship the review artifact with a link to a named GitHub repo or a project page — that breaks double-blind. Ship the code and a runnable path inside the anonymized package.
Reproduce the claim, not just the metric
For an Applications-track paper the headline is usually a constraint ("runs at 2 W on device D under sub-10-lux"), and an artifact that only reproduces an accuracy number does not support that claim. Include what a reviewer needs to check the systems result:
Applications artifact must let a reviewer:
1. Run the model and reproduce the headline metric within the stated spread.
2. Measure (or see logged) the constraint: latency/wattage/memory on the named device.
3. Re-run at least one baseline under the same constraint, to confirm the comparison.
4. Do all of this without learning who the authors are.Show full SKILL.md (163 more words)Show less
Datasets and licensing
If a dataset is a claimed contribution, plan its public availability for the release and state the license (and any collection/consent basis for field or human data). In the anonymous artifact, provide the data or a de-identified sample that reproduces the reported rows without revealing the collecting institution. Do not defer the license decision to the last day — an unlicensed dataset is not really released.
Sync across the two rounds
A Revise-and-Resubmit revision often adds an experiment or re-tunes a baseline; the artifact must move with it. Before resubmitting to Round 2, re-run the reproduction on the revised claims and diff the artifact's outputs against the revised paper. A reviewer who finds the Round 2 artifact reproducing the Round 1 numbers will read it as an unfinished revision.
Reverify each cycle
- The camera-ready dataset/code-release obligation and its deadline.
- Anonymity rules for artifacts submitted with review (待核实 size/format caps for 2026).
- Any license or ethics requirements for released data.
Output format
[Artifact stage] anonymous review / public release
[Anonymized] no names/repos/org strings in review artifact: yes/no
[Claim reproducible] headline metric within spread: yes/no
[Constraint reproducible] latency/power/memory checkable on named device: yes/no
[Baseline] at least one baseline re-runnable under the constraint: yes/no
[Dataset] license + public-availability plan set: yes/no
[Round sync] artifact matches revised paper: yes/no© 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 WACV-Skills/skills/wacv-artifact-evaluation of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Wacv 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 |
|---|---|---|---|---|---|---|
| Wacv Artifact Evaluation this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~938 | 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 | |
| Mobisys Artifact Evaluationbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1k | Automated safety check: Pass | MIT | |
| Fast Artifact Evaluationbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.6k | Automated safety check: Pass | MIT |
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Questions about Wacv Artifact Evaluation
What does Wacv Artifact Evaluation do?
A skill your agent uses when packaging code, data, and models for a WACV paper, covering the anonymous review artifact versus the public post-acceptance release, reproducing constraint-based…. Wacv Artifact Evaluation is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when packaging code, data, and models for a WACV paper, covering the anonymous review artifact versus the public post-acceptance release, reproducing constraint-based applications claims (latency, power, robustness) not just accuracy, dataset licensing and release, and keeping the artifact in sync across the two-round Revise-and-Resubmit lap.
When should I use Wacv Artifact Evaluation?
Wacv Artifact Evaluation fits situations like: models for a WACV paper; covering the anonymous review artifact versus the public post-acceptance release; reproducing constraint-based applications claims (latency; robustness) not just accuracy.
How do I install Wacv Artifact Evaluation in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill wacv-artifact-evaluation -a claude-code`. Or copy the skill folder (WACV-Skills/skills/wacv-artifact-evaluation in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/wacv-artifact-evaluation in your project. Claude Code loads it when a task matches its description.
How do I install Wacv Artifact Evaluation in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill wacv-artifact-evaluation -a codex`. Or copy the skill folder (WACV-Skills/skills/wacv-artifact-evaluation in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/wacv-artifact-evaluation in your project. Codex loads it when a task matches its description.
Can I use Wacv 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 wacv-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/wacv-artifact-evaluation, .gemini/skills/wacv-artifact-evaluation, .github/skills/wacv-artifact-evaluation and .opencode/skills/wacv-artifact-evaluation in your project.
What does Wacv Artifact Evaluation need to run?
SKILL.md names no scripts, command-line tools or credentials: Wacv Artifact Evaluation is instructions for the agent only.
Does Wacv 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 Wacv 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 Wacv Artifact Evaluation use?
Wacv 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 Wacv Artifact Evaluation use?
About 938 tokens (SKILL.md is roughly 3.8k 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 Wacv Artifact Evaluation?
Skills that share tags, products or a category with Wacv 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 Mobisys 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 Wacv 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.