cvpr-artifact-evaluation
Use when packaging code, models, datasets, or demo videos for a CVPR paper at either review time or release time, covering anonymous supplement packaging under the external-link ban, the dataset-release-by-camera-ready rule, model-weight and license decisions, and making a vision artifact runnable by a skeptical stranger.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cvpr-artifact-evaluation --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# CVPR Artifact Evaluation CVPR has no badge-granting artifact committee; the "evaluation" of your artifacts is done informally, twice, by two different audiences. At review time, reviewers poke at an anonymous supplement while deciding whether to trust your tables. After acceptance, the entire field — 2026 drew 44,011 authors alone — decides whether your method becomes a baseline or a citation nobody reproduces. Package for both audiences deliberately. ## Review-time artifacts: sealed-box rules Everything must ride inside the uploaded supplement, because the 2026-verified policy bans external links that expand content or subvert review. That kills the usual anonymous-repo-link workflow many venues tolerate. Practical consequences: - Code goes **in the supplement archive**, scrubbed: no `.git` history, no usernames in paths, no institutional cluster hostnames, no wandb entity names in configs. - Model weights usually exceed sane archive sizes; ship configs + training commands + exact dependency pins instead, and say which checkpoints will be released. - Demo videos are first-class CVPR evidence (see `cvpr-supplementary`) but strip container metadata — video files carry author tags
- Review-time artifacts: sealed-box rules
- The runnable-by-a-stranger bar
- Release-time artifacts: promises come due
- Weights and hosting decisions
- Anti-patterns with CVPR-specific cost
- After release: the maintenance tail
- Reverify each cycle
- Output format
Build a clean review artifact from a working repo git archive --format=tar HEAD | tar -x -C /tmp/artifact # no .git, no untracked junk cd /tmp/artifact grep -rniE "$(whoami)|<lab-name>|<cluster-host>|wandb\.(ai|entity)" . | head # identity scan pip-compile --quiet requirements.in # pin, don't approximate zip -r ../supplement_code.zip . -x '*.ckpt' '*.pth' # weights out, recipe in
What does the cvpr-artifact-evaluation skill do?
Use when packaging code, models, datasets, or demo videos for a CVPR paper at either review time or release time, covering anonymous supplement packaging under the external-link ban, the dataset-release-by-camera-ready rule, model-weight and license decisions, and making a vision artifact runnable by a skeptical stranger.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cvpr-artifact-evaluation --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Awesome-Journal-Skills, a repository with 909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.