HyperFrames Animation
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Agent skill
Cvpr Artifact Evaluation
by brycewang-stanford in brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses 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…
Install Cvpr Artifact Evaluation
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cvpr-artifact-evaluation -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills cvpr-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/CVPR-Skills/skills/cvpr-artifact-evaluation .claude/skills/cvpr-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 "cvpr-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CVPR-Skills/skills/cvpr-artifact-evaluation into .claude/skills/cvpr-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cvpr-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/CVPR-Skills/skills/cvpr-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 cvpr-artifact-evaluation -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills cvpr-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/CVPR-Skills/skills/cvpr-artifact-evaluation .agents/skills/cvpr-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 "cvpr-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CVPR-Skills/skills/cvpr-artifact-evaluation into .agents/skills/cvpr-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cvpr-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 cvpr-artifact-evaluation -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills cvpr-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/CVPR-Skills/skills/cvpr-artifact-evaluation .cursor/skills/cvpr-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 "cvpr-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CVPR-Skills/skills/cvpr-artifact-evaluation into .cursor/skills/cvpr-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cvpr-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 CVPR-Skills/skills/cvpr-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 cvpr-artifact-evaluation -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills cvpr-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/CVPR-Skills/skills/cvpr-artifact-evaluation .gemini/skills/cvpr-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 "cvpr-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CVPR-Skills/skills/cvpr-artifact-evaluation into .gemini/skills/cvpr-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cvpr-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 cvpr-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 cvpr-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/CVPR-Skills/skills/cvpr-artifact-evaluation .github/skills/cvpr-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 "cvpr-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CVPR-Skills/skills/cvpr-artifact-evaluation into .github/skills/cvpr-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cvpr-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 cvpr-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 cvpr-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/CVPR-Skills/skills/cvpr-artifact-evaluation .opencode/skills/cvpr-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 "cvpr-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CVPR-Skills/skills/cvpr-artifact-evaluation into .opencode/skills/cvpr-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cvpr-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
cvpr-artifact-evaluation- GitHub stars
- 1.2k
- Token cost
- ~1.7k tokens
- SKILL.md length
- 769 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, models, datasets, or demo videos for a CVPR paper at either review time or release time, covering anonymous supplement packaging under the external-link…
- Demo videos for a CVPR paper at either review time
- SKILL.md covers Review-time artifacts:…, The runnable-by-a-stranger bar, Release-time artifacts:… and Weights and hosting decisions, plus 4 more sections
- Calls git
- Covering anonymous supplement packaging under the external-link ban
What it does
Cvpr Artifact Evaluation is an agent skill from brycewang-stanford/Awesome-Journal-Skills. 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.
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.
It sits in Media & Creative, covering Video production. 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
- Demo videos for a CVPR paper at either review time
- Covering anonymous supplement packaging under the external-link ban
- The dataset-release-by-camera-ready rule
- Model-weight and license decisions
Example prompts
- “/cvpr-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
Shell commands in SKILL.md call:
git
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use git, which can reach the network depending on how they are called.
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
Cvpr Artifact Evaluation loads about 1.7k tokens when it runs. Until then it costs about 87 tokens; SKILL.md has 769 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). 769 words, ~1,690 tokens.
Download SKILL.mdSave it as
.claude/skills/cvpr-artifact-evaluation/SKILL.md (or your agent's skills folder).- name
- cvpr-artifact-evaluation
- description
- 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.
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
.githistory, 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 more often than PDFs do. - The 2026 supplementary size cap was not verifiable at check time (待核实); design the package assuming a strict cap, then confirm on the current page.
# 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 inThe runnable-by-a-stranger bar
A reviewer who opens your archive gives it minutes, not an afternoon. One entry point, one config, one expected number:
| Artifact layer | Minimum viable | Gold standard |
|---|---|---|
| Environment | pinned requirements.txt + CUDA/driver note | Dockerfile reproducing the CRF hardware row |
| Inference | script + 5 sample images + expected outputs | notebook rendering figure-quality results |
| Training | full config + command + seed | resumable run with logged curves |
| Evaluation | script that recomputes one main-table row | all-tables harness with dataset download stubs |
| Data | loader + split files + provenance note | checksummed archive or release plan with license |
The single highest-leverage file is REPRODUCE.md mapping each table/figure in the
paper to one command. It converts a suspicious reviewer into a supportive one faster
than any rebuttal sentence.
Release-time artifacts: promises come due
Two clocks start at acceptance. First, the verified dataset clause: a dataset claimed as a contribution must be public by the camera-ready deadline — hosting, license, and consent scrubbing included. Second, the softer but real credibility clock: the gap between "code coming soon" in the README and actual code is measured publicly at CVPR scale.
Release decisions to make explicitly rather than by default:
- License: weights and data need one (CC-BY-SA? research-only? non-commercial?); "no license" means legally unusable for the industrial half of the CVPR audience.
- Weights: which checkpoints — final only, or the ablation grid reviewers saw?
- Provenance: for scraped or generated data, document sources and filtering; for human data, document consent posture. Vision datasets attract scrutiny years later.
- Benchmarks: if you release an evaluation, freeze the metric code and version it — silent metric fixes fork the leaderboard.
Show full SKILL.md (318 more words)Show less
Weights and hosting decisions
Model releases have their own failure modes at vision scale:
- Host durability: lab web servers die with graduations. Prefer archival or platform hosting (institutional repositories, model hubs) with a checksum published in the README; the CVF open-access page will outlive your URL, so choose hosts with the same life expectancy.
- Checkpoint provenance: name each released checkpoint after the paper's table row
it reproduces (
table2_row4_vitb.pth), and state which exact code commit evaluates it to the printed number. - Safety posture for generative models: decide before release what you ship — weights, LoRA deltas, or inference-only API — and write the model card's misuse paragraph yourself rather than letting the first misuse write it for you.
- Version pinning at release: tag the repo at camera-ready (
v1.0-cvpr) so later refactors don't silently break the commands printed in the paper's supplement.
Anti-patterns with CVPR-specific cost
- A project-page URL in the submission "for videos" — that is the banned external link, not a convenience.
- Supplement code that hard-codes
/home/<name>/data— an anonymity leak and proof nobody ran it elsewhere. - Claiming a dataset contribution, then releasing it months after camera-ready — a verified policy breach, not just bad manners.
- Releasing training code without evaluation code: at a benchmark-driven venue the evaluation is the artifact people actually reuse.
After release: the maintenance tail
A used artifact generates issues, and the field reads your issue tracker. Budget a
small maintenance window post-conference: pin the environment against dependency rot,
answer the first wave of "can't reproduce Table 2" issues (usually environment
mismatches — point to the Dockerfile), and keep a RESULTS.md of community
reproductions. Six months of light maintenance is what turns a CVPR paper into the
baseline the next cycle's papers must cite.
Reverify each cycle
- Supplement size/format caps and whether anonymized-repo links are (dis)allowed — the external-link wording can shift by year.
- The dataset-release clause wording.
- Any new artifact, checklist, or reproducibility fields on the OpenReview form.
Output format
[Artifact stage] review-supplement / camera-ready-release
[Identity scan] clean / hits: <files>
[Runnability] entrypoint · pins · expected-output map present?
[Weights & data plan] <ship now / release plan + license>
[Dataset clause] n/a / due at camera-ready: <status>
[Gaps] <ordered fixes>© 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 CVPR-Skills/skills/cvpr-artifact-evaluation of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Cvpr 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 |
|---|---|---|---|---|---|---|
| Cvpr Artifact Evaluation this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.7k | Automated safety check: Pass | MIT | |
| HyperFrames Animationheygen-com/hyperframes | 60k | 3 repos | ~2.1k | Automated safety check: Pass | Apache-2.0 | |
| Stitch to Remotion Walkthrough Videosgoogle-labs-code/stitch-skills | 8.5k | 6 repos | ~3.2k | Automated safety check: Notes | Apache-2.0 | |
| Faceless Explainer Videoheygen-com/hyperframes | 60k | 3 repos | ~7.7k | Automated safety check: Notes | Apache-2.0 | |
| Video Understandcalesthio/OpenMontage | 66k | — | ~841 | Automated safety check: Pass | AGPL-3.0 | |
| Video ShotcraftVincentwei1021/video-shotcraft | 11k | — | ~2.6k | Automated safety check: Pass | Apache-2.0 |
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Video Understand
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Questions about Cvpr Artifact Evaluation
What does Cvpr Artifact Evaluation do?
A skill your agent uses 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…. Cvpr Artifact Evaluation is an agent skill from brycewang-stanford/Awesome-Journal-Skills. 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.
When should I use Cvpr Artifact Evaluation?
Cvpr Artifact Evaluation fits situations like: demo videos for a CVPR paper at either review time; covering anonymous supplement packaging under the external-link ban; the dataset-release-by-camera-ready rule; model-weight and license decisions.
How do I install Cvpr Artifact Evaluation in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cvpr-artifact-evaluation -a claude-code`. Or copy the skill folder (CVPR-Skills/skills/cvpr-artifact-evaluation in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/cvpr-artifact-evaluation in your project. Claude Code loads it when a task matches its description.
How do I install Cvpr Artifact Evaluation in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cvpr-artifact-evaluation -a codex`. Or copy the skill folder (CVPR-Skills/skills/cvpr-artifact-evaluation in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/cvpr-artifact-evaluation in your project. Codex loads it when a task matches its description.
Can I use Cvpr 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 cvpr-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/cvpr-artifact-evaluation, .gemini/skills/cvpr-artifact-evaluation, .github/skills/cvpr-artifact-evaluation and .opencode/skills/cvpr-artifact-evaluation in your project.
What does Cvpr Artifact Evaluation need to run?
Going by SKILL.md and its folder, Cvpr Artifact Evaluation needs the command-line tools its instructions call (git).
Does Cvpr Artifact Evaluation access the network?
SKILL.md contains no URLs. Its commands use git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Is Cvpr 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 Cvpr Artifact Evaluation use?
Cvpr 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 Cvpr 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 Cvpr Artifact Evaluation?
Skills that share tags, products or a category with Cvpr Artifact Evaluation: HyperFrames Animation (heygen-com/hyperframes, 60k stars), Stitch to Remotion Walkthrough Videos (google-labs-code/stitch-skills, 8.5k stars), Faceless Explainer Video (heygen-com/hyperframes, 60k stars) and Video Understand (calesthio/OpenMontage, 66k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Cvpr 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.