Agent skill · Data & Analytics

iccv-artifact-evaluation

Use when packaging code, models, or data for an ICCV paper at review or release time, covering anonymous in-archive code under the do-not-cite-your-repo rule, the June-to-October release runway after acceptance, weight and license decisions in a field whose landmark releases became infrastructure, and reuse-grade packaging.

brycew6m878★ · +32/wk · 1 repos on radarProfile →
claude-codeMIT
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill iccv-artifact-evaluation --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 1
SKILL.md size: 6 KB
Bundled scripts: none
Path: ICCV-Skills/skills/iccv-artifact-evaluation/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 909 · +31 this week
Language: Stata
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# ICCV Artifact Evaluation ICCV grants no artifact badges; the judgment is social and it is severe, because the venue's own history sets the bar. ICCV papers like Mask R-CNN (2017), Swin Transformer (2021), ControlNet (2023), and Segment Anything (2023) are cited as much for their released artifacts as for their PDFs — at this venue, the release *is* part of the contribution's afterlife. Package for two moments: the anonymous review in spring, and the public release whose natural deadline is the October conference. ## Review-time: the sealed archive The 2025 rules shape review packaging in two specific ways. First, do not cite or link your public codebase — anonymity guidance says to write that code "will be made publicly available" instead. Second, paper and supplement land on the same day, so the code package cannot be an afterthought of a post-deadline week (`iccv-supplementary`). Inside the archive: - Ship source, configs, and exact environment pins; leave the weights out unless small — describe which checkpoints the release will include. - Scrub identity the way archives actually leak: git metadata, usernames in paths, experiment-tracker entity names, cluster hostnames in laun

What's inside
Steps it walks through
  1. Review-time: the sealed archive
  2. The four-month release runway
  3. Decisions to make on purpose
  4. The infrastructure test
  5. Maintenance as reputation
  6. Reverify each cycle
  7. Output format
Commands it runs
Anonymized review package from a live repo, reproducibly
git archive HEAD -o /tmp/pkg.tar && mkdir -p /tmp/pkg && tar -xf /tmp/pkg.tar -C /tmp/pkg
cd /tmp/pkg
rm -rf .github wandb outputs
grep -rliE "$(git config user.name | awk '{print $1}')|$(hostname -s)" . | head   # identity residue
python -m pip freeze > requirements.lock                                          # pin exactly what ran
printf 'table→command map:\n  Tab.1: bash run/eval_main.sh\n' > REPRODUCE.md
zip -qr ../iccv_code.zip .
More from Awesome-Journal-Skills
All skills →
About this skill
What does the iccv-artifact-evaluation skill do?

Use when packaging code, models, or data for an ICCV paper at review or release time, covering anonymous in-archive code under the do-not-cite-your-repo rule, the June-to-October release runway after acceptance, weight and license decisions in a field whose landmark releases became infrastructure, and reuse-grade packaging.

How do I install it?

Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill iccv-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.

Keep going