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
Iccv Artifact Evaluation
by brycewang-stanford in brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses 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…
MITAuto-check passed
Install Iccv Artifact Evaluation
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill iccv-artifact-evaluation -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills iccv-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/ICCV-Skills/skills/iccv-artifact-evaluation .claude/skills/iccv-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 "iccv-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICCV-Skills/skills/iccv-artifact-evaluation into .claude/skills/iccv-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "iccv-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/ICCV-Skills/skills/iccv-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 iccv-artifact-evaluation -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills iccv-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/ICCV-Skills/skills/iccv-artifact-evaluation .agents/skills/iccv-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 "iccv-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICCV-Skills/skills/iccv-artifact-evaluation into .agents/skills/iccv-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "iccv-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 iccv-artifact-evaluation -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills iccv-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/ICCV-Skills/skills/iccv-artifact-evaluation .cursor/skills/iccv-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 "iccv-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICCV-Skills/skills/iccv-artifact-evaluation into .cursor/skills/iccv-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "iccv-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 ICCV-Skills/skills/iccv-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 iccv-artifact-evaluation -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills iccv-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/ICCV-Skills/skills/iccv-artifact-evaluation .gemini/skills/iccv-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 "iccv-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICCV-Skills/skills/iccv-artifact-evaluation into .gemini/skills/iccv-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "iccv-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 iccv-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 iccv-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/ICCV-Skills/skills/iccv-artifact-evaluation .github/skills/iccv-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 "iccv-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICCV-Skills/skills/iccv-artifact-evaluation into .github/skills/iccv-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "iccv-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 iccv-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 iccv-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/ICCV-Skills/skills/iccv-artifact-evaluation .opencode/skills/iccv-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 "iccv-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICCV-Skills/skills/iccv-artifact-evaluation into .opencode/skills/iccv-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "iccv-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
iccv-artifact-evaluation- GitHub stars
- 1.2k
- Token cost
- ~1.5k tokens
- SKILL.md length
- 699 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, 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…
- Works in 4 steps: Fresh cloud machine, no lab credentials:… → Do the printed numbers reproduce? State… → Is there exactly one entry point per use… → …
- Data for an ICCV paper at review
- SKILL.md covers Review-time: the sealed archive, The four-month release runway, Decisions to make on purpose and The infrastructure test, plus 3 more sections
- Calls git and python
What it does
Iccv Artifact Evaluation is an agent skill from brycewang-stanford/Awesome-Journal-Skills. 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.
Its SKILL.md is about 1.5k 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
- Data for an ICCV paper at review
- 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
Example prompts
- “/iccv-artifact-evaluation”
Requirements
- Python 3
Workflow steps
4 steps, taken from the first numbered list in SKILL.md.
- Fresh cloud machine, no lab credentials: does git clone → env setup →
- Do the printed numbers reproduce? State the tolerance you observed
- Is there exactly one entry point per use case (inference / train / eval),
- Would a person with only the checkpoint and the eval script get your Table 1
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:
gitpython
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
Iccv Artifact Evaluation loads about 1.5k tokens when it runs. Until then it costs about 88 tokens; SKILL.md has 699 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). 699 words, ~1,531 tokens.
Download SKILL.mdSave it as
.claude/skills/iccv-artifact-evaluation/SKILL.md (or your agent's skills folder).- name
- iccv-artifact-evaluation
- description
- 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.
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 launcher scripts.
- Include five-sample inference with expected outputs, so a reviewer can smoke- test without your datasets.
# 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 .REPRODUCE.md — one command per paper table/figure — is the highest-value file
per byte in the archive; it converts "trust the tables" into "verify one row."
The four-month release runway
ICCV's calendar gives artifact work a gift no November-deadline venue offers: decisions in late June, conference in mid-October. Plan the runway backward from conference week, when attention peaks:
| Weeks after decision | Release milestone |
|---|---|
| 0–2 | License chosen; hosting decided; repo made public with paper, citation, and "weights coming <date>" |
| 2–6 | Weights uploaded with checksums; inference path verified from a clean machine outside your network |
| 6–10 | Training/eval reproduction path documented; issues from early adopters triaged |
| by conference | Everything in the paper's availability statement is live; project page links CVF open access |
A repo that goes live during conference week arrives exactly when thousands of people are reading the paper; a repo that goes live three months later arrives after the field has moved to reproducing someone else's implementation of your method — which then becomes the cited one.
Show full SKILL.md (338 more words)Show less
Decisions to make on purpose
- License, split by asset: code (permissive vs copyleft), weights (research- only vs unrestricted — decide who may fine-tune and redistribute), data (can you even relicense what you scraped?). "No license" reads as "do not use" to every industrial lab.
- Checkpoint provenance: name checkpoints for the table rows they reproduce and record the code commit that evaluates each to the printed number.
- Data documentation: sources, filtering, consent posture for human imagery. Vision datasets get audited years later; write the provenance section while the facts are fresh.
- Generative-model posture: for synthesis work, decide what ships (full weights, adapters, inference-only) and write the misuse considerations yourself before a journalist does.
- Benchmark freeze: if you release evaluation code others will report numbers from, version it and changelog every metric fix — silent corrections fork leaderboards.
The infrastructure test
Before calling the release done, run the test the field will run:
- Fresh cloud machine, no lab credentials: does
git clone→ env setup → five-image inference work inside an hour? - Do the printed numbers reproduce? State the tolerance you observed
(
iccv-reproducibility); a ±0.2 note preempts a hundred issues. - Is there exactly one entry point per use case (inference / train / eval), each documented in under a screen of README?
- Would a person with only the checkpoint and the eval script get your Table 1 row? If not, what unstated asset is missing — and can it be shipped?
Maintenance as reputation
The two-year gap to the next ICCV means your artifact is the state of the record
for a long time. Budget light maintenance: pin dependencies against rot, tag the
camera-ready state (v1.0-iccv<year>), answer the first month's reproduction
issues (usually environment drift), and keep a short log of community-confirmed
reproductions. The venue's landmark releases earned their citation counts in
this phase, not at upload.
Reverify each cycle
- The availability-statement and repo-citation wording in the current Author Guidelines.
- Supplement size limits that constrain review-time code (待核实 for 2025).
- Any artifact checklist or badge program the 2027 chairs may introduce.
Output format
[Phase] review-archive / release-runway / maintenance
[Archive] identity-clean · pinned · smoke-testable · REPRODUCE.md maps n/m tables
[Runway] weeks to conference: <n>; milestones on track: yes/no
[Licenses] code/weights/data each decided: yes/no
[Infrastructure test] clean-machine hour test: pass/fail at <step>
[Debt] <unshipped promises from the availability statement>© 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 ICCV-Skills/skills/iccv-artifact-evaluation of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Iccv 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 |
|---|---|---|---|---|---|---|
| Iccv Artifact Evaluation this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.5k | 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 Iccv Artifact Evaluation
What does Iccv Artifact Evaluation do?
A skill your agent uses 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…. Iccv Artifact Evaluation is an agent skill from brycewang-stanford/Awesome-Journal-Skills. 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.
When should I use Iccv Artifact Evaluation?
Iccv Artifact Evaluation fits situations like: data for an ICCV paper at review; 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.
How do I install Iccv Artifact Evaluation in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill iccv-artifact-evaluation -a claude-code`. Or copy the skill folder (ICCV-Skills/skills/iccv-artifact-evaluation in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/iccv-artifact-evaluation in your project. Claude Code loads it when a task matches its description.
How do I install Iccv Artifact Evaluation in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill iccv-artifact-evaluation -a codex`. Or copy the skill folder (ICCV-Skills/skills/iccv-artifact-evaluation in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/iccv-artifact-evaluation in your project. Codex loads it when a task matches its description.
Can I use Iccv 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 iccv-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/iccv-artifact-evaluation, .gemini/skills/iccv-artifact-evaluation, .github/skills/iccv-artifact-evaluation and .opencode/skills/iccv-artifact-evaluation in your project.
What does Iccv Artifact Evaluation need to run?
Going by SKILL.md and its folder, Iccv Artifact Evaluation needs the command-line tools its instructions call (git and python). Our summary lists: Python 3.
Does Iccv 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 Iccv 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 Iccv Artifact Evaluation use?
Iccv 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 Iccv Artifact Evaluation use?
About 1.5k tokens (SKILL.md is roughly 6.1k 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 Iccv Artifact Evaluation?
Skills that share tags, products or a category with Iccv 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 Iccv 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.