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Agent skill
Wsdm Artifact Evaluation
by brycewang-stanford in brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when packaging code, data, and models as evidence for a WSDM paper - anonymous repositories cited in the PDF, the proprietary-log dilemma of web-scale research…
Install Wsdm Artifact Evaluation
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill wsdm-artifact-evaluation -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills wsdm-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/WSDM-Skills/skills/wsdm-artifact-evaluation .claude/skills/wsdm-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 "wsdm-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/WSDM-Skills/skills/wsdm-artifact-evaluation into .claude/skills/wsdm-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wsdm-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/WSDM-Skills/skills/wsdm-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 wsdm-artifact-evaluation -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills wsdm-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/WSDM-Skills/skills/wsdm-artifact-evaluation .agents/skills/wsdm-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 "wsdm-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/WSDM-Skills/skills/wsdm-artifact-evaluation into .agents/skills/wsdm-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wsdm-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 wsdm-artifact-evaluation -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills wsdm-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/WSDM-Skills/skills/wsdm-artifact-evaluation .cursor/skills/wsdm-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 "wsdm-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/WSDM-Skills/skills/wsdm-artifact-evaluation into .cursor/skills/wsdm-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wsdm-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 WSDM-Skills/skills/wsdm-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 wsdm-artifact-evaluation -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills wsdm-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/WSDM-Skills/skills/wsdm-artifact-evaluation .gemini/skills/wsdm-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 "wsdm-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/WSDM-Skills/skills/wsdm-artifact-evaluation into .gemini/skills/wsdm-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wsdm-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 wsdm-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 wsdm-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/WSDM-Skills/skills/wsdm-artifact-evaluation .github/skills/wsdm-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 "wsdm-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/WSDM-Skills/skills/wsdm-artifact-evaluation into .github/skills/wsdm-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wsdm-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 wsdm-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 wsdm-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/WSDM-Skills/skills/wsdm-artifact-evaluation .opencode/skills/wsdm-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 "wsdm-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/WSDM-Skills/skills/wsdm-artifact-evaluation into .opencode/skills/wsdm-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wsdm-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
wsdm-artifact-evaluation- GitHub stars
- 1.2k
- Token cost
- ~1.7k tokens
- SKILL.md length
- 746 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 as evidence for a WSDM paper - anonymous repositories cited in the PDF, the proprietary-log dilemma of web-scale research…
- Models as evidence for a WSDM paper - anonymous repositories cited in the PDF
- SKILL.md covers The reviewer-facing artifact, The proprietary-log dilemma, Public substitutes worth knowing and Model and prompt artifacts, plus 3 more sections
- Calls make
- The proprietary-log dilemma of web-scale research
What it does
Wsdm Artifact Evaluation is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when packaging code, data, and models as evidence for a WSDM paper - anonymous repositories cited in the PDF, the proprietary-log dilemma of web-scale research, public-benchmark substitution tiers, WSDM Cup datasets, and what credible artifact release looks like at a venue without a formal badge process.
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 Documents & Office. 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 as evidence for a WSDM paper - anonymous repositories cited in the PDF
- The proprietary-log dilemma of web-scale research
- Public-benchmark substitution tiers
- WSDM Cup datasets
Example prompts
- “/wsdm-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:
make
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
Wsdm Artifact Evaluation loads about 1.7k tokens when it runs. Until then it costs about 84 tokens; SKILL.md has 746 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). 746 words, ~1,667 tokens.
Download SKILL.mdSave it as
.claude/skills/wsdm-artifact-evaluation/SKILL.md (or your agent's skills folder).- name
- wsdm-artifact-evaluation
- description
- Use when packaging code, data, and models as evidence for a WSDM paper - anonymous repositories cited in the PDF, the proprietary-log dilemma of web-scale research, public-benchmark substitution tiers, WSDM Cup datasets, and what credible artifact release looks like at a venue without a formal badge process.
WSDM Artifact Evaluation
Package artifacts for a venue where they are persuasion, not process. The pack found no formal artifact-evaluation track or badge system for current WSDM editions (待核实 each cycle) - the CFP-level expectation is the community norm of "practical yet principled": reviewers reward submissions whose claims a skeptic could re-derive. That means the artifact's job is to be inspectable during review and usable after publication, with no committee to certify it.
The reviewer-facing artifact
Because appendices count against WSDM's page budget and there is no rebuttal in which to hand over materials later, the anonymous repository referenced in the PDF is the only expansion space you get. Build it to be skimmed in ten minutes:
anon-artifact/
├── README.md # 1 screen: claim -> script -> expected output table
├── LICENSE # anonymized placeholder license during review
├── env/ # lockfile or container spec, exact versions
├── data/
│ ├── public/ # download scripts for public benchmarks
│ └── PROPRIETARY.md # honest statement of what cannot be shared and why
├── src/ # training/ranking/mining code, no company paths
├── configs/ # one config per reported table row
└── results/
└── seeds_1-5/ # raw metric dumps behind every table in the paperAnonymity requirements are stricter than habit: fresh remote with no commit
history, no author handles in configs or notebook metadata, no internal package
registries, no dataset paths like /nfs/companyname/.... An Associate Chair
can see who you are; your reviewers must not.
The proprietary-log dilemma
WSDM's core subject matter - query logs, click data, user graphs, ad interactions - is often legally unshareable. The venue's reviewers know this; what they punish is unverifiable work, not industrial work. Choose a rung and state it explicitly in the paper:
| Rung | What is released | What the paper must then carry |
|---|---|---|
| Full release | Data + code + configs | Just the pointers |
| Sampled/anonymized release | A privacy-scrubbed sample + full code | The scrubbing procedure and how sample results track full-data results |
| Public-benchmark mirror | Code + experiments re-run on public datasets | Both result sets, with the deltas discussed, not hidden |
| Code-only | Pipeline code, no data | Enough dataset statistics that a platform-holder could replicate |
| Nothing sharable | - | A candid limitation; expect a proportional credibility discount |
The public-benchmark mirror rung is the WSDM workhorse: pair the proprietary result with the same method on public data so at least one full row of the evidence is end-to-end reproducible by anyone.
Public substitutes worth knowing
- Standard IR/rec benchmarks with public tooling (MS MARCO, TREC collections, MovieLens/Amazon review dumps, KuaiRec-style log releases) - verify current licensing before citing a release plan.
- WSDM Cup datasets: the conference's own annual challenge (the 2027 site already carries a WSDM Cup call) periodically produces real industrial datasets; using one signals community alignment. Check the specific cup's redistribution terms.
- Graph/social benchmarks (SNAP collections and successors) for the network side of the venue.
Anything here can go stale or change license - re-verify at packaging time.
Model and prompt artifacts
Foundation-model-era WSDM papers ("Search with Foundation Models" is in-scope per the 2026 CFP) add artifact surfaces older guidance misses:
- Pin model identity precisely: provider, model string, version/date, and decoding parameters; an unpinned API model makes results unreproducible by construction - say what you did about that (snapshotting outputs, local checkpoints, fixed eval windows).
- Ship prompts verbatim in the repository even when the page budget forces the paper to summarize them.
- Cache and release model outputs used in evaluation when terms permit; that lets others audit the judgment layer without paying for inference.
Show full SKILL.md (257 more words)Show less
Credibility signals at a badge-free venue
With no committee stamping artifacts, reviewers use fast proxies to decide whether the repository is real or decorative. Engineer the proxies:
| Signal | Reads as | Cost to provide |
|---|---|---|
| Config file per reported table row | The numbers came from these runs | Minutes |
| Raw seed-level metric dumps | Variance claims are checkable | Minutes |
A make reproduce-table2 entry point | Someone actually reran this | An hour |
| Download script for each public dataset | The pipeline is end-to-end | An hour |
Honest PROPRIETARY.md | The authors know what they cannot prove | Minutes |
| Commit dated one hour before deadline as the only commit | Decorative repo | - avoid |
The last row is about the squeeze: a repository assembled on deadline night
tends to mismatch the paper's numbers, and one mismatch found by a reviewer
discounts the entire artifact. Freeze the repo when experiments freeze
(wsdm-workflow sets this at one week out), not when the PDF does.
Post-acceptance conversion
At camera-ready time the anonymous mirror becomes the citable artifact: move to the real organization/account, add the actual license, tag the release that matches the camera-ready numbers, and archive a snapshot (e.g., a DOI-issuing archive) so the URL in the ACM DL version outlives your CI. Update the README's claim-to-script table against final camera-ready table numbers - drift between repo and proceedings is the most common post-publication complaint.
One more conversion detail: if the paper used the public-benchmark-mirror rung, keep both pipelines in the public repo permanently - the mirror is what future papers will actually build on, and its issues page becomes your citation engine.
Output format
[Artifact tier] full / sampled / public-mirror / code-only / none + stated in paper?
[Ten-minute test] README claim->script->output table present: yes / no
[Anonymity sweep] history / handles / paths / registries: clean or leaks listed
[FM pins] model string+date+decoding params recorded: yes / no / n-a
[Post-acceptance plan] public home, license, tagged release, archival snapshot© 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 WSDM-Skills/skills/wsdm-artifact-evaluation of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Wsdm 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 |
|---|---|---|---|---|---|---|
| Wsdm Artifact Evaluation this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.7k | Automated safety check: Pass | MIT | |
| Markdown Article FormatterJimLiu/baoyu-skills | 27k | 6 repos | ~3.5k | Automated safety check: Pass | MIT | |
| MarkitdownImCa0/just-laws | 781 | 14 repos | ~3.2k | Automated safety check: Notes | MIT | |
| Obsidian MarkdownAtmosphere/atmosphere | 3.8k | 20 repos | ~1.3k | Automated safety check: Pass | Apache-2.0 | |
| DOCXrvdbreemen/OTGW-firmware | 207 | 33 repos | ~4.3k | Automated safety check: Pass | Proprietary | |
| Word Document Reader and WriterHKUDS/DeepTutor | 41k | — | ~2.5k | Automated safety check: Pass | Apache-2.0 |
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Categories
Questions about Wsdm Artifact Evaluation
What does Wsdm Artifact Evaluation do?
A skill your agent uses when packaging code, data, and models as evidence for a WSDM paper - anonymous repositories cited in the PDF, the proprietary-log dilemma of web-scale research…. Wsdm Artifact Evaluation is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when packaging code, data, and models as evidence for a WSDM paper - anonymous repositories cited in the PDF, the proprietary-log dilemma of web-scale research, public-benchmark substitution tiers, WSDM Cup datasets, and what credible artifact release looks like at a venue without a formal badge process.
When should I use Wsdm Artifact Evaluation?
Wsdm Artifact Evaluation fits situations like: models as evidence for a WSDM paper - anonymous repositories cited in the PDF; the proprietary-log dilemma of web-scale research; public-benchmark substitution tiers; WSDM Cup datasets.
How do I install Wsdm Artifact Evaluation in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill wsdm-artifact-evaluation -a claude-code`. Or copy the skill folder (WSDM-Skills/skills/wsdm-artifact-evaluation in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/wsdm-artifact-evaluation in your project. Claude Code loads it when a task matches its description.
How do I install Wsdm Artifact Evaluation in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill wsdm-artifact-evaluation -a codex`. Or copy the skill folder (WSDM-Skills/skills/wsdm-artifact-evaluation in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/wsdm-artifact-evaluation in your project. Codex loads it when a task matches its description.
Can I use Wsdm 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 wsdm-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/wsdm-artifact-evaluation, .gemini/skills/wsdm-artifact-evaluation, .github/skills/wsdm-artifact-evaluation and .opencode/skills/wsdm-artifact-evaluation in your project.
What does Wsdm Artifact Evaluation need to run?
Going by SKILL.md and its folder, Wsdm Artifact Evaluation needs the command-line tools its instructions call (make).
Does Wsdm 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 Wsdm 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 Wsdm Artifact Evaluation use?
Wsdm 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 Wsdm Artifact Evaluation use?
About 1.7k tokens (SKILL.md is roughly 6.7k 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 Wsdm Artifact Evaluation?
Skills that share tags, products or a category with Wsdm Artifact Evaluation: Markdown Article Formatter (JimLiu/baoyu-skills, 27k stars), Markitdown (ImCa0/just-laws, 781 stars), Obsidian Markdown (Atmosphere/atmosphere, 3.8k stars) and DOCX (rvdbreemen/OTGW-firmware, 207 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Wsdm 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.