Agent skill

Kdd Artifact Evaluation

by brycewang-stanford in brycewang-stanford/Awesome-Journal-Skills

MITAuto-check passed

Install Kdd Artifact Evaluation

skills CLI

$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill kdd-artifact-evaluation -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install brycewang-stanford/Awesome-Journal-Skills kdd-artifact-evaluation --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/KDD-Skills/skills/kdd-artifact-evaluation .claude/skills/kdd-artifact-evaluation && rm -rf skills-src

Use ~/.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/

Paste into Claude Code

Install the "kdd-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/KDD-Skills/skills/kdd-artifact-evaluation into .claude/skills/kdd-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "kdd-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

$skill-installer install https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/KDD-Skills/skills/kdd-artifact-evaluation

Type 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.

skills CLI

$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill kdd-artifact-evaluation -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install brycewang-stanford/Awesome-Journal-Skills kdd-artifact-evaluation --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/KDD-Skills/skills/kdd-artifact-evaluation .agents/skills/kdd-artifact-evaluation && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "kdd-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/KDD-Skills/skills/kdd-artifact-evaluation into .agents/skills/kdd-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "kdd-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.

skills CLI

$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill kdd-artifact-evaluation -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install brycewang-stanford/Awesome-Journal-Skills kdd-artifact-evaluation --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/KDD-Skills/skills/kdd-artifact-evaluation .cursor/skills/kdd-artifact-evaluation && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "kdd-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/KDD-Skills/skills/kdd-artifact-evaluation into .cursor/skills/kdd-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "kdd-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

$ gemini skills install https://github.com/brycewang-stanford/Awesome-Journal-Skills.git --path KDD-Skills/skills/kdd-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.

skills CLI

$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill kdd-artifact-evaluation -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install brycewang-stanford/Awesome-Journal-Skills kdd-artifact-evaluation --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/KDD-Skills/skills/kdd-artifact-evaluation .gemini/skills/kdd-artifact-evaluation && rm -rf skills-src

Use ~/.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/

Paste into Gemini CLI

Install the "kdd-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/KDD-Skills/skills/kdd-artifact-evaluation into .gemini/skills/kdd-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "kdd-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.

GitHub CLI

$ gh skill install brycewang-stanford/Awesome-Journal-Skills kdd-artifact-evaluation

Installs 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).

skills CLI

$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill kdd-artifact-evaluation -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/KDD-Skills/skills/kdd-artifact-evaluation .github/skills/kdd-artifact-evaluation && rm -rf skills-src

Use ~/.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/

Paste into GitHub Copilot

Install the "kdd-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/KDD-Skills/skills/kdd-artifact-evaluation into .github/skills/kdd-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "kdd-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.

skills CLI

$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill kdd-artifact-evaluation -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install brycewang-stanford/Awesome-Journal-Skills kdd-artifact-evaluation --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/KDD-Skills/skills/kdd-artifact-evaluation .opencode/skills/kdd-artifact-evaluation && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "kdd-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/KDD-Skills/skills/kdd-artifact-evaluation into .opencode/skills/kdd-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "kdd-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: kdd-artifact-evaluation
GitHub stars: 1.2k
Token cost: ~1.7k tokens
SKILL.md length: 747 words
Files: 1
Skills in repo: 2,387
Repo updated: Sep 27, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

Deployment evidence for a KDD paper
SKILL.md covers Artifact strategy by track, Building the anonymized…, Scale claims need scale… and ADS evidence packaging without…, plus 4 more sections
Calls docker and bash
Where the repository cited in the submission is the only artifact reviewers can reach because rebuttals ban links

What it does

Kdd Artifact Evaluation is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when packaging code, datasets, configs, and deployment evidence for a KDD paper, where the repository cited in the submission is the only artifact reviewers can reach because rebuttals ban links. Covers anonymized repo construction, scale-claim harnesses, ADS evidence without production data, and post-acceptance release.

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.

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

Deployment evidence for a KDD paper
Where the repository cited in the submission is the only artifact reviewers can reach because rebuttals ban links

Example prompts

“/kdd-artifact-evaluation”

Requirements

Python 3
Docker

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:
- docker
- bash
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use docker, 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

Kdd Artifact Evaluation loads about 1.7k tokens when it runs. Until then it costs about 88 tokens; SKILL.md has 747 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~88
When it runs · the whole SKILL.md, loaded when a task matches: ~1.7k

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). 747 words, ~1,736 tokens.

Download SKILL.mdSave it as .claude/skills/kdd-artifact-evaluation/SKILL.md (or your agent's skills folder).

name: kdd-artifact-evaluation
description: Use when packaging code, datasets, configs, and deployment evidence for a KDD paper, where the repository cited in the submission is the only artifact reviewers can reach because rebuttals ban links. Covers anonymized repo construction, scale-claim harnesses, ADS evidence without production data, and post-acceptance release.

KDD Artifact Evaluation

Use this while the submission is being assembled — not after. KDD's review mechanics create one hard constraint that reorders all artifact work: the rebuttal phase does not allow hyperlinks, so the anonymized repository referenced inside the submitted PDF is the complete and final artifact channel for the whole review. There is no "we'll share code if reviewers ask"; asking happens in a phase where you cannot answer with a link.

Artifact strategy by track

Track	Primary artifact	What reviewers actually probe	Non-shippable core, and its substitute
Research	Anonymized code + configs + data loaders	Can the headline table be regenerated? Does the scale claim have a runnable path?	Massive datasets → downsampled slice + full-scale download script
ADS	Measurement definitions + pipeline skeleton	Are post-launch metrics precisely defined? Is the eval window stated?	Production data/code → metric spec, schema, synthetic replay generator
Datasets & Benchmarks	The dataset itself + loaders + baseline harness	License, provenance, documentation, versioning	Nothing — the artifact is the paper

Building the anonymized repository

Create a fresh repository, never a scrubbed clone: git history, CI configs, issue templates, and .git metadata leak identity that no README edit removes.
Sweep for: usernames in paths, institutional cluster names, cloud bucket names, internal package registries, license headers, notebook execution metadata, and dataset names that only one company uses.
Cite the repository in the paper body (and optionally the abstract); after the deadline this reference is unchangeable, so double-check the URL resolves logged out.
Structure for a 10-minute reviewer, because that is the realistic inspection budget:

bash

anonymous-artifact/
├── README.md            # 1 screen: claim -> command -> expected output table
├── env/                 # lockfile or container spec, exact versions
├── configs/             # one config per reported table/figure row
├── data/
│   ├── get_data.sh      # public downloads, checksums
│   └── sample/          # small slice so the pipeline runs in minutes
├── run.sh               # regenerates the smallest headline result end-to-end
└── results/expected/    # committed reference outputs for diffing
# smoke-test on a clean machine:
docker run --rm -v $PWD:/w -w /w python:3.11 bash -c "pip install -r env/requirements.txt && bash run.sh --sample"

Scale claims need scale artifacts

KDD reviewers read "scales to billions of edges" as a checkable claim, not marketing:

Ship the throughput/memory harness, not only accuracy scripts; a scalability figure without its benchmark script is the least-trusted plot in the paper.
Record hardware, dataset cardinalities (rows/edges/events), and wall-clock per run in a machine-readable manifest, so the repo answers "on what?" precisely.
Where the full-scale run needs a cluster no reviewer has, provide the mid-scale run that completes on one machine plus the exact extrapolation logic the paper uses.

ADS evidence packaging without leaking production

The ADS track requires quantified post-launch performance, but production data almost never ships. Reviewers accept that trade when the package contains:

Exact metric definitions (numerator, denominator, unit, aggregation window) for every post-launch number in the paper.
The measurement design: A/B split or pre/post window, traffic share, duration, and what guardrail metrics were tracked.
A synthetic or replayed data generator that exercises the same pipeline shape, so the method is runnable even though the evidence is observational.
An honesty note distinguishing measured production numbers from simulated ones — mixing them silently is a rejection pattern.

Show full SKILL.md (311 more words)Show less

After acceptance

Replace the anonymous mirror with the public, licensed, citable repository; the camera-ready must carry the durable link (see kdd-camera-ready).
Prefer an archival deposit (DOI-stamped release) over a bare git URL for the version that the proceedings reference.
Check the current cycle for artifact-badging or code-availability initiatives before assuming none exists (待核实 — such programs change year to year).

Vignette: packaging a billion-edge graph paper

A Research Track paper claims its sampler trains GNNs on a 3B-edge graph on one machine. What the artifact must contain for that claim to survive contact with a skeptical reviewer:

get_data.sh that downloads the public 3B-edge graph (or constructs it from public parts) with checksums — a scale claim on an unfetchable graph is attested, not rerunnable, and should be labeled accordingly (kdd-reproducibility).
A --scale small|medium|full switch: small finishes on a laptop in minutes and validates the pipeline; medium reproduces one main-table row on a single GPU overnight; full documents the exact hardware used for the headline.
The peak-memory tracker wired into every run, because the paper's "one machine" claim is really a memory claim.
results/expected/ with per-scale reference outputs, so a reviewer's partial rerun has something to diff against.

What it must not contain: the 40GB of intermediate artifacts (regenerable), the authors' cluster submission scripts (identity leak), or a README promising "full instructions after acceptance" — that sentence tells reviewers the artifact is theater.

Common artifact failures at this venue

Failure	Why it is fatal at KDD specifically
Repo created but never cited in the PDF	The link ban makes it undiscoverable during rebuttal
Git history preserved from the lab repo	Identity leak → desk-level anonymity problem
Accuracy scripts only, no efficiency harness	The paper's scale/efficiency axis becomes unverifiable
Sample data missing, full data gated	Reviewer's 10-minute budget ends at the download wall
ADS package with raw production extracts	Confidentiality violation risk transferred to reviewers

Output format

text

[Artifact channel] repo cited in PDF: yes/no (if no: unrecoverable after deadline)
[Track register] research-repro / ads-deployment-evidence / dataset-release
[Regeneration level] one-command sample / scripted / descriptive only
[Scale evidence] throughput+memory harness: present / missing
[Anonymity sweep] <paths/history/metadata findings>
[Post-acceptance plan] <public repo, license, archival DOI>

Files

Just SKILL.md in KDD-Skills/skills/kdd-artifact-evaluation of brycewang-stanford/Awesome-Journal-Skills.

Open the folder on GitHubat commit 932eb23

Compare with similar skills

Kdd 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.

Kdd Artifact Evaluation compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Kdd Artifact Evaluation this skillbrycewang-stanford/Awesome-Journal-Skills	1.2k	—	~1.7k	Automated safety check: Pass	MIT	13 days ago
DatasetsArize-ai/phoenix	12k	—	~1.6k	Automated safety check: Pass	Custom licence	today
Arize Evaluatorgithub/awesome-copilot	40k	1 repos	~8.1k	Automated safety check: Notes	MIT	yesterday
Ccs Artifact Evaluationbrycewang-stanford/Awesome-Journal-Skills	1.2k	—	~969	Automated safety check: Pass	MIT	13 days ago
Micro Artifact Evaluationbrycewang-stanford/Awesome-Journal-Skills	1.2k	—	~1.7k	Automated safety check: Pass	MIT	13 days ago
Mobisys Artifact Evaluationbrycewang-stanford/Awesome-Journal-Skills	1.2k	—	~1k	Automated safety check: Pass	MIT	13 days ago

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Questions about Kdd Artifact Evaluation

What does Kdd Artifact Evaluation do?

A skill your agent uses when packaging code, datasets, configs, and deployment evidence for a KDD paper, where the repository cited in the submission is the only artifact reviewers can reach because…. Kdd Artifact Evaluation is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when packaging code, datasets, configs, and deployment evidence for a KDD paper, where the repository cited in the submission is the only artifact reviewers can reach because rebuttals ban links.

When should I use Kdd Artifact Evaluation?

Kdd Artifact Evaluation fits situations like: deployment evidence for a KDD paper; where the repository cited in the submission is the only artifact reviewers can reach because rebuttals ban links.

How do I install Kdd Artifact Evaluation in Claude Code?

Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill kdd-artifact-evaluation -a claude-code`. Or copy the skill folder (KDD-Skills/skills/kdd-artifact-evaluation in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/kdd-artifact-evaluation in your project. Claude Code loads it when a task matches its description.

How do I install Kdd Artifact Evaluation in Codex?

Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill kdd-artifact-evaluation -a codex`. Or copy the skill folder (KDD-Skills/skills/kdd-artifact-evaluation in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/kdd-artifact-evaluation in your project. Codex loads it when a task matches its description.

Can I use Kdd 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 kdd-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/kdd-artifact-evaluation, .gemini/skills/kdd-artifact-evaluation, .github/skills/kdd-artifact-evaluation and .opencode/skills/kdd-artifact-evaluation in your project.

What does Kdd Artifact Evaluation need to run?

Going by SKILL.md and its folder, Kdd Artifact Evaluation needs the command-line tools its instructions call (docker and bash). Our summary lists: Python 3; Docker.

Does Kdd Artifact Evaluation access the network?

SKILL.md contains no URLs. Its commands use docker, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Kdd 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 Kdd Artifact Evaluation use?

Kdd 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 Kdd Artifact Evaluation use?

About 1.7k tokens (SKILL.md is roughly 6.9k 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 Kdd Artifact Evaluation?

Skills that share tags, products or a category with Kdd Artifact Evaluation: Datasets (Arize-ai/phoenix, 12k stars), Arize Evaluator (github/awesome-copilot, 40k stars), Ccs Artifact Evaluation (brycewang-stanford/Awesome-Journal-Skills, 1.2k stars) and Micro 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 Kdd 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.

Install Kdd Artifact Evaluation

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

KDD Artifact Evaluation

Artifact strategy by track

Building the anonymized repository

Scale claims need scale artifacts

ADS evidence packaging without leaking production

After acceptance

Vignette: packaging a billion-edge graph paper

Common artifact failures at this venue

Output format

Files

Compare with similar skills

Similar skills

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